diff --git a/Data/Surface_growth_rate_data/MSA_Supplementary_DataS3.xlsx b/Data/Surface_growth_rate_data/MSA_Supplementary_DataS3.xlsx new file mode 100644 index 0000000..e113166 Binary files /dev/null and b/Data/Surface_growth_rate_data/MSA_Supplementary_DataS3.xlsx differ diff --git a/Data/Surface_growth_rate_data/MSA_Supplementary_DataS4.xlsx b/Data/Surface_growth_rate_data/MSA_Supplementary_DataS4.xlsx new file mode 100644 index 0000000..1382637 Binary files /dev/null and b/Data/Surface_growth_rate_data/MSA_Supplementary_DataS4.xlsx differ diff --git a/Data/Surface_growth_rate_data/surface_mumax.xlsx b/Data/Surface_growth_rate_data/surface_mumax.xlsx new file mode 100644 index 0000000..2d4ba4d Binary files /dev/null and b/Data/Surface_growth_rate_data/surface_mumax.xlsx differ diff --git a/Data/eGFP_expression_Bienick2014.xls b/Data/eGFP_expression_Bienick2014.xls deleted file mode 100644 index 3ca6125..0000000 Binary files a/Data/eGFP_expression_Bienick2014.xls and /dev/null differ diff --git a/Data/eGFP_protein_sequence.txt b/Data/eGFP_protein_sequence.txt deleted file mode 100644 index 6e40682..0000000 --- a/Data/eGFP_protein_sequence.txt +++ /dev/null @@ -1 +0,0 @@ -MRKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFGYGVQCFARYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYKLEHHHHHH diff --git a/Data/enzyme_sets.json b/Data/enzyme_sets.json deleted file mode 100644 index fa192e8..0000000 --- a/Data/enzyme_sets.json +++ /dev/null @@ -1,146 +0,0 @@ -{ - "optimized_kcats_core": { - "P25516": { - "CE_ACONTa_P25516": { - "f": 5.2, - "b": 5.2 - }, - "CE_ACONTb_P25516": { - "f": 5.2, - "b": 5.2 - } - }, - "P0AC98": { - "CE_ACt2rpp_P0AC98": { - "f": 15, - "b": 15 - } - }, - "P0A9P0_P0AFG3_P0AFG6": { - "CE_AKGDH_P0A9P0_P0AFG3_P0AFG6": { - "f": 35, - "b": 35 - } - }, - "P0AEX3": { - "CE_AKGt2rpp_P0AEX3": { - "f": 100, - "b": 100 - } - }, - "P0A6E6_P0AB98_P0ABA0_P0ABA4_P0ABA6_P0ABB0_P0ABB4_P0ABC0_P68699": { - "CE_ATPS4rpp_P0A6E6_P0AB98_P0ABA0_P0ABA4_P0ABA6_P0ABB0_P0ABB4_P0ABC0_P68699": { - "f": 800, - "b": 800 - } - }, - "P0A6E6_P0AB98_P0ABA0_P0ABA4_P0ABA6_P0ABB0_P0ABB4_P68699": { - "CE_ATPS4rpp_P0A6E6_P0AB98_P0ABA0_P0ABA4_P0ABA6_P0ABB0_P0ABB4_P68699": { - "f": 800, - "b": 800 - } - }, - "P26458_P26459": { - "CE_CYTBD2pp_P26458_P26459": { - "f": 300, - "b": 300 - }, - "CE_CYTBDpp_P26458_P26459": { - "f": 300, - "b": 300 - } - }, - "P0ABJ9_P0ABK2_P56100": { - "CE_CYTBDpp_P0ABJ9_P0ABK2_P56100": { - "f": 300, - "b": 300 - } - }, - "P0AC53": { - "CE_G6PDH2r_P0AC53": { - "f": 1446, - "b": 1446 - } - }, - "P08839_P0AA04_P69797_P69801_P69805": { - "CE_GLCptspp_P08839_P0AA04_P69797_P69801_P69805": { - "f": 2765, - "b": 0 - } - }, - "P08839_P0AA04_P69783_P69786": { - "CE_GLCptspp_P08839_P0AA04_P69783_P69786": { - "f": 2765, - "b": 0 - }, - "GLCptspp": { - "f": 2765, - "b": 0 - } - }, - "P00350": { - "CE_GND_P00350": { - "f": 1446, - "b": 1446 - } - }, - "P0AFC3_P0AFC7_P0AFD1_P0AFD4_P0AFD6_P0AFE0_P0AFE4_P0AFE8_P0AFF0_P31979_P33599_P33602_P33607": { - "CE_NADH16pp_P0AFC3_P0AFC7_P0AFD1_P0AFD4_P0AFD6_P0AFE0_P0AFE4_P0AFE8_P0AFF0_P31979_P3359\n9_P33602_P33607": { - "f": 166.67, - "b": 0 - } - }, - "P06959_P0A9P0_P0AFG8": { - "CE_PDH_P06959_P0A9P0_P0AFG8": { - "f": 37.9, - "b": 37.9 - } - }, - "P0A799": { - "CE_PGK_P0A799": { - "f": 10, - "b": 10 - } - }, - "P0A830": { - "CE_SUCCt2_2pp_P0A830": { - "f": 30, - "b": 30 - }, - "SUCCt2_2pp": { - "f": 30, - "b": 30 - } - }, - "P0AC98": { - "CE_SUCCt2_2pp_P0AC98": { - "f": 30, - "b": 30 - } - }, - "P07014_P0AC41_P0AC44_P69054": { - "CE_SUCDi_P07014_P0AC41_P0AC44_P69054": { - "f": 182, - "b": 0 - } - }, - "P07001_P0AB67": { - "CE_THD2pp_P07001_P0AB67": { - "f": 1000, - "b": 0 - } - }, - "P27302": { - "CE_TKT1_P27302": { - "f": 410000, - "b": 410000 - } - }, - "P33570": { - "CE_TKT1_P33570": { - "f": 410000, - "b": 410000 - } - } - } -} \ No newline at end of file diff --git a/Data/mcPAM_iML1515_EnzymaticData.xlsx b/Data/mcPAM_iML1515_EnzymaticData.xlsx index 2dd613f..c7ff489 100644 Binary files a/Data/mcPAM_iML1515_EnzymaticData.xlsx and b/Data/mcPAM_iML1515_EnzymaticData.xlsx differ diff --git a/Data/proteinAllocationModel_iML1515_EnzymaticData_py.xls b/Data/proteinAllocationModel_iML1515_EnzymaticData_py.xls deleted file mode 100644 index fecf60f..0000000 Binary files a/Data/proteinAllocationModel_iML1515_EnzymaticData_py.xls and /dev/null differ diff --git a/Data/proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx b/Data/proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx deleted file mode 100644 index 4aa9836..0000000 Binary files a/Data/proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx and /dev/null differ diff --git a/Data/proteinAllocationModel_mc-core_EnzymaticData_241209_multi.xlsx b/Data/proteinAllocationModel_mc-core_EnzymaticData_241209_multi.xlsx new file mode 100644 index 0000000..6c71ea6 Binary files /dev/null and b/Data/proteinAllocationModel_mc-core_EnzymaticData_241209_multi.xlsx differ diff --git a/Data/proteinAllocationModel_mciML1515_EnzymaticData_241209_multi.xlsx b/Data/proteinAllocationModel_mciML1515_EnzymaticData_241209_multi.xlsx index 42b2f46..5d476cd 100644 Binary files a/Data/proteinAllocationModel_mciML1515_EnzymaticData_241209_multi.xlsx and b/Data/proteinAllocationModel_mciML1515_EnzymaticData_241209_multi.xlsx differ diff --git a/Data/proteome_data_extract_schmidt2016.xlsx b/Data/proteome_data_extract_schmidt2016.xlsx deleted file mode 100644 index a028b70..0000000 Binary files a/Data/proteome_data_extract_schmidt2016.xlsx and /dev/null differ diff --git a/Examples/mcpam_generator.py b/Examples/mcpam_generator.py index 4a69be0..7bab4d7 100644 --- a/Examples/mcpam_generator.py +++ b/Examples/mcpam_generator.py @@ -1,24 +1,44 @@ from Scripts.mcpam_simulations_analysis import (run_pam_mcpam_core_with_optimized_kcats, - run_simulations_pam_mcpam, + run_simulation_pam_mcpam, + run_simulations_pam_mcpam_w_different_areas, set_up_ecolicore_pam, set_up_ecolicore_mcpam, + set_up_ecolicore_mcpam_new_surface_parameter, compare_mu_for_different_sensitivities_ecolicore_pam, perform_single_gene_ko_for_all_genes, perform_and_plot_single_KO) from Scripts.mcpam_generation_uniprot_id import set_up_ecoli_mcpam, set_up_ecoli_pam +from Scripts.mcpam_toy_generation import build_toy_model +from src.PAModelpy.utils.pam_generation import set_up_pam, set_up_core_pam import matplotlib.pyplot as plt; plt.rcdefaults() if __name__ == "__main__": + # Build full scale pam new parsing + pam_info_path = 'Data/proteinAllocationModel_mciML1515_EnzymaticData_241209_multi.xlsx' + pam = set_up_pam(pam_info_file=pam_info_path, sensitivity=False, membrane_sector=False) + mcpam = set_up_pam(pam_info_file=pam_info_path, sensitivity=False, membrane_sector=True) - pam = set_up_ecoli_pam(sensitivity=False) - mcpam = set_up_ecoli_mcpam(sensitivity=False) - - # pam, mcpam = run_pam_mcpam_core_with_optimized_kcats(sensitivity=True, type='full scale', enzyme_sets_name="enzyme_sets.json") models = [pam, mcpam] - run_simulations_pam_mcpam(models, type="full scale") + run_simulation_pam_mcpam(models, type="full scale") + + # Build core pam + # pam = set_up_ecolicore_pam(sensitivity=False) + # mcpam = set_up_ecolicore_mcpam_new_surface_parameter(sensitivity=False) + # models = [pam, mcpam] + # run_simulations_pam_mcpam_w_different_areas(models, type='core') + + # Build core pam new parse + # pam = set_up_core_pam(sensitivity=False, membrane_sector=False, pam_info_file='Data/proteinAllocationModel_mc-core_EnzymaticData_241209_multi.xlsx') + # mcpam = set_up_core_pam(sensitivity=False, membrane_sector=True, pam_info_file='Data/proteinAllocationModel_mc-core_EnzymaticData_241209_multi.xlsx') + # models = [pam, mcpam] + # run_simulation_pam_mcpam(models, type="core") + + # Build toy pam + # toy_pam = build_toy_model(membrane_sector=True) + # toy_pam.objective = "R11" + # toy_pam.optimize() + # print(toy_pam.objective.value) - for protein, kcats in mcpam.sectors.get_by_id("MembraneSector").membrane_proteins.items(): - print(protein, kcats) diff --git a/Examples/toy_model_generator.py b/Examples/toy_model_generator.py deleted file mode 100644 index da4ad48..0000000 --- a/Examples/toy_model_generator.py +++ /dev/null @@ -1,6 +0,0 @@ -from Scripts.toy_model_TS_generation import build_toy_model - -toy_mcpam = build_toy_model(sensitivity=False) -toy_mcpam.objective = 'R11' -result = toy_mcpam.optimize() -print(toy_mcpam.objective.value) diff --git a/Figures/.ipynb_checkpoints/Figure2_sensitivities_gecko_pam-checkpoint.ipynb b/Figures/.ipynb_checkpoints/Figure2_sensitivities_gecko_pam-checkpoint.ipynb deleted file mode 100644 index 7366776..0000000 --- a/Figures/.ipynb_checkpoints/Figure2_sensitivities_gecko_pam-checkpoint.ipynb +++ /dev/null @@ -1,1055 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6d2a8cae-9e89-4faa-9b23-2c97b6186037", - "metadata": {}, - "source": [ - "# Code to generate figure 2 in the publication\n", - "Analysis of sensitive enzymes and reactions in the model simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3e02abee-b41a-4257-ae81-9f22b71d17b3", - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "import matplotlib.colors as mcolors\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "\n", - "import numpy as np\n", - "from PIL import Image\n", - "\n", - "from PAModelpy.configuration import Config\n", - "from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector, CustomSector\n", - "from PAModelpy import CatalyticEvent\n", - "\n", - "\n", - "if os.path.split(os.getcwd())[1] == 'Figures':\n", - " os.chdir(os.path.split(os.getcwd())[0])\n", - "# sys.path.append('../Scripts/')\n", - "from Scripts.pam_generation import set_up_ecoli_pam\n", - "\n", - "BIOMASS_RXNID = Config.BIOMASS_REACTION\n", - "DATA_DIR = 'Data'#os.path.join(os.path.split(os.getcwd())[0], 'Data')\n", - "PAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'proteinAllocationModel_iML1515_EnzymaticData_py.xls')\n", - "glc_uptake_rates = list(np.linspace(0.5, 10, 20))" - ] - }, - { - "cell_type": "markdown", - "id": "47bbc17e-1d0f-4375-922a-4f630333f787", - "metadata": {}, - "source": [ - "## sensitivities of GECKO and PAM" - ] - }, - { - "cell_type": "markdown", - "id": "fd5ff0a1-bc1e-42fe-bc4c-7d93571a5c27", - "metadata": {}, - "source": [ - "### 1 Usefull functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "69b86edc-c37a-420f-9c88-2fa9fd186b59", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_sensitivities(pamodel):\n", - " glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - " Ccsc = []\n", - " Cesc = []\n", - " y_axis = []\n", - " fluxes = []\n", - " \n", - " # disable pyruvate formate lyase (inhibited by oxygen)\n", - " pamodel.change_reaction_bounds(rxn_id = 'PFL', upper_bound = 0)\n", - " \n", - " for glc in glc_uptake_rates:\n", - " print('glucose uptake rate ', glc, ' mmol/gcdw/h')\n", - " with pamodel:\n", - " # change glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -glc, upper_bound = -glc)\n", - " # solve the model\n", - " sol_pam = pamodel.optimize()\n", - " fluxes.append(sol_pam.fluxes)\n", - " if pamodel.solver.status == 'optimal': y_axis += [glc]\n", - " # save data\n", - " Ccsc_new = list()\n", - " \n", - " if pamodel.solver.status == 'optimal':\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - "\n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of enzyme sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6),'\\n')\n", - "\n", - " return {'Ccsc':Ccsc, 'Cesc':Cesc, 'y_axis':y_axis, 'fluxes':fluxes, 'capacity coefficients':capacity_coeff, 'enzyme coefficients':enzyme_coeff}\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e47c6d59-277f-41bc-89a6-647ecc476120", - "metadata": {}, - "outputs": [], - "source": [ - "def parse_x_axis_heatmap(capacity_coeff, enzyme_coeff):\n", - " x_axis_csc = []\n", - " \n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if csc == 'flux_ub' or csc == 'flux_lb':\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[capacity_coeff['constraint'] == csc].rxn_id.to_list()]\n", - " else:\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == csc].enzyme_id.to_list()]\n", - " \n", - " x_axis_esc = enzyme_coeff.enzyme_id.to_list()\n", - " return x_axis_csc, x_axis_esc" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "cf73a888-2270-4ea9-9cc8-4c9e0f5eceaa", - "metadata": {}, - "outputs": [], - "source": [ - "def make_heatmap_subfigure(results, csc_matrix, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc,\n", - " ylabels = True, xlabels=False, cbar =True, title=None, fontsize = 16, \n", - " vmin = -1.5, vmax = 1.5, annotate = None, phenotype_data = None, cmap=None\n", - " #cmap = plt.cm.get_cmap('viridis')\n", - "):\n", - " # fig = plt.figure()\n", - " if cmap is None:\n", - " # Create separate colormaps for positive and negative values and a color for zero\n", - " colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128))\n", - "# colors_pos = plt.cm.PuOr(np.linspace(0.5, 1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128))\n", - "# colors_pos = plt.cm.PuOr(np.linspace(0.5, 0, 64)) # Use part of the PuOr colormap\n", - "# colors_pos = np.vstack((colors_pos, plt.cm.PuOr(np.linspace(0.5, 1, 64))))\n", - "# colors_pos = lighten_colormap(plt.cm.plasma)(np.linspace(0, 0.5, 64)) # Use part of the PuOr colormap\n", - "# colors_pos = np.vstack((colors_pos, plt.cm.plasma(np.linspace(0.5, 1, 64))))\n", - " colors_pos = plt.cm.OrRd(np.linspace(0.1,1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128))\n", - "\n", - " colors_zero = np.array([[1,1,1, 1]]) # gray for zero\n", - "\n", - " # Combine them into a single colormap\n", - " colors = np.vstack((colors_neg, colors_zero, colors_pos))\n", - " combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap')\n", - "\n", - " # Create a norm that handles the zero color properly\n", - " bounds = np.linspace(vmin, vmax, len(colors))\n", - " norm = mcolors.BoundaryNorm(bounds, combined_cmap.N)\n", - " \n", - "\n", - " if cbar:\n", - " gs = gridspec.GridSpecFromSubplotSpec(3, 2, width_ratios=[len(yaxis), 1], \n", - " height_ratios=[1,len(x_csc), len(x_esc)], hspace =0,\n", - " subplot_spec=grdspc)\n", - " else:\n", - " gs = gridspec.GridSpecFromSubplotSpec(3, 1, width_ratios=[len(yaxis)], \n", - " height_ratios=[1,len(x_csc), len(x_esc)], hspace =0,\n", - " subplot_spec=grdspc)\n", - "\n", - " \n", - " esc_ax = fig.add_subplot(gs[2,0]) #ESC heatmap\n", - " acetate_ax = fig.add_subplot(gs[0,0]) #acetate production\n", - " csc_ax = fig.add_subplot(gs[1,0],sharex = esc_ax) #CSC heatmap\n", - " if cbar:\n", - " cbar_ax = fig.add_subplot(gs[1:,1]) #colorbar\n", - "\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " acetate_ax.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.5), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize*1.5, weight = 'bold')\n", - "\n", - " glc_fluxes = [-sim.EX_glc__D_e for sim in results['fluxes']]\n", - " \n", - " #add arrow indicating growth regime\n", - " #0. remove the box to improve readability of the text\n", - " acetate_ax.spines['top'].set_visible(False) \n", - " acetate_ax.spines['right'].set_visible(False) \n", - " \n", - " #1. Find the start of the overflow regime (which is when acetate is being produced)\n", - " for i,ac in enumerate([sim.EX_ac_e for sim in results['fluxes']]):\n", - " if ac >0.01:\n", - " glc_onset = glc_fluxes[i]\n", - " break\n", - " #2. determine the dx covered by respiration\n", - " dx_respiration = glc_onset-glc_fluxes[0]\n", - " #3 create respiration arrow\n", - " #forward arrow\n", - " acetate_ax.arrow(\n", - " glc_fluxes[0], 11, dx_respiration, 0,\n", - " linewidth = 2, color = 'purple', label = 'Respiration', length_includes_head = True, head_width = 3, head_length = 0.5\n", - " )\n", - " #reverse arrow\n", - " acetate_ax.arrow(\n", - " glc_onset, 11, -dx_respiration, 0, head_starts_at_zero = True,\n", - " linewidth = 2, color = 'purple', label = 'Respiration', length_includes_head = True, head_width = 3, head_length = 0.5\n", - " )\n", - " #annotate\n", - " acetate_ax.annotate('Respiration',\n", - " xy=(dx_respiration/3, 15),\n", - " xytext=(10, -10), fontsize = fontsize,\n", - " textcoords='offset points', color = 'purple')\n", - " #remove the box\n", - " acetate_ax.spines['top'].set_visible(False) \n", - " acetate_ax.spines['right'].set_visible(False) \n", - "\n", - " #4. create overflow arrow\n", - " #forward arrow\n", - " acetate_ax.arrow(\n", - " glc_onset, 11, 10-glc_onset,0,\n", - " linewidth = 2, color = 'black', label = 'Overflow', length_includes_head = True,head_width = 3, head_length = 0.5\n", - " )\n", - " #reverse arrow\n", - " acetate_ax.arrow(\n", - " 10, 11, -(10-glc_onset),0,\n", - " linewidth = 2, color = 'black', label = 'Overflow', length_includes_head = True,head_width = 3, head_length = 0.5\n", - " )\n", - " #annotate\n", - " acetate_ax.annotate('Overflow',fontsize = fontsize,\n", - " xy=((10-glc_onset-2)/2+glc_onset, 15),\n", - " xytext=(10, -10),\n", - " textcoords='offset points', color = 'black')\n", - " \n", - " #acetate graph\n", - " acetate_ax.plot([-sim.EX_glc__D_e for sim in results['fluxes']], [sim.EX_ac_e for sim in results['fluxes']], \n", - " linewidth = 4, color = 'darkblue')\n", - " acetate_ax.tick_params(axis='y', labelsize=fontsize)\n", - " acetate_ax.set_xlim([0, 10.5])\n", - " acetate_ax.set_ylim([-0.5, 15])\n", - " acetate_ax.xaxis.set_visible(False)\n", - " if ylabels:\n", - " acetate_ax.set_ylabel(r'Acetate' '\\n' '[$mmol_{ac}/g_{CDW}/h$]',fontsize =fontsize, rotation = 0,\n", - " labelpad = 30)\n", - "\n", - " #add phenotype data if this is given\n", - " if phenotype_data is not None:\n", - " acetate_ax.scatter(phenotype_data['EX_glc__D_e'], phenotype_data['EX_ac_e'],\n", - " color='purple', marker='o', s=40, linewidths=1.3,\n", - " facecolors=None, zorder=0,\n", - " label='Data')\n", - "\n", - " if title is not None: acetate_ax.set_title(title, fontsize = fontsize*1.5)\n", - " \n", - " #CAC heatmap\n", - " im_csc = csc_ax.imshow(csc_matrix, aspect=\"auto\", cmap=combined_cmap, norm=norm)\n", - " csc_ax.set_yticks(np.arange(len(x_csc)), labels=x_csc, fontsize =fontsize)\n", - " csc_ax.xaxis.set_visible(False)\n", - " if ylabels:\n", - " csc_ax.set_ylabel('CSC', fontsize = fontsize*1.25, labelpad = 30)\n", - "\n", - " #Make line between CSC and ESC data more clear\n", - " axis = 'bottom'\n", - " csc_ax.spines[axis].set_linewidth(10)\n", - " csc_ax.spines[axis].set_color(\"black\")\n", - " csc_ax.spines[axis].set_zorder(0)\n", - " \n", - " #ESC heatmap\n", - " im_esc = esc_ax.imshow(esc_matrix, aspect=\"auto\", cmap=combined_cmap, norm=norm)\n", - " esc_ax.set_yticks(np.arange(len(x_esc)), labels=x_esc, fontsize =fontsize)\n", - " esc_ax.set_xticks(np.arange(len(yaxis)),labels = yaxis, fontsize =fontsize, rotation=45, ha='right')\n", - " if ylabels:\n", - " esc_ax.set_ylabel('ESC', fontsize = fontsize*1.25, \n", - " labelpad = 30)\n", - " if xlabels:\n", - " esc_ax.set_xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - " \n", - " #colorbar\n", - " if cbar:\n", - " cbar_ax.xaxis.set_visible(False)\n", - " make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm,\n", - " vmin = vmin, vmax=vmax, fontsize=fontsize*1.25)\n", - " # fig.set_figwidth(24)\n", - " # fig.set_figheight(7)\n", - " # fig.align_labels()\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f1fc7967-5869-4911-8ed4-2cee897adbd7", - "metadata": {}, - "outputs": [], - "source": [ - " \n", - "def make_scaled_colorbar(ax, fig, cmap, norm,vmin, vmax,\n", - " fontsize=16, cbarlabel='Sensitivity Coefficient'):\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " \n", - " cbar = fig.colorbar(sm, ax=ax, cax=ax, shrink=1, fraction=1)\n", - " \n", - " # Adjust the tick intervals\n", - " tick_locations = np.linspace(vmin, vmax, num=5) # Adjust num to the desired number of ticks\n", - " cbar.set_ticks(tick_locations)\n", - " cbar.set_ticklabels([f\"{tick:.1f}\" for tick in tick_locations]) # Optional: customize tick labels\n", - "\n", - " # Setting the fontsize of the colorbar\n", - " cbar.set_label(cbarlabel, fontsize=fontsize, labelpad = 30)\n", - " cbar.ax.tick_params(labelsize=fontsize)\n", - " cbar.ax.yaxis.get_offset_text().set(size=fontsize)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "23c93efe", - "metadata": {}, - "outputs": [], - "source": [ - "def parse_reaction_id(input_str: str) -> str:\n", - " new_id = CatalyticEvent._extract_reaction_id_from_catalytic_reaction_id(\n", - " input_str, protein_id_pattern = r'^\\d+\\.\\d+\\.\\d+\\.\\d+$|^\\d+\\.\\d+\\.\\d+\\.-$')\n", - " return new_id\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ba775506-b0e9-4fc3-a403-7c68515059e7", - "metadata": {}, - "outputs": [], - "source": [ - "#adjust labels for better readibility\n", - "def adjust_heatmap_labels(labels):\n", - " new_labels = labels.copy()\n", - "\n", - " for i, label in enumerate(labels):\n", - " if 'EX_glc__D_e' in label or label[:-3] == 'EX_glc__D_e':\n", - " if label[-1] == 'B': new_labels[i] = 'EX_glc_'+label[-2:]\n", - " else: new_labels[i] = 'EX_glc_lb'\n", - " if label == 'TotalProteinConstraint_proteome':\n", - " new_labels[i] = 'Protein pool'\n", - " if label[0].isdigit(): #all enzyme ids start with a digit\n", - " rxn_ids = [parse_reaction_id(rid).split('_')[0] for rid in pamodel.get_reactions_with_enzyme_id(label)]\n", - " rxn_name = pamodel.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0]\n", - " new_labels[i] = '\\n'.join([part for part in rxn_name.split(' ')])\n", - " return new_labels\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f0b9ed5c-3082-4af5-8e58-5f56e30e5f79", - "metadata": {}, - "outputs": [], - "source": [ - "def find_nonzero_sensitivities(Cv, x_axis):\n", - " indices = []\n", - " for row in Cv:\n", - " for index, coeff in enumerate(row):\n", - " if abs(coeff)>0 and index not in indices:\n", - " indices.append(index)\n", - " \n", - " coeff_nonzero = []\n", - " for row in Cv:\n", - " coeff_nonzero.append([coeff for i, coeff in enumerate(row) if i in indices])\n", - " x_coeff_nonzero = [coeff for i, coeff in enumerate(x_axis) if i in indices]\n", - "\n", - " return coeff_nonzero, x_coeff_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f3415083-0114-49b5-84f1-6d280deeef8f", - "metadata": {}, - "outputs": [], - "source": [ - "def find_top5_sensitivities(Cv, x_axis, yaxis, threshold = 0.05):\n", - " #top 5 enzymes per simulation\n", - " Cv_df = pd.DataFrame(Cv, columns = x_axis, index =yaxis)\n", - " largest = list()\n", - " for i, row in Cv_df.iterrows():\n", - " top5 = abs(row).nlargest()\n", - " if top5.iloc[0]:\n", - " largest += [index for index, value in top5.items() if abs(value)>threshold]\n", - " #remove duplicates\n", - " largest_list = list(set(largest))\n", - "\n", - " #extract non duplicate top5 enzymes\n", - " top5_df = Cv_df[largest_list].T.drop_duplicates().sort_index()\n", - " largest_list = top5_df.index.values\n", - "\n", - " top5_matrix = [list(row) for i, row in top5_df.iterrows()]\n", - " return top5_matrix, largest_list\n" - ] - }, - { - "cell_type": "markdown", - "id": "c259d078-bc25-4abc-9725-54b159fcbe79", - "metadata": {}, - "source": [ - "### 2 Run GECKO simulations\n", - "#### 2.1 Adjust the total protein constraint for the lack of translational and unused protein" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "edf9d83b-43b2-469d-a031-2a487f22efd7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.11123186965376783 g_p/g_cdw/h\n" - ] - } - ], - "source": [ - "#calculate average protein which is allocated to sectors (5 mmol_glc/gdw/h and growth rate of 0.35)\n", - "translational_info = pd.read_excel(PAM_DATA_FILE_PATH , sheet_name='Translational')\n", - "unused_protein_info = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name = 'ExcessEnzymes')\n", - "\n", - "#protein allocated to unused enzymes\n", - "ups_0 = unused_protein_info[unused_protein_info.Parameter == 'ups_0'].loc[2, 'Value']\n", - "smax = unused_protein_info[unused_protein_info.Parameter == 's_max_uptake'].loc[1, 'Value']\n", - "ups_mu = ups_0 / smax\n", - "\n", - "ups = ups_0 - (5*ups_mu)\n", - "\n", - "#protein allocated to translational enzymes\n", - "tps_0=translational_info[translational_info.Parameter == 'tps_0'].loc[1,'Value']\n", - "tps_mu=translational_info[translational_info.Parameter == 'tps_mu'].loc[2,'Value']\n", - "\n", - "tps = tps_0 + (0.35*tps_mu)\n", - "\n", - "#total protein left for the active enzyme sector\n", - "total_protein = 0.258 - tps - ups\n", - "\n", - "print(total_protein,' g_p/g_cdw/h')" - ] - }, - { - "cell_type": "markdown", - "id": "54a7cc6d-577a-48cc-bd51-e6965f3484e2", - "metadata": {}, - "source": [ - "#### 2.2 Build GECKO model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "41159a71-2c44-4815-a5ec-41c380741133", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "Read LP format model from file /tmp/tmpddxh55pe.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.11123186965376783 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "# build the GECKO model with PAModelpy\n", - "pamodel_gecko = set_up_ecoli_pam(unused_enzymes = False, translational_enzymes = False, total_protein =total_protein)" - ] - }, - { - "cell_type": "markdown", - "id": "2b696f7d-d722-49ba-b5ff-4ac0e846aa79", - "metadata": {}, - "source": [ - "#### 2.3 Run simulations for glucose uptake of 0-10 mmol/gcdw/h" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6b9e3329-f635-4e9f-be49-bfe044aeab30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "glucose uptake rate 0.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000222\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.019923 \n", - "\n", - "glucose uptake rate 2.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000759\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.070857 \n", - "\n", - "glucose uptake rate 2.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000616\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.059771 \n", - "\n", - "glucose uptake rate 3.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000602\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.052536 \n", - "\n", - "glucose uptake rate 3.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001816\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.164562 \n", - "\n", - "glucose uptake rate 4.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001337\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.160092 \n", - "\n", - "glucose uptake rate 4.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.009369\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.150594 \n", - "\n", - "glucose uptake rate 5.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.00918\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.126258 \n", - "\n", - "glucose uptake rate 5.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008899\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.091465 \n", - "\n", - "glucose uptake rate 6.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008635\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.058735 \n", - "\n", - "glucose uptake rate 6.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008834\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.084114 \n", - "\n", - "glucose uptake rate 7.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.009218\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.140851 \n", - "\n", - "glucose uptake rate 7.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.009026\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.124982 \n", - "\n", - "glucose uptake rate 8.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008842\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.109759 \n", - "\n", - "glucose uptake rate 8.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.011449\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.443317 \n", - "\n", - "glucose uptake rate 9.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.012052\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.522156 \n", - "\n", - "glucose uptake rate 9.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.012018\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.520266 \n", - "\n", - "glucose uptake rate 10.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.011991\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.518 \n", - "\n" - ] - } - ], - "source": [ - "results_gecko = calculate_sensitivities(pamodel_gecko)\n", - "x_axis_csc_gecko,x_axis_esc_gecko = parse_x_axis_heatmap(results_gecko['capacity coefficients'], results_gecko['enzyme coefficients'])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0e45ccd4-69f1-4ca5-96fc-5e9c77523197", - "metadata": {}, - "outputs": [], - "source": [ - "#get all nonzero sensitivities\n", - "csc_nonzero_gecko, x_csc_nonzero_gecko = find_nonzero_sensitivities(results_gecko['Ccsc'], x_axis = x_axis_csc_gecko)\n", - "esc_nonzero_gecko, x_esc_nonzero_gecko = find_nonzero_sensitivities(results_gecko['Cesc'], x_axis = x_axis_esc_gecko)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d285624b-a4e2-4189-a7c1-c3abd74b7e05", - "metadata": {}, - "outputs": [], - "source": [ - "csc_nonzero_gecko_t = np.transpose(np.array(csc_nonzero_gecko))\n", - "esc_nonzero_gecko_t = np.transpose(np.array(esc_nonzero_gecko))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "54338172-b87a-4b41-9cca-d189c740e15a", - "metadata": {}, - "outputs": [], - "source": [ - "#get top 5 nonzero sensitivities\n", - "csc_top5_gecko, x_csc_top5_gecko = find_top5_sensitivities(results_gecko['Ccsc'], x_axis = x_axis_csc_gecko, yaxis = glc_uptake_rates)\n", - "esc_top5_gecko, x_esc_top5_gecko = find_top5_sensitivities(results_gecko['Cesc'], x_axis = x_axis_esc_gecko, yaxis = glc_uptake_rates)\n", - "csc_top5_gecko_t = np.transpose(np.array(csc_top5_gecko))\n", - "esc_top5_gecko_t = np.transpose(np.array(esc_top5_gecko))" - ] - }, - { - "cell_type": "markdown", - "id": "e58822c2-7c17-4c99-b0c5-cd52c6862db9", - "metadata": {}, - "source": [ - "### 3 Run PAM simulations\n", - "#### 3.1 Build PAModel" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bf9cd6cb-6992-4df6-a10b-47405063969e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmpm41if_f3.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.258 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel = set_up_ecoli_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "a87b7228-cc6b-42d7-81b1-ed069b20b55d", - "metadata": {}, - "source": [ - "#### 3.2 Run simulations for glucose uptake of 0-10 mmol/gcdw/h" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "191d8735-d3a4-4fc0-afd7-960dc42bb628", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "glucose uptake rate 0.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002288\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.246793 \n", - "\n", - "glucose uptake rate 1.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000682\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.09303 \n", - "\n", - "glucose uptake rate 1.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0004\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0661 \n", - "\n", - "glucose uptake rate 2.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000288\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.055724 \n", - "\n", - "glucose uptake rate 2.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.00103\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.157977 \n", - "\n", - "glucose uptake rate 3.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000931\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.16114 \n", - "\n", - "glucose uptake rate 3.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000581\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.162681 \n", - "\n", - "glucose uptake rate 4.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002886\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.891683 \n", - "\n", - "glucose uptake rate 4.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.003329\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.128506 \n", - "\n", - "glucose uptake rate 5.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.003113\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.13419 \n", - "\n", - "glucose uptake rate 5.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002869\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.117895 \n", - "\n", - "glucose uptake rate 6.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.00266\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.103972 \n", - "\n", - "glucose uptake rate 6.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002479\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.091937 \n", - "\n", - "glucose uptake rate 7.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002351\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.095135 \n", - "\n", - "glucose uptake rate 7.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002211\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.086089 \n", - "\n", - "glucose uptake rate 8.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002087\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.07806 \n", - "\n", - "glucose uptake rate 8.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001976\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.070886 \n", - "\n", - "glucose uptake rate 9.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001876\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.064437 \n", - "\n", - "glucose uptake rate 9.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001786\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.058608 \n", - "\n", - "glucose uptake rate 10.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001704\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.053314 \n", - "\n" - ] - } - ], - "source": [ - "results_pam = calculate_sensitivities(pamodel)\n", - "x_axis_csc_pam,x_axis_esc_pam = parse_x_axis_heatmap(results_pam['capacity coefficients'], results_pam['enzyme coefficients'])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3f87f6f6-a880-4adb-b1f8-3daf07f8a9f6", - "metadata": {}, - "outputs": [], - "source": [ - "#get nonzero sensitivities\n", - "csc_nonzero_pam, x_csc_nonzero_pam = find_nonzero_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam)\n", - "esc_nonzero_pam, x_esc_nonzero_pam = find_nonzero_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam)\n", - "csc_nonzero_pam_t = np.transpose(np.array(csc_nonzero_pam))\n", - "esc_nonzero_pam_t = np.transpose(np.array(esc_nonzero_pam))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b893abf9-cc29-470d-85ab-23e575200079", - "metadata": {}, - "outputs": [], - "source": [ - "#get top5 nonzero sensitivities\n", - "csc_top5_pam, x_csc_top5_pam = find_top5_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam, yaxis = glc_uptake_rates)\n", - "esc_top5_pam, x_esc_top5_pam = find_top5_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam, yaxis = glc_uptake_rates)\n", - "csc_top5_pam_t = np.transpose(np.array(csc_top5_pam))\n", - "esc_top5_pam_t = np.transpose(np.array(esc_top5_pam))" - ] - }, - { - "cell_type": "markdown", - "id": "999ba86b-fa39-470a-ad63-9c99da737ee7", - "metadata": {}, - "source": [ - "### 4 Create plot" - ] - }, - { - "cell_type": "markdown", - "id": "df74f52e-fdf7-4b82-b797-297bd4a139a7", - "metadata": {}, - "source": [ - "#### 4.1 Load phenotypic data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b11ae446-27b5-4e18-b3bb-9553acbb397c", - "metadata": {}, - "outputs": [], - "source": [ - "# load phenotype data from excel file\n", - "pt_data = pd.read_excel(os.path.join(DATA_DIR, 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'), sheet_name='Yields', index_col=None)\n", - "pt_data['EX_glc__D_e'] = -pt_data['EX_glc__D_e']" - ] - }, - { - "cell_type": "markdown", - "id": "1feb4cc2-599f-4947-af30-daa66489dd3f", - "metadata": {}, - "source": [ - "### 4.2 Load manually created plot with mapped sensitivities" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7280d7de-3ee7-46ef-aa2c-945f4401db78", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Software/anaconda3/envs/PAModelpy/lib/python3.9/site-packages/PIL/Image.py:3182: DecompressionBombWarning: Image size (109343152 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "image_path = os.path.join('Figures','Figure2_mapped_sensitivities.png')\n", - "\n", - "sensitivities_mapped =np.asarray(Image.open(image_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "419a3716", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_138825/167582513.py:24: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig_pam.subplots_adjust(left=0.3)\n", - "/tmp/ipykernel_138825/167582513.py:40: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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1ze4nJSXZtT253uLFi5GZ+d/qmn369LE5uWngwIFwc/vvw3TZsmXIyLA90LkwXbx4EVJKs1tGRgZiY2Nx4MABTJ06Fd26dYNK5ZyPxHsTxeUkk5s3b54pmWPz5s3RuHFjp/RFufP29sayZcvg4eFhVu7n54elS5eavUdLivLlyztcN7eJikRERERERERERETkmOTkZLNbVlaW1fr3X6cD7i5644j7z3sDQHq64wNu86tGjRqIjIzEtm3bMGjQoHwlIdNqtXjrrbdw+vRpzJw50+EEekRERGVFRpYe3yzeCWljlW8hBMYP6wKNRu2awIiIiMqgW0mZMCiWX8o1K3iDebmJiIiIiIiIiIjy59jlO5i55YzN6195EUKiSbUAvNGjERfMIyJyEaOjH9pETtKiZhDe7NEIjn7zSwmcjknE1A2nTPk9iKhsCvH3wgtdWgA2PlGkAnw6ZxuUXMYIlERMJldG6PXmKyRmZ2fne9sLFy7g9u3bZmWVK1e2K7ERAPj7+wP4b4X75ORku7Yn15s7d67Z/eeee87mNgEBAejZs6fpfnJyMv7880+nx1acDRo0CBqNBgBw5swZ7N+/H/PmzTM9fm+yOSo8Go3GIkFg48aNUa1ayVyd3cvLy+G6qampzg6HiIiIiIiIiIiIyjgBQIgydvv/fa9SpQr8/PxMt4kTJ1p9ru6/Tgc4nkwut+3sue5XEEePHsXs2bOxZs2afA2w0ev1+PXXXzF27FgcPnzYBRGSPe59X5bERXiIiEobKSVW7z6FmHjbY2nqhQbjiQfruCAqIiKisuv6HcvE8ABQr7K/awMhIiIiIiIiIiIqofQGBa9M3wO9UXG4DTe1GrNfaw93Ny6yRETkKsy9RUVNCIFx/ZqhRnkfhxd5khL4YsURxNwp/MWaiaj4EkLgnQFt4eGmsVpPQuKvI+dx9lq8iyIrXEwmV0b4+fmZ3U9MTMz3tgcOHDD9LaWEEAItWrSwO4b7JyFkZWXZ3Qa5TnR0NPbu3WtW1qNHD2g0Gpu3iIgIs+3mzJnjusCLgZCQEHTt2tV0/7XXXsOFCxcA3E1wNmjQoKIKrczIyMhA//79kZZmPqhv9+7dmDBhQhFFVTD374s9db29vZ0dDhEREREREREREVGZdfXqVSQlJZluH3zwgd1tOLpacm7bFfbKiZmZmXj99dfRvHlzLFiwACkpKWaPBwUFoV69eqhdu7bFNcnMzEwsW7YMLVu2xJgxY2A0Ggs1Vsq/uLg409+8jkBEVPT0BiM+m7fN5ve6EAIfvfAo3DScMENERFSYrsSn5TpZqm4lX4eP6YmIiIiIiIiIiMoKKSX+2H0BB87FOZyUSAggvGtDNKse6NzgiIjIKgnJhHJU5LzcNfjt1bZQF+C6XEqGHm/N3gdF4RuaqCyrWt4ffTs0gq3slAaDxKTFfxf6uHxXYDK5MsLf3x/AfxNMTp06le9t9+3bZ1HWqlUru2O4c+cOgP8mtHh5edndBrnO3LlzLcqMRmO+bopivlLAtm3bEBMT46rQi4Xnn3/e9PfBgwdNfz/xxBMoV65cUYRUZO4fPJffL8/U1FSH+wwPD8fx48dN9+/9vPn0008RGRnpcNtF5datW/mue/PmTbP7AQEBzg6HiIiIiIiIiIiIqMzy9fU1u+l0Oqv1tVqtRVlGRoZDfee23f0LOjlTeno6unfvjp9//tns/H758uXx9ddf48KFC4iPj8fp06dx9uxZJCYm4uTJkxg7dix8fX1N9aWU+Prrr/HCCy9YXEci17t48aJZMrmQkJAijIaIiAAg8sgFnLl622a90BBfPPlIQyaxISIiKkRSSpy7kWxRLgQQGsRxr0RERERERERERLYkpmXj/fkHCpSMqFqINz4Z0JzXxYiIXKwU5NChUkAIgfYNK+DZtjVt5X/Kk5RAxIEr2Hkq1rnBEVGJIoTAmGc7QKuy/mEiIbF0+zHE3kmxWq8kYDK5MqJGjRqmv6WU2L17d7633bJli8XBdrt27eyO4fZt80Gv904goeJFURTMnz+/2LZXEvTu3duUxPFe9yaZKyvuTxyZnp6er+2uX7/uUH/z5s3D7NmzTfc7d+6M3bt3w8PDA8DdpIiDBg2yKzlbcXDkyJF81z169KjZ/YYNGzo5GiIiIiIiIiIiIiLKL09PT4uyzMxMh9rKLZlcbu07y6hRo7B9+3azso4dO+L48eN47733zK5B5mjQoAEmTJiAI0eOoEmTJmaPLVy4EN9++22hxUu2ZWVlYfTo0ab7QgiL14mIiFzLqCj4fN52m/WEEBjdvy08dJaJaomIiMi5zsbmlkxOoIJ/4R2DExERERERERERlQZSSny+/AhuJTk2NgYA1CqBX19+GN7uGidGRkRE+cFcclRcqITA5KEPIdjH+oLP1hgViVdn7kVmttGJkRFRSdOgWjm0b1rdZr30LD1mrj1gtgB7ScSjqDKiRYsW0Gg0MBrvfskdOXIEp0+fRv369a1ud/r0aZw6dcosmZybmxtat25tdwznz583u1+pUiW72yDX+Ouvv3D16lXT/U6dOmHr1q12tXHlyhVUr17d9CE5d+5cjBkzxqlxFmc6nQ79+/fHjBkzTGX+/v7o3bt3EUZVNAICAszuX758GY0aNbK53d9//213X6dOncKrr75qul+xYkUsWLAA5cqVw48//oiXXnoJAHDjxg0MGTIEGzduhEpVMvKqrl271mxylzWrV682u//QQw8VRkhERERERERERERUhgmVgLCxQldpk7O/YWFhUKvVCA8PR3h4uM3tAgMDLcpSU1MdiiEtLc2iLCgoyKG2bImMjMS8efPMyurXr4/Vq1fDx8fH5vY1atTA+vXr0aJFC8TFxZnKP/74YwwdOhTBwcFOj7mk+eyzz/JdNzY21q769zIajUhNTcWlS5ewY8cOJCQkQAgBKSWEEA4tJEZERM5z/OJN7D951eYgtEAfDzzXpYXFgpBERETkXBJAzG3LBVM1KoGgAkxWISIiIiIiIiIiKgvOXE/CtE2n4Gj+BSGApx6shs4PVOZ1MSKiolDCE+hQ6RLso8OUFx7CCz//7fBb83RMImZtO4NXuzbgbwuiMkqlEvjo+cew/chFKEreHyZSAr9E7MNbz7SDj2fJHRvAZHJlhLu7O1q0aIEDBw6Yyt577z2LhEP3++6770x/50wmeOyxx6DT2f+m37t3r2lSAgDUrVvX7jbINebOnWt2//nnn7e7japVq6JDhw6IjIwEcDcx4f79+8tUUqvRo0ejQYMGpvs1a9aEu7t7EUZUNBo2bGh2f8eOHejevbvVbTIzM/H777/b1U96ejqeeeYZ00Q6tVqNP/74A+XKlQMAjBgxApGRkVi4cCEAYMuWLfjyyy8xbtw4u/opKn///TeOHj2Kpk2bWq137do1rFy50nS/bt26aNy4cWGHR0RERERERERERFRmREVFwdfXN9/1y5cvb1F27do1h/rObbuc8+DO9v3331uUTZo0KV+J5HKEhoZi3LhxeOONN0xlGRkZmDVrFt5//31nhFmijR8/3ubgpJxrqzdv3sSnn35a4D5z2svpV6fT4dlnny1wu0RE5BgpJb5eGAmjolitJwTw6lOt4edd9sYcEBERuZpRkbiZnGFR7qnTwEvHYddERERERERERER5MSoK3pi1D1l669e+rPHz0OKHF1tDVcYWuSQiKi6YS46KEyEEBrStielbTmP36VsOtSEl8MmSw+j/cE2E+HHcDVFZ1aZRNTSqXh7/Xoi1Wi8uKQMRu05iSOdmJTYBpaqoAyDXGTp0KACYErqtW7fO6iSN9evXY9asWRZv7iFDhtjdd1JSEk6dOmW6r9PpUL16dbvbocKXkpJilojKy8sLTz/9tENt3Z+E7v4kdaVdnTp18Oabb5puvXv3LuqQikTFihXN/t/nzJmDpKQkq9u8+eabuHHjhl39hIeH48SJE6b748ePR4cOHczqTJs2DfXq1TOrs2PHDrv6KSpSSrz00kvIyLAcqJjDaDRi5MiRSE//b2XckSNHltgfKURERERERERERESlQbVq1SzKrly54lBbuW1Xo0YNh9qyxmAwYOvWrWZlgYGB6NGjh91tDR482OI89ZYtWwoUX2kjpcz1lp869t6EEGYLgH355ZcICgoqit0mIiIA124lYtXu07A1FttTp8UrvR/ktV8iIiIXyDYYkZiaZVHu6+kGrZrDromIiIiIiIiIiHIjpcTWY9ex7fgNm9e+8iIAfDKgBSoGeDgzNCIisoP8/xtRcaFVqzDjlUfgrnX8Ol1CWhY+WXzIYlwmEZUdGrUK4557NF+LgH/9RyQMRscTZBc1jmooQ4YMGQJ/f38A/yWUmzx5Mlq3bo05c+bg6NGjOHfuHP766y+MGjUKffr0gXLfyscVKlRA37597e579erVZqvct2rVigNci6lly5aZJaJ6+umn4eXl5VBb/fr1g6enp+n+4sWLkZVlOciKSr97EwveunUL/fr1w507dyzqxcfH44UXXsD06dPh5uaW7/bnzp2LOXPmmO537twZY8eOtajn7e2NZcuWwcPj7slEo9GIQYMGIS4uzo69KRo6nQ5RUVHo1q0bLl++bPF4fHw8nnnmGaxfv95UVrduXbz66quuDJOIiIiIiIiIiIiI7nPvIic5Lly44FBbuW2XW/sFdf78eaSlpZmVtWzZEiqV/ZeXg4KCUKtWLbOyexeHIZgSvN1/y08de27Af0npfH198fPPP+PNN98sgj0mIiLg7mfyL6v3IyNbb7WegMDT7RuhQqCPiyIjIiIq29IzjcjINlqUV/T3gFrNca9ERERERERERES5ydIb8dbsfVAUx5K0CAE0Cg3AyM71Of+ciKgIMdkWFUf1K/vjtW4N4ehPBCmBOZFncTom0alxEVHJIYRAj9b1UDnY9hi801fisf+UYwvHFweaog6AXMfb2xtTpkzB8OHDzVacP3DgAKKioizq56xMf+/fH374IXQ6nd19z50716yddu3aFWxnqNDcm5ALME8CZi8fHx/06dMHCxcuBAAkJCRg9erVeOaZZwoSIpVA//vf/zB9+nTcvHkTALB161bUrFkTPXv2RM2aNZGZmYnTp09j27ZtSE9Ph1qtxs8//4yXX37ZZtsnT540S5hWsWJFLFiwIM8JbU2aNMGPP/6Il156CQBw/fp1DBkyBBs2bHBoEpyrTJ48Gf/73/+wY8cO1K1bF48//jgaNWoEtVqN6OhobNy40SwRpIeHB+bOnQt3d/cijJqIiIiIiIiIiIhKLbXq7q0s+f/9DQsLg1qtRnh4OMLDw21u1qxZM9N1uRz79u1zKIT7txNCoGnTpg61Zc3t27ctyoKDgx1uLyQkBOfOnTPdz23BmbKoatWqVgdAX7582fTe0Wg0qFSpkkP9aDQaeHt7IyAgAI0aNUKbNm3w1FNPmS0KRURErpeakY3f1x20WU+jERgz2PaqqEREROQcyRnZMOQy4bVyoCf4bUxERERERFS4chZGulduCzEREVHxIqXE739F48z1ZIfbUKsEfh35CNzd1E6MjIiI7MVUclQcCSHwYb9m+GPXBcTcSbe9QS6yDAremRuFNR90hlrFY0yisshDp8WrTz6ED2dttZo8VVEkvl60Aw9PqA5VCfy8YDK5MmbYsGHYuHEjli1bZpZQLrc3+b0nWYUQ6NChA0aNGmV3n5cvX8b27dvNJsl07NjR4X2gwnPhwgXs2rXLdL9KlSp49NFHC9Tm888/b0omB9xNLMhkcmVPUFAQFi9ejJ49eyItLQ0AkJSUZPbeyOHu7o7p06ejffv2NttNT0/HM888Y0qiplar8ccff6BcuXJWtxsxYgQiIyNN/W/evBlffvklxo0bZ++uuUzPnj2RmZmJ999/H9nZ2Vi/fj3Wr1+fa10vLy/8+eefaN26tYujJCIiIiIiIiIiIir9oqKi4Ovrm+/6fn5+aNy4Mf79919T2cmTJ5GQkICAgIB8tyOlxN69e83KmjZtCh8f2yuk2Su3hUoyMjIcbu/exVAAMInZ/7t06ZLVx+9dBKdRo0b4559/CjkiIiJyFSklVu48gfhk69+vQgi0f6AG6oY6ntSViIiI7BOXnJnrmNrQIK8iiIaIiIiIiKjoWJtUC6BQErwdO3YML774olnZ0KFD8dprrzGhHBFRMZaYlo1PlxyGja+OvAmJZ1rXQpv6IU6Ni4iI7OfwZzlRIfP10GLKCw9h0A+RUHJZGMoWKYHNR2Ow48QNPNbEsYV9i4qUEoqi5KuuSqUq8PFzfvqzt5/87oMQwmzsrD1ycidlZWUhISEBt27dQnx8PDIzM6EoClQqFbRaLXx8fFChQgUEBgbCy8sLGo3Goecsp7/8nD/Jq/3COtdRGK9hYbbrKkIIDO/xIL5cuAOpGdlW6245dB5XbiWgeoVAF0XnPEwmVwYtWrQIKpUKS5YsydfKHFJKNGvWDEuXLnXoH3bSpEmQUpq29fHxYTK5YmrevHlmX1SDBw92+Is2x+OPP47KlSsjJiYGALBp0ybExsaiQoUKBWqXSp6OHTtiz549ePPNN7F9+3aLx4UQ6NixI7799ls0a9bM5gQqAHj11Vdx8uRJ0/3x48ejQ4cO+Ypn2rRpOHjwIM6cOWPatl27dvneviiMHj0aTZs2xZgxY3KdMKZWq9GzZ098//33qF69uusDJCIiIiIiIiIiIqJcdenSxSyZnJQSq1atwtChQ/Pdxvbt25GQkGDRbmEICbEcnJuf8/a5URQFly9fNisLDmZCHHsU10EVRETkOL1RwaRFkfkYTAiMe/7RErnCKRERUUl1KzETyOUrumYFHx6fERERERFRqZUzGTgxMRFnzpzB4cOHcfr0aVy6dAlpaWkwGAwQQkCr1cLLyws1atRAgwYN0LRpU9SuXRsBAQFOmSyclpaGw4cPm5077dy5c0F3j4iICpGUEl/9eRTxqVkOt+Hn4YZvXgiDiuffiIiKXH4SIxEVBSEE+rauhkc2lsPOU7EA7P/doCgS/5u1F4e+eQo6rdr5QRaSffv24aWXXrL5v6lSqbBs2TLUr1+/QP0tWLAAX331VZ6Pe3t7IzIyEh4eHvlqT1EU/PLLL5g2bZrVfdBqtZg4cSK6du1q1/kFRVFw7do1rFq1CitWrMCJEyeQmJgIo9GY5zZCCOh0OpQvXx4tWrRA165d0alTJ1SpUiXfyeWMRiOeeOIJxMbGWq2n0+ng7e0NtVoNrVYLnU4Hf39/hIaGolq1aqhWrRpq166NihUrwtPTM195oGxJSkpCx44dodfr86zz4YcfYtCgQXa1+++//2LQoEF5vo4qlQrr1q1D1apV7WrXlYL9PPHsY03x27qDkLkNDPh/2XoFUyP2YdLIbiVunACTyZVBarUaf/zxB3r16oX333/flOQrNzqdDi+//DImTpwIT09Pu/u6evUqZs2aBeC/1Ui6d+8OrVbrWPBUqMaPH4/x48c7tU2VSoVr1665tP/IyMgCt5GXjh07FsoBkL+/f5EeWDkyAcyR5/mBBx7AX3/9hcuXL2Pnzp24ceMGVCoVQkND8eCDD6JGjRqmutWrV7f5nMyZMwdz5syxOw7g7o/U06dPO7RtQeT3dR46dGiukwg7d+6Mzp074+TJkzh69ChiYmJMz+Gjjz6a6wQ/IiIiIiIiIiIiIipaAwYMwJQpU8zKZs6caVcyud9++82ibODAgQUNLVcVKlSAl5cX0tLSTGVHjx51aMGg/fv3IzEx0aysTp06zgizTODAPCKi0mnfiSs4c+22zXr1QoPQplE1F0REREREOS7Fpd6de3LP4ZgQQHm//E2GICIiIiIiKkmklIiLi8PSpUsxf/58/Pvvv8jMzDQ9Zk3ORFp3d3fUqVMHTz31FAYOHIg6depArVaXuIm2RETkmCvxqfhl42k4OrxBCODDp5uhYgDPvxEREZF1GrUKP41ogwffX41sg+JQG6dikrB870UMalerRBy3Sinx3Xff4cSJEzbrCiEwbdo0fPfddwXatzt37uDkyZN5Pu7r6wtFyd/zrygKfvjhB7z77rtWk7u5ubnh119/tSuRnNFoxK5duzB+/Hjs2bMHer0+32NupZTIyMjApUuXcOnSJfz5559QqVSoV68eXnrpJQwaNAghISFWY5FS4uzZs7h69Wq++sxLTvI4X19fNGvWDP3798fTTz9ts39rjEYjTp06hezs7Dzr3Llzx+5209PTcfLkyTyfZyGE1T6LAyEE3ur/COZsOgS9wdr7RWLOhn/w0fOPwdfT3WXxOQOTyZVhgwYNwsCBA7Fr1y5s2rQJV65cQVxcHNzd3VGhQgW0adMG3bp1Q7ly5Rzu48iRI3juuefMyuyZFENEpVNOhlxyXMOGDdGwYcOiDoOIiIiIiIiIiIjKKKESEKriP4jCmXL2NywsDGq1GuHh4QgPD8/XtmFhYWjWrBmOHDliKtuzZw82bNiAbt262dz+6NGjWL58uVlZy5Yt0bx58/zvgB20Wi06duyIdevWmcqklPjmm28skuLZ8uWXX1qUde7cucAxlgUXL140/e3m5laEkRARkTMpUmLykr8hbYypFAJ479mO0KpVrgmMiIiIIKXE9YQ0i4mvEhKhQV5FExQREREREVEhkFLixo0bmDBhAubNm4e0tDS7FznKqZ+RkYFjx47h33//xYQJE9C4cWN88MEH6NWrFzw8PErE5HwiInKMlBIfLjyEtGyDQ9sLAdQq74tXuzbg9wURUTHBtU+puGtSNQDPta+D37efcej9KiUwduFB9A6rBh8PrfMDdLLExERs2rQpX3WllFi8eDEmTJgAL6+iv7apKAq+++47vP/++3kmkhNCQKfTYeHChejTp0++fhNKKXHp0iWMHDkS27Ztg5SywAs3SylhNBpx8uRJvP3225gxYwaOHTsGjabw03LlxJ+YmIjIyEjs2LED7777LgYMGIDPPvsMlSpV4m9lJ6sTGoxHGlfHjiMXIZH3e+dOSgZW7z6FwY83K1GvAZPJlXEqlQrt27dH+/btC6X9Xr16oVevXoXSNhEREREREREREREREZGrRUVFwdfX1+7tPvjgAwwYMMCsbOTIkTh06BBCQkLy3C4tLQ3Dhg2DXq83Kx87dmy++u3YsSN27NhhVrZ9+3Z07NjR6nb9+vUzSyYHAD/88AM6duyY7+t/3377LdauXWtWptFo8NRTT+Vr+7KOC/MQEZVO124lYcuhc1YHogFAsJ8X+rZvVKIGohEREZUG126nW5QJCAR564ogGiIiIiIiIuczGAz47bffMGbMGCQnJxd4wnWOnInXR48exbPPPovq1atjypQp6N27N9RqtVP6ICKi4uXfywlYtvcibFz2ypMQAt8NbQ0PN35PEBEVFwqzyVExJ4TAhMEt8OeBS7iTmuVQG9fupGPqhlN4v0+TYj8uZ8OGDUhJScl3/Vu3bmHXrl144oknCjEq2xRFweTJkzF27Ng8E8kBgE6nw+LFi9G7d+98J5JbtWoVhg0bhqSkJKed07i/j6wsx95bzuo/LS0Ns2fPxsqVK/HNN99g2LBhPLfiRGqVCu8Pao8dR60fy0gp8e2SXRj4WFNo1MX7s+JeXLqWiIiIiIiIiIiIiIiIiKiQ9e/fH+3atTMru3r1Kh5++GGcPHky122uXr2KRx99FIcPHzYrf/TRR9G3b99CixUAnnvuOdSrV8+szGg0om/fvpg4cSIyMjLy3PbOnTsIDw/HO++8Y/HYiBEjUKNGDafHS0REVBJIKfHr6n3I1is2677YtRW8PdxcEBURERHd60aCZTI5lRDw9dQWQTRERERERETOlZKSgv79++PVV1/NddK1EAJCCOh0OtSoUQOdO3fGCy+8gA8++AAffvgh3nrrLfTr1w+tWrVCcHAwVCpVrpO9pZS4ePEi+vXrh4MHD7pq94iIyIUUReL9BVHQK44l8BBCol398niieaVin8SFiKgsSckwFHUIRDaF+Lrjo2eawdGfEFICU9b8i4TUbOcG5mRGoxE///yzXQnTpJT45ZdfCiXJWn4pioJvvvnGaiI5IQTc3d2xZMkSuxLJ/fTTT+jfvz8SExOLdB9dQUqJxMREjBw5EqNGjSrSBHelUcemNVG1nJ/NescuxuLo+RsuiMh5NEUdABERERERERERERERERFRWTBv3jy0aNECCQkJprJz586hSZMm6N27N9q1a4dKlSrh5s2biIqKwtKlS6HX683aCAwMxJw5cwo9VrVajZkzZ6Jz585mAxAMBgPGjh2Lb7/9Fj179jRNFDEajbh58yb27duHdevWIS0tzaLN2rVr4/PPPy/02ImIiIqr1Iws/L7uoM3BjO5uGox66iFOnCEiInIxRQJxyZkW5TqtGt4eTCZHREREREQl2+3bt9GlSxccPnw41yRyOp0OnTt3xssvv4wHH3wQAQEB0Ghyn34qpURWVhZiYmKwbds2zJkzB4cOHYLBYDBrW0oJg4HJKIiISqM9Z25i67HrgIM5PLRqNX4Y3hoatcq5gRERUYHojcb//03P8QpUfAkh8NLj9fDTupO4cCvFoTbupGbhq5XH8PXzrYrt+JwrV67g0KFDdm+3bds2xMfHIyQkpBCisk5RFEycOBEff/wxFCXvxTbd3d2xdOlS9OjRI9+J5BYvXozRo0dbjKvOTU6yfF9fX1SrVg0hISEICQlBWloa4uPjERMTg7i4ONPC2sU5MZ2iKPjtt98QFxeHJUuWwM2Ni5M6g5tWjdeeegjvTt9ktZ6iSHy3dBfmf9i/2H5W3I/J5IiIiIiIiIiIiIiIiIjILkItINQl44Kos+Tsb1hYGNRqNcLDwxEeHm5XG9WrV8eaNWvQtWtXpKammsoVRUFERAQiIiKsbu/j44N169ahatWqdsfviHbt2mHhwoUYNGgQsrPNV2CMj4/HnDlz8p3YrnLlyti4cSOCg4MLIVIiIqKSYf3+aMQnZ1itIyDQuWVthOZj5VMiIiJyLqNRwe1cksl5aFXQclIrERERERGVYGlpaXjyySdzTSSnVqvRr18/TJw4EdWqVYNKZfv4RwgBDw8P1K5dG7Vq1cKIESNw7tw5TJw4EUuWLEFWVlaxnohNREQFYzAqeHdeFIyKg5/1Ahj4cC00qRrg3MCIiKjA7uaREw4nCyVyFQ83NSYPfRD9Jv8FxYHfJFIC07acwhu9GqJyoFchRFgwUkosWrQoX4nT7peeno7Vq1fjxRdfdGnyK0VR8OWXX+KTTz6xmkjO09MTy5YtQ7du3fId3+nTp/Hyyy/bfD6EEPD29saQIUPw4osvon79+vDw8DA71yGlhNFoRGpqKs6dO4c1a9ZgxYoVOHPmDBRFcer5jNDQUAwYMABqtRoAYDQaERcXh6tXryI6OhqxsbH56lNKiVWrVuH999/HlClT8nXuhqwTQuC5J1ri03mRSM3Islp39Z5TuJ2cjmC/4vdZkRsmkyuDbt68if3795uVhYSEoE2bNk7tZ8+ePYiPjzcre/jhhzlBhMgBea3k46ht27ahQ4cOTm2ztPjss8/w2WefOa29Dh06YNu2bU5rj4iIiIiIiIiIiIiKVlRUFHx9fR3e/pFHHsGePXvQv39/nD59Ot/bNWjQAMuWLUOjRo0c7tsRTz/9NA4cOIAhQ4bg+PHjDrXRr18/TJ8+HYGBgU6OrmzKysrC0aNHcfbsWSQlJSEpKcmhAUP3+vjjj50UHRER5cWoKPhu2S6bg/+ECnh/UAeoSshKpkRERKWJXpFIzzZalAf66JhMjoiIiIiISixFUfDqq69iz549ZucnhRDw8/PDrFmz8NRTTzk8EVkIAbVajXr16mH27NkYN24cXn/9dWzevJkJ5YiISqnIE7GIOh9vu2IevHUaTBjcwqXJTYiIKH8UKXMyyhV1KERWCSHQs2UVPFQ7GPvOxsGRw8+0LD2+XHEUP49oU+x+l+j1evz+++95HlfnxJvb41JKTJs2DUOHDjUlMStsRqMR48ePx5dffmkzkdyKFSvwxBNP5Ps51+v1GDp0KNLS0qzWE0KgS5cumDlzJkJDQ/NsXwgBlUqFgIAAhIWFoVWrVvjoo49w8uRJfP/991i6dGm+4sqPDh064Jtvvsn1sezsbFy9ehV//PEHpk6dilu3blk9jyKlxE8//YSuXbuiS5cuxe49WxKF+HvhqbYNsHDLUUgrWVTTMvVY8tcxvPpU6xLxvDOZXBk0atQorFq1ynTf09MTkZGRTu/Hzc0NgwYNQkbGfysqDxkyBHPnznV6X0SlndFoOUCtIHgxJm+Kojj1+Xb2a0dEREREREREREREJV+TJk1w9OhRzJgxAz///DPOnDmTZ9369esjPDwcI0eOhFardWGU/2natCmOHDmCtWvXYvr06YiMjDS7Bpib4OBg9O7dG+Hh4WjRooWLIi299Ho9/vjjD8yePRu7d+92+vUHJpMjIip8py/fwqEz123Wq1s5CK3qh7ogIiIiIrpfepYBGdkGi3JfdzcmeiUiIiIiohJJSolVq1ZhwYIFFnOJ/P39sWXLFrRo4bxkPkII1K5dG2vWrMEff/yB//3vf05pl4iIio9sg4Ix86McnqMqBPBWr8aoFOjp5MiIiMgZFOYgoBJEo1bh+xdb45Gxa2Fw4L0rpcDcyHN4/6kHUDXEuxAidNzRo0dx6dKlXB8TQqBjx45IS0vDgQMHcq1z5MgRXLhwAXXq1CnEKO8yGAwYPXo0fvrpJ6uJ5Ly8vLBy5Up07tw53+chpJSIiIhAVJT1359CCLz44ov45Zdf4ObmZlf8QghotVo0bdoUv//+Oz777DPMmjXLqUnDcmtLp9Ohdu3aGDduHF577TW8++67mD17ttXn0Gg0YtSoUTh+/Dg8Pfl7uqCEEHiz3yNYtO0oZN5PO6SUmPrnPrzc60FoNa5J0FgQTCZXxuzcuRMREREQQkBKCSEEfvrpJ7Rs2dLpfbVq1Qo//vgjRowYYSpbuHAh3njjDU4YISIiIiIiIiIiIiIiojLNzc0Nr732Gl577TWcPXsW//zzD65du4b09HR4enoiNDQULVu2RO3atQvUj7MWlVKr1XjyySfx5JNPwmAw4NixYzh9+jQSEhKQlJQEjUYDf39/BAUFoWnTpgWOm/6zd+9eDB8+3JR00NmLBpWEVfKIiEo6KSV+XLkHRsX6Z7iAwJvPtIVWrXJRZERERHSvjGwDDLl8X1cO8gIPnYiIiIiIqCRKTU3FG2+8YTER2c3NDcuWLXNqIrl7aTQaDBkyBK1bt3b6IklERFR0pJRYe+gKDl+6DUeHLoT4euCtno05VoGIqJi6e5mEn9FUcrSsGYxuLUKx5uBVh7bPyDZg3B+HMPf19sXm94mUEtOmTbM6VnTMmDFITEzEwIEDc61nNBoxd+5cfP7554W6XwaDAW+99RZ++eWXPJOgCSFMieQef/xxu+IxGo2YMGGCzURyTzzxhEOJ5HJrq0qVKvjkk08K1I69fQYEBGD69OkIDQ3F559/bjWh3KVLl7B06VK88MILxeY9W5I9UKsCHqhZAUfO3bBaLzomHv+cvY6HGlRxUWSOYzK5MubeDywhBHr37o2hQ4cWWn8vvvgiIiIisHbtWlMCu08++QRr1qwptD6JSiNnTwqivI0fPx7jx48v6jCIiIiIiIiIiIiIijeBsjde6P/3NywsDGq1GuHh4QgPD3dK03Xq1HHJ6n/OotFo0KJFCy4g5QJbtmxB7969kZ2dbbpe5MzBH7wGRUTkGklpmVgeeRyA9c9dXy8d+nXg5BkiIqKicjslK9fjpCAfXRFEQ0REREREVDBSSsyaNQvXrl0zKxdC4PXXX8djjz1WqOcihRCoU6eOS69H5dZXQfexMNq8t22j0YjU1FTcvHkTd+7cwa1bt2A0GuHv74/g4GCUL18e/v7+cHNzc/p1wvT0dMTFxeHWrVu4efMm9Ho9AgICEBwcjIoVK8Lf3x9qtbpQ3idSSuj1eiQmJuLmzZtISEjA7du3IaWEl5cXAgMD4ePjg+DgYPj4+JgSAjj7OVAUBcnJybhx4wZu376N+Ph4CCFQvnx5hISEoHz58vDy8oJKxUVgiAAg26Dgo0WHHE4kJwTw6YDm8PPUOjcwIiJyGqlICNga3UBUfKhUAlOGPoQtR2OQqc87AVdepASW7b2Ij59pjtoVfQshQvulpqYiIiIiz+PpwMBAPPzww9Dr9fD29kZKSopFHSkl5s+fj3HjxsHd3b1Q4jQYDHjjjTcwbdo0q8nPvLy8EBER4dB5iDNnzuD48eNW63h7e2P69OkFTiR3r6IYu6VWq/HRRx/hzJkzWLJkSZ6vv5QSkydPxuDBg6HV8nd1QalVKrzx9CN4cdIKq+ewFEXix+W7sWDcgGI/to/J5MqQy5cvY8eOHaakbm5ubvj+++8Lvd/vv/8emzZtgsFggJQSGzduxPXr11GpUqVC75uIiIiIiIiIiIiIiIjImaKiouDrWzwGjFDpFhMTg759+yIrKwtCCNPgg3sHK3h7e8PX15cDQoiIijEpJVbvPoXEtCyr9YQAnunYGAE+Hi6KjIiIiO6XkqHPdXZUtXLerg+mGMhJLFBUici9vLzMBuIXNB6VSgWNRgO1Wg2VSuXwIH8pJTIyMqxOilGr1XB3dy/2EwmIiIiIqHTLzMzEt99+a/Ebuly5cvjoo49c9nvVVf0YDAaMGTMGiYmJpjIfHx9MmjTJ4WtpiqJg3LhxuHXrlqnMw8MDkydPhk7neOJxo9GI06dPY+7cuVi3bh0uXryIrKz/EpxLKU3Pm0ajQVBQEFq2bImnn34aXbt2Rfny5R1KbialRGZmJjZu3Ijp06fjwIEDSEpKyrVfNzc3VKtWDU8//TRGjBiBGjVqOOW11Ov1iIqKwvTp0xEZGYnY2Fjo9XqzGHP6EUKY9r9evXro0qULevXqhbp160Kr1Tocj6IouHjxIubNm4cVK1bgwoULyMzMtKgnhICXlxfCwsIwcuRI9OjRA56enjzWozJLSom1h67i9PUkh9uoFuKN5zrU5v8REVExpnBxUiqBapX3wbPtamHO9rMOJb3N0isYv+QfzH+jQ7H4nbJt2zYkJCTk+pgQAt27d4eXlxcAoGPHjlizZk2uda9evYqDBw+ibdu2To8xISEBEyZMwPTp0/O8dieEgL+/P9avX4+HHnrI7udWSok///zT6jU5IQQGDx6MKlWq2NV2caVWq/HDDz9g48aNZuc37nf69GlcvnwZtWvXdl1wpZQQAk+2bQD/qTokpFqeG7jXmr2ncTs5HcF+Xi6KzjFMJleGzJs3z3QyLecDsVq1aoXeb82aNTFo0CDMnTsXwN2TbfPmzcOYMWMKvW8iIiIiIiIiIiIiIiIiopLoww8/RFpamlkSObVajWeffRbPPvsswsLCEBwcXMRREhGRLUZF4oflu20mPVEJFV7v+3CxGJRKRERUVsXcSQMELBLKBXrryuR3dHp6OurWrYuUlBSX963RaBAdHW123Jueno7atWsjLS3NoTZVKhW8vb0RFBSEBg0a4PHHH8djjz2GatWqQa1W57sdKSW6deuGw4cP51mnTZs22LBhQ5l83xARERFR8bF3715cu3bNrEwIgVGjRpXKxbOklFiyZInZPgcHB2PixIkFSia3bNkynDt3zlTm6+uLr776yqFkclJKHD58GO+88w527twJRVHyPHecU67X6xEbG4t169Zh/fr10Ol06NKlC5YsWQJ3d/d89200GrFy5Uq89957uHz5ss1+s7KyEB0dja+++gpTpkzBs88+i2+++QbBwcEOHetIKXHixAkMHz4cBw8ehJTSZgxSSmRnZ+PGjRu4ceMGduzYgXHjxuHhhx9GZGSkXcdyOe1dvXoV7777LiIiIqDX662eu5dSIiUlBX/99Re2b9+O0NBQ/Pjjj+jdu7dDyfyISrpsg4KP/zjkUIIW4O7CSpOeC4OHm33/u0RE5FqKRK7XSoiKMyEEPhvYAsv3XERKpsGhNlbsv4RPYpujTkU/J0dnH0VRMHXqVKvHKi+//LLpuGz48OFYu3ZtnvWnTp2KRx55xKnXrDIzM/HUU0/hn3/+sZpILiAgAJs2bULLli0dPo7ctGmT1edCCIGRI0eWqmtyISEhGDBgAGbMmJHnviuKgh07dhQomVxmZqbVRH25ycjIcLi/4szPyx39OjbBb+sOWn2/pWXqsXzHvxjZy/7kiK7EZHJlyPr16wH8tzrDSy+95LK+R44ciblz55r+GdasWcNkckREREREREREREREREREucjKysKyZcvMEslVrFgRa9euRfPmzYs4OiIiskf0tXgcu3DTZr1mtcqjQbVyLoiIiIiI8pKQmp3rZNjQoOK9snhhSk1NLbJkcvcP1JdSIjU1FampqQ63m5SUhJiYGBw7dgxLly6FRqNB06ZN8cknn6Br167QaPI3tD49Pd3q85Kenu5wjEREREREziClxNy5cy1+V2u1WgwdOrRYT3gtrbKzs/HJJ5/g22+/tZnELC9SSmRmZmLTpk3IyMjIdzK59PR0jBgxAkuWLLF7onhOQrd58+Zh8+bNWLJkCdq2bWvXe0hKiWXLlmHYsGHIyMhwaN9z2pFS4saNG3a3oSgKFixYgNdeew2pqal2b5+TiO7pp5/Gyy+/jO+//96hhIJEJZWUEuv+uYrT15McbqNhZX/0DqvK7yAiomJOkZKJ5KhEqhTgiZFd6mPKmuMOJb/N0iv4dMlhzH+jQ5H+Xrlx4wZ27dqV5+Ply5dHy5YtTfc7duwIX19fJCVZ/k6TUmLDhg1ISkqCv7+/02LMzs7GoUOH8nw8J5Hcli1b0Lx5c4efz+zsbERHR1utExQUhHr16jnUfnElhMDzzz+PGTNm5FlHSomdO3fixRdfdPj5/frrr7F06VK7trl50/YYuJJICIHX+rTB7+sPwWgj6fzPf+7B8O5h0GqKb5JsJpMrI/R6PY4cOQIhBKSUqFSpElq3bu2y/lu3bo3KlSvj+vXrphU0jEaj3as/EBERERERERERERERUdETahWEumyttJ2zv2FhYVCr1QgPD0d4eHgRR0Wl1a5du5CRkWG6vqtWq7F69WomkiMiKmGklPg1Yr/NyXlCAG/0awu1ipNniIiIioqUElfiLZOUCQH4e7oVQURU2KSU0Ov1OHjwIJ588kk88cQTmD9/PoKCgoo6NCIiIiKiAsvOzsZff/1lUV6/fn2EhoYWQURlW3p6Op555hls2LDBIolZzqRvb29vVKlSBVWrVoW7uztSUlIQExODmJgYpKWlmRKpOdJ33759sXnz5lz71mq1qFq1KmrVqgWdTocbN27g/PnzSEhIMKsvpURsbCy6du2KiIgIPP744/masC6lxL59+/D8888jKyvLon+NRoMKFSqgatWqqFixIrRaLYxGI5KSknD16lXExMQgJSXF4f0H7iaSmzx5MsaOHQuj0WgRgxACwcHBqFKlCipWrAgAuHr1Ki5evGjq+962pk+fjvj4eCxatAhardahmIhKEiklMrKN+PiPQw4lZgEAlUrgy8Gt4FaMEy4QEdFdiiIBIQHJ8QtUsgghMPrJJpi55QySMvQOtbFi/yV8erMFalXwdXJ0+ZOTiPv+Y6ccQgj06dPHLLG4r68vHn/8caxYsSLXbZKTk7Fx40YMHDiwUGLOLcbAwEBs2bIFzZo1K1BivtTUVCQmJlqtU7t27Xwl+nb0eBJAkSQXrFevHnQ6HTIzM/OsEx0dDSmlw/HFx8cjPj7e0RBLnQbVyuGBmuVx+NwNq/XOXLmDU5dv4YFaFV0Umf2YTK6MOHLkCLKyskwntzp06ODyGNq3b48//vgDAJCVlYWjR4+iRYsWLo+DiIiIiIiIiIiIiIiIyFFRUVHw9S2agSJUdly6dMn0txAC3bt3N1tNkoiISobUjGws+euozUW7/b3c0fPh+kW6sjEREREBCanZuZaX83PPtZxKD0VRsHHjRrRt2xbbt29HhQoVijokIiIiIqICiYuLw40b5pNfhRDo0qULVKqytWhYUdPr9Rg0aJBFIrmcRGrdunXD22+/jWbNmsHb29vs9VEUBenp6bh48SLWrl2L+fPnIzo6Ot99G41GvPLKKxaJ5IQQ8Pb2xltvvYWXX34Z5cuXh1p9N8GTlBLp6enYs2cPPvzwQxw6dMi0bc5jzzzzDA4cOIC6devajCE7OxsjRoxAdvZ/x9xCCPj7++Odd97Bc889hwoVKkCj0VicI1cUBRkZGbh27Rq2bt2KefPm4ciRI/ne/5yYp06dig8++MBs4RchBHx9fTFs2DC89NJLqFGjBnQ6nSkGRVGQmpqKnTt3YuzYsTh+/LjZ87BixQp8/vnn+PTTT3lun0olKSUUCcTcTkPEgcuY9Vc0TsUkOdxe82pB6NqcyUyJiEqC1AwDAP6+oZIpxNcdrz7RCF+tOuJQEtwsg4Kv//wX0195uEh+5xsMBsycOTPPxGdCCAwdOtQsNiEEXnnlFaxcuTLX7XKOifr37++S8wGBgYHYunUrmjZtWuDnMC0tDQaDwWqd2rVr56uf8+fP47vvvrOrf5VKhQkTJhTJmHFvb2/4+flZTSYXGxsLo9HI8zxOolYJhPdpg5e++RPSymg/RZH4NWIvfnm7T7E9H8BkcmXE5cuXze43bNjQ5TE0btzY7P6FCxeYTI6IiIiIiIiIiIiIiIiI6D45q/3lrBrYqVOnIo6IiIgcse2fc7idkmG9kgD6dWgCPy8mqSEiIipqV2+nWpQJIeCp43DrskBKiTNnzqBXr17YuXMn3N35+4yIiIiISq7o6GizxFk5HnvssWI70bU0klJiypQpWL16tUUyt6pVq2LRokVo3bp1nhO/1Wo1fHx88MADD6BJkyZ49913ceTIEXzxxRc2X0cpJVauXImFCxda9F2rVi2sX78+10n3OYnmunTpgg4dOmD8+PGYNGmS2fspOTkZAwcOxN69e6HT6azGsWfPHpw6dcoUgxACVapUwfbt21GjRg2r+6FWq+Ht7Y369eujXr16GDVqFC5evIiFCxfm+328f/9+vPfeexaJ5Nq3b48FCxagcuXKubalVqvh5+eHnj174vHHH8cHH3yAH3/80dSOlBJff/01evfujVatWuUrFqLiLieB3PU7aYiIuox528/h3ysJ0BuMkAVIKqRSCXwzNAxaDZNcEBGVBMmZ2Q4l4SIqDoQQGP1UI8zcdgrxKVn2NyCBRbvO4+P+zRAa5OX8AG04ffo0zpw5k+fjoaGheOCBByzKW7dujcDAQNy+fTvX7Q4cOIArV66gevXqzgo1T7Vr10b9+s5ZUNNgMOSZWC9HfhO9xcbG4tdff7XZ3r00Gg0+/PDDIkkmp1ar4eVl/T2o1+vt2h+yTgiBp9o2xFu/rENKWt6fHxISK/4+iUmjesDH0/o5kaLCI68yIiEhAQBMHwRVqlRxeQyhoeZZ43NiIiIiIiIiIiIiIiIiIiKi/2g05okKKlasWESREBGRoxRF4odle2wO2lMLFcL7tuYETiIioiImASSn6y3KtSoBXw+t6wMih6hUKmi1WoubWq3O1+8tKSUOHTqEX375hZMviIiIiKhEO3nypEWZEAK1a9cugmjKrgsXLuDzzz+3SOZWu3Zt7NmzB23atMkzkdz9hBDQarUICwvDn3/+CT8/P6v109PTMXr0aIskaqGhodi+fTvq1Klj8zhJp9NhwoQJeOedd8zqSilx5MgRzJ8/3+qxk5QSq1atsij/7bffbCaSu58QAmq1GrVr18bHH3+cr+ctMzMTw4cPR1bWf5PAhRDo1asXNm7ciNDQ0HzF4O7ujsmTJ+N///ufWX29Xo+33noLRqMx3/tBVNxIKaEoEldvp2LqplNo88Ea1HltOd78fT8OXbiNbINSoERyQgAP1g5G2/rlnRg1EREVJkXhuXEq2fy93PBO7yZwdBhORrYekyKOufw6kZQSv/32W66J4YG7xzIDBgzINaG3l5cXunfvnufxjV6vx+LFi12yTwcOHMDQoUPNjsMKU36PqUui/CSRJ+cK8PFArza2kyHeTsnAtn/OuSgq+3GpvDLi/sRttjJQFoacPnP+aZhMjoiIiIiIiIiIiIiIqIRSC0Bdei/A50p99xpXWFgY1Go1wsPDER4eXsRBUWlVqVIls/vp6elFFAkRETnqWlwS9p66YrNew2rBaFitnAsiIiIiImsURSIuKdOiXKNWQatRF0FERU+n02HevHkwGAw26xqNRnz66ae5JqzIERQUhMmTJ8PHx8dme0II+Pr62hUvALz55psYM2aMWZmUEllZWTh+/Dh+/fVXrF+/3upEfyklvvnmG4wcObJIxlsTERERERWUlBLR0dEWk4q1Wi0CAwOLKKqyR0qJ999/HxkZGWblfn5+2LBhg8X1QHvYmiwvpcTSpUtx9epVs3KNRoMFCxagcuXKdvX1+eefIzIyEgcPHjS9r6SU+OKLLzBkyBC4u7vnuf2xY+ZJGPz9/fHwww8XaIGV/CYLX7JkCU6dOmXqPyeR3/z5863GnBu1Wo2vvvoKkZGROHr0KKSUkFJi7969OHz4MFq1auXQvhAVFUWRuJWUgTUHr2FuZDQOno+H3miElM5d/EgIgS8HtYKmrI0xIiIqwYxMJkclnBACI7vUw5TV/yI+xf6EZlIKzI08hw/7NUN5P49CiDB36enpWLJkSZ4JwoQQeO6553I9HhJC4MUXX8SCBQty3VZKiVmzZuHtt9+Gm5ubU+POra+lS5cCAObOnZtr8rv8ylksylrStMxMy+u7pYGiKDb3Lb+LaVH+CSHwWp82WPzXMVjL1SelxM9/7kPvhxtCpSp+rwGTyZUR9w96SElJcXkM9/fJFReIiIiIiIiIiIiIiIiopImKinJoMjORPVq2bAngv4kQ90/0ICKi4k1KiYVbj0Cvz3214BxCCIT3aQN1KV4ll4iIqKRQpERqpt6i3MdDA52mbH5XazQaPPnkk/mqK6XE6tWrrSaTK1++PJ577jmo1YWXnK9mzZoICQnJ9bEqVargiSeewIIFCzBixAjo9Zavd46bN2/in3/+Qbt27QorVCIiIiKiQnXnzh2LMjc3N7sTaJHjrl+/jnXr1plNehdCYOLEiahZs2ah9m00GvHDDz9Y9N2zZ0+0bdvW7snmOp0Ov/76K1q3bm2WcPzKlSvYu3cvHn300Vy3UxQFt27dMivz9/cvUDKB/DIYDJg0aZLFc/Dzzz/nK8l5bnQ6HSZNmoQnnnjCVKYoCn777Te0bNmSk/ip2JNSIjEtGxEHLuP3v6Jx6Hw8sgzKPUkS7n8PyzzK8+/B2sFo26C8w9sTEZHrMZkclQZ+nm54p3cTjF100GpCqLykZmZj+qbT+OiZZi77nb97926L46d71ahRA/Xq1cvz8bCwMJQrVw43b97M9fELFy7g2LFjLkmEnZNQTkqJefPmOXwMqNPpoFKpoCh5j7+6ePGio2EWa+np6UhISLBaJzg4uEDXXZs3b46ePXvatU1UVBQ2bdpkNcFfSde8TiVUr+CPC9etP/+7j1/CjTvJqBzs56LI8o/J5MqI+1fGs/YlUlji4uIA3P3gF0LA09PT5TEQERERERERERERERERERV3devWRcOGDXHq1CkAwJYtWzBu3LgijoqIiPJLb1Awa30UJKwPnPNw06BPu8acYEZERFQMZBsUpGYZLMo9ddpiuZo4OUalUuG5557D7t27MXPmTKsTHSIjIx1KskBEREREVBzkzOO7l0ajgaoAC1sYjcZc280Pf3//MpXITkqJiIgIZGVlmZVXrlwZzz//fKEfZ1y/fh0nTpwwKxNC4KOPPnL4PdCsWTO0atUK+/btM5VJKbFgwQJ07Ngx130SQlgkbrt9+zbS0tLg51e4k63PnDmD06dPm5XVq1cPjz76aIGe/3bt2qFSpUqIiYkBcPc52LBhA/R6Pdzc3AoUM1Fhi03MQOsxq3H1dhoKkiAuv1QqgS8Ht4JGXTYXaiAiKqmUUpwgiMoOIQRGdqmHKav/RXxKlu0N7iOlwM8bTuJ/PRrB36vwf+dLKTF16tQ8r1sJIfDss89Cq9Xm2Yanpyf69u2LadOm5dqOlBLTp093SiJslUoFf39/JCQk5BmzlBLLli2DlBLz5893KKGcr68vvL29kZiYmGeds2fPwmAwWH1ugLvnRAIDA83KDAYDkpOTi2VitIsXLyIjI8NqnRo1ahTotRw2bBhee+01u7bZv38/Nm3a5HCfJYGbRo3h3cIwbtZmqyP/srIVLNl2FG/1b1fsrifzCKyMKF/+bub2nDfgjRs3XB7D9evXc42JiIiIiIiIiIiIiIiIiIjMjR49GlJKSCmxe/duHDlypKhDIiKifDp2/gYuxSZarSMg8MSDdRHsx8UYiYiIigO9QUG2wWhRHuyjYzK5UkYIgddff93qoH4ppSnBOxERERFRSZTbhGN3d3eo1WqH27x16xaqV6+OKlWq2H3bsGFDQXanxJFSYvHixWYT0oUQeOGFF+Dh4VHo/e/btw9Go/kxbrVq1dCoUSOH21Sr1RgxYoTFsdS2bdtgMFgmZwfu7nO1atXMylJSUjB37txCnawvpcSaNWssnv9BgwZBo9EUqG2dTocOHTqYlV2/fh23b98uULtEhU1KiZlbzuDanXRYSyQnhISbRoVWNULwcN0KVuvaEuztjtZ1yjm8PRERFQ2jUvySKhE5ws/TDaO6NISjOZ5up2Zh4d/nXZJoLD4+Hlu3brVaJyAgAGfPnkV0dHSut7NnzyI4ODjP7XOSnqelpRU4Xi8vL2zatAlhYWE2r7ctX74cQ4YMsUi2nh/u7u6oUqWK1TrXr1/HzZs3bbb14IMP4tq1a2a3EydOwNfX1+64CltOIj5rhBBo27ZtgfoRQth9KwuEEBj0eFNotbbOoUn8vvEfGIyKS+KyB5PJlRE1atQw/S2lxPbt210eQ2RkpNmHw70xERERERERERERERERUQki1ICqjN3E3YvCYWFhaNiwIaZOnVrELwKVdkOHDkX79u0B3L3G+/LLL9tcaZCIiIqelBLTVu+DzbGkAvhf3zZlZqAdERFRcZeWZYDBaPkF7uOhLcCUWSquKlSoYDOBQFEs3E1ERERE5Cy5nXfU6/VQFMcnuEopYTAYHLoVpN+SKDMzEydPnrQof/rppwv9nLCUEjt37rRIeNChQwdotdoCtd2xY0eoVOZTkm/cuIHExMRc6wsh0KNHD7N9llLivffew/fff4/09PRCScwgpcSmTZss2u7cuXOBn38hBB566CGzMqPRiCtXrhSoXSJXyMg2QCK3/7m7CeRa1grCD8Pa4OzP/bBrYg/UquADkWv9/ElMz0JcMsc4EBGVNAqTyVEpIYRAePf68HV37DhISuCb1ceQqbdcjMqZpJT4888/bY4NHT16NOrXr2/19sUXX1ht4/bt207JNSSEQIMGDbB582Y8+OCDNhPKrVixAoMHD0ZmZqZd/ahUKtMY2rwYDAasWrXK5rGlSqWCu7u72S0wMLBASfcLS2pqKubMmWN1n4QQePTRRznurJBUKeePB+uF2qx35ko8Tl+Jc0FE9mEyuTKiSZMmZoMejh07htjYWJf1f/PmTRw9etR0X61W44EHHnBZ/0RERERERERERERERETOEBUVhZMnTyI8PLyoQ6EyYNmyZahduzaklDh06BB69eqFuLjiN/CAiIj+k5qRjVW7T9scpFg52BcPNbS+ei4RERG5TmqmHrmlNijv5+nyWMg1CiNhARERERFRcRESEmJRVtBkcpR/t2/fRlJSklmZp6cnatas6ZL+jx07ZlHWtm3bAk8yr1ixIvz8/MzK9Hq91WTcPXr0QEBAgFlZVlYW3nnnHdSvXx8ffPABjhw5Ykos54xjNb1ejzNnzpiVaTQaVKpUydSHIzdFUaAoCnx9fS36vHz5coHjJipMQgi82KkufN3dAEgIAWjVAi1qBuH7F1sj+ud+2DexN8K7NUCVYG/cSc3Eyv2XIAuwzEK2UcGqqCs8B0NEVIJIKWHMJZkcP8mppArxdceIzvXg6KHQ1fg0bDx8zblB3UdRFMyYMcPqbyZ7j12stfPzzz877dyAn58fNm7ciIceeshmQrmVK1di0KBByMrKynf7QgibSdmllPjuu+/sTlRXXEkpMXnyZJv5oCpVqoR69eq5KKqyR6USCO/b2uZ5FKOiYM6Gg8XumMf6kmpUanh4eKB58+aIiooylf32228YN26cS/qfOXMmpJSmf5RmzZrBw8PDJX0TEREREREREREREREREZVEISEh2L17N5566ins3bsX27dvR+PGjfH2229j2LBhKFeuXFGHSERE9/nr8HncSbG+WjAgMPjxZtBpOXSLiIiouEjN1AO5DPIu5+9eBNFQYYuJiYHRaLRap2LFii6KhoiIiIjI+e5P+AXcTbBlMBgcblMIYbrZUtwm0bpabGysxeT84OBgeHoWfsJyRVFw69YtszIhBOrWrVvgtnU6HYKDg3Hnzh2z8tjYWDzwwAO5bhMQEICvvvoKr7zyitlzIqXE1atXMWnSJHzzzTfw9/dHixYt0K1bN7Rt2xb16tWDj49Pvt9z90pPT0dCQoJZmcFgQL9+/RAQEGD1/ZlXUgWj0YikpCRIKXH9+nWzx6SUuH37tl0xEhWF2hV8cWTKU4g4cBne7lp0aVoZlYM8obrv/0xKiWmbTiM92/HvjLsNAXO3n8WoJxpAXbBclkRE5EJ6o+XvIX6MU0klhMDbvRpj2qbTSMty7LfNZ0sPo1erqtCoVU6O7q7z58/j6NGjhdJ2bnbu3ImbN2867TqYv78/Nm7ciG7dumHfvn15Hm9JKREREYFnn30WixYtgrt7/q7BPvjggyhfvrzV5GoXLlzA1KlT8c477xQ4iXpRklJizZo1+Oqrr6wetwoh8MILL0Cn07kwurKna1hd+HrqkJRmPVHhkr+O4YsRT8BDp3VRZLZxRGIZ0qdPH0RFRUEIASklvvnmG4wcOTLXlUacKT4+Ht98842pXyEE+vTpU6h9EhERERERERERERERERGVZC+++KLp75o1a+Lw4cPIyspCXFwcxo4di7Fjx6JOnTqoXbs2AgICoNU6NhBBCIFZs2Y5K2wiojJNSomZa6JsTpRUqwRe6NqiRA9gJCIiKm3iknIfBB7ko+N3dikjpcTUqVNtTsJo0KCBC6MiIiIiInKu6tWrW5RlZWUhKSkJvr6+DrUZHByMqCjr5z/j4+PxzDPPICkpyaE+Sovk5GSLsnLlykGtVhd63waDwSKRGgAEBQUVuG2VSoWKFSsiOjrarPzGjRt5biOEwPDhw3Hz5k18+umnFgkNpZSQUuLOnTvYunUrtm3bBgDw9vZG48aNMWDAAPTt2xeVK1eGSpW/5A2ZmZm59nPgwIF8be+IgiRqJHIVIQSql/PBGz0aWT3flZppwLTNp3Jbd8Fux67cQcztNFQN8S54Y0RE5BJxybYWzyMqWSr4e2DAIzUxe/sZSGnfNT8pgX+vJiLqXDza1HP+wr9SSsyePdvmAkjOlJmZieXLl+O1115z2jVQPz8/bNiwAd27d8fevXvzlVDujz/+yFdCOU9PT7z88sv4/PPPrbb70UcfoUWLFnj00UdL5LVdg8GA3377DW+//Tays7Ot1vX19cXrr79eIvezJPH1ckf3h+pi8fZ/rZ4Lu5GQhn+iY/BIk+quC84GJpMrQ4YMGYKPP/7Y9EWSmpqKkSNHYsWKFYX2ISGlxMiRI5GSkmLqQ6PR4Pnnny+U/oiIiIiIiIiIiIiIiMgF1Oq7t7Lk//c3LCwMarUa4eHhCA8PL+KgqDSbM2dOrtdxcxbxAoDo6GicPXvW4T5yFgNjMjkiIueIT0rHjqMXbNZrUqM8alcu+MQ9IiIicp7kDH2uE2QrB3q6PhgqNIqiYP78+ZgzZ47NBMAldbIJEREREZEQAnXr1jW7pgTc/T187do1VKlSxaF2tVotmjVrZrVOdnY2PD09y3wyufj4eIsyLy8vlxxjSCktEhEIIaDROGcq8f2T/aWU0Ov1VrdRqVQYO3Ys2rRpg7feegvHjx83bXu/nLKUlBTs3bsX+/btw3vvvYfHHnsMX3zxBVq0sL1QS3Jyss1jPqKyzNr/kJQSK/dfQlxyllP6MhglVh24jNe6N+R5FiKiEiLboBR1CEROJYTAB30fwMK/zyHLgfe3lBITVx5DxJhOUDn590xWVhYWLFjg0uMXKSVmzJiBUaNGOe04ETBPKLdnzx6rid9WrVqF3r17Y+XKlfD2tp50WAiBN954A9OmTcOtW7fyrJeVlYUnn3wSM2bMQP/+/V2SzL0gcp6fzMxMHDhwAB9//DF27doFRbH+HhVC4OOPP0a5cs5PbkjmhBB45cmHsHj7v9YrSmDaqv14uHG1YnPMw2RyZUhoaCgGDBiAhQsXmk4Gr1q1CiNHjsSMGTMKpc9Ro0bhzz//NPUnhMCAAQNQuXLlQumPiIiIiIiIiIiIiIiIqDBFRUXB19e3qMOgMiRn0Mi9gwzuH3DgyGCi4jJogYiotJBSYt2+00jPMlivKARGPvkQ1Cp+DhMRERUnV+JTLcqEAPw83YogGnLUuXPncp1IkpGRgePHj+PXX3/Fxo0bLRIr3K98+fJo3rx5YYVJRERERFToGjVqlGv5wYMH0aZNGxdHU/bkdh3OlckBCvM6oKP7oVKp0KlTJxw6dAi7d+/GL7/8gu3bt+POnTuQUlpNNJCdnY1NmzZh27ZtGDVqFCZNmgSdTmc1xvvbE0LA39+/0BIKeHh4FEq7RK6mN0pM+vNYrosu5EfOx0/O9lICv/91DqO6NoBGzWtjREQlgcHIpLxU+tQs74Oerapg5f7Ldv/OkRLYfPQarsSlono5H6fGdfDgQcTExOT5uFqtRt26de0+3khPT0d0dHSeiclOnTqFM2fO5HnuwFG+vr5Yv349evTogd27d1s9ztu6dSt69+6N1atX20woFxAQgClTpuCFF17Ic5+klEhNTcWQIUOwaNEifPrpp2jcuDG0Wm2e420TEhJgMNgY51VAp0+fxs6dO6HVaiGlREJCAlJTU3Hp0iXs378fe/fuxc2bN83iyosQAp06dcJrr73G8b8u0qpeKCoF+SAmPjnPOhISG6KikZqRDR/PvM9VuBKTyZUxEydOREREBNLT000J3mbNmoXY2FjMmDEDFSpUcEo/t27dwssvv4w1a9aYfQh5enriyy+/dEofRERERERERERERERERERlhbMnmLhywgoRUVmgSImZa6Jsfr56aDV48pEGHNRHRERUjEgpkZyuz/WxCv6eLo6GCuLHH3/EL7/8YlYmpYTRaLSanOBeQgiMHj0anp587YmIiIio5KpWrRp8fHyQnPzfZFcpJTZt2sRJxy5Qvnx5i7KkpCRIKQv9uVepVNBozKcNSymh1+d+3GsPKSXS0tLMyoQQcHd3z3cbWq0WHTt2RIcOHZCRkYHz589j165d2LhxIw4dOoTY2FgoimJx/JazDz/99BMuXLiAFStWwM0t9wTwXl5eprmzOYKCgnD8+HH4+fnZscf5d/9zTlRS7Y++hTM38k6UYI0QQPMaQThxNQFZ+v+SfJy4dgdXbqWjZkXrSUKIiKh4MOSRqImoJBNCYGzfpog4cAVGB8ZN6o0Kvl93At8Nfchpx3RSSotrWvdr06YNtm3bZvfxRnZ2Nh588EH8+++/uT6uKApmzZqFKVOmOP0Y1dfXF+vWrUPPnj2xa9cuqwnlIiMj85VQTgiBZ599Fn/99RfmzJlj9XqfoihYt24d1q9fjypVqqBt27Zo0aIFQkJC4ObmhuTkZMTHx+PAgQPYs2cPUlJSCrzP1hw6dAgdOnQocDtCCDRs2BCLFy/O81iYnM/dTYNnOjbGDyv2Wn3fJaRkYs+Jy3girK4Lo8sbz1CUMaGhoZg8eTJGjRoFIYTppNi6devQuHFjfPTRR3jhhRfg7+/vUPtJSUmYO3cuPv/8c9OqEDl9CCEwefJkhIaGOneniIiIiIiIiIiIiIiIiIhKmfbt23MiDxFRCRITl4xD0XmvFpzj4cZVUc6fk2WIiIiKm6tx6bmWe+rULo6ECkJRFGRnZzu8vRACzZs3R3h4OI/JiYiIiKhE8/LywgMPPIBdu3aZle/evRvJycmFllCrNFIcSKbh6+trUXb79m0YDIZCn/StVqsRGBiIGzdumJXHxsaiUaNGBWpbURTExsZalFeuXNnutoQQ8PT0RJMmTdCkSRO88soryM7Oxo0bN7Bt2zYsWrQIe/bsQVZWltmE7Zy5sL/88gveeOONXI/dPDw8oNFozI4Ps7Ky4OnpaVfiO6KyRpESX/15DFJxbGE6L50Gi998FMN/3Ymdp26ayo1GieX7L+DdJ5vwfAsRUQlgdPB7gKi4e6B6EB6sHYK90bfs3lZKYMGO8/i0fwv4eTnnmC4xMRHr1q3LM0GVEAKvvPKKQ8eQOp0OQ4cOxejRo3NtX0qJP/74A1988UWhLK6Uk1CuR48e+Uoo16tXL6xZs8ZqQjm1Wo2pU6fi2rVr2Lp1q9XEXjkLTF2+fBmXL1/GwoULC7xPRUkIgaZNm2LTpk0ICgoq6nDKFCEEnn+iBX5cuRfW8lBKKTFj9QF0aVWnWBzzMJlcGTRy5EgcPHgQs2bNMksod+fOHbz99tsYO3Ys+vTpg8ceewxhYWFo1KgRVCpVrm1JKXHixAlERUXhr7/+wsqVK5GZmWn64M15kwsh8OKLL2LkyJEu208iIiIiIiIiIiIiIiIiopIqMjKyqEMgIqJ8klJi5c4TyDYardYTQuCV3g9BpSr6QWNERERkLjnTMgGZEAIB3roiiIaKghACdevWxdq1a5lcgIiIiIhKPCEEnnvuOezevdtsgnVSUhIiIyPx5JNPFmF0hef+CbvWJpfnh16vR3Jyst3bVaxYEWq1GgaDwVR269YtpKSkFPrEb5VKhcqVK+PEiROmMiklTp06hU6dOhWo7czMTMTFxVmUV6xYsUDtAndfO51Oh+rVq2P48OEYNmwYYmNj8fXXX2PatGlmieGklPjyyy/x0ksvwcvLy6ItLy8v+Pr6Ij4+3lSWlpaGW7duwcfHp8CxEpVWl2+lYNu/1+HQJ6eQGNSuFmpV9MHQx+pg1+lYSHn3M1kCmL/jPN7u1RgaNa+REREVd0Yjk8lR6aRWCXzcvxm6T9hsNSlUXhLSsrBi3yUMe8w5yaI2bNiAlJSUPB/38PDAE0884VDbQgj069cPH3zwQZ6LMN28eRO7d+9G586dHerDFh8fH6xbtw69evXC33//bTWh3I4dO9CzZ0+sWbPG6jGbh4cHIiIiMHToUCxfvrzAx/wlgVqtxsCBAzFt2jSryfao8DSsXh61KgbibMxtq/W2/nMOiamZCPDxcFFkecs9QxiVejNmzMDgwYPNkr7lJJXLyMjAH3/8gZdeegnNmjWDt7c3qlSpgsaNG+ORRx5B27Zt0bhxY1SpUgVeXl5o2rQpRowYgUWLFiEjIwNSSlN7wN0P78GDB2PGjBlFuctERERERERERERERETkLGpV2bwBCAsLQ8OGDTF16tQifhGIiIiouFAUiXmb/oGt2TW+njp0alHLNUERERFRvkkAt5IzLMrVKgGdRu36gMjlVCoVunTpgl27djklCQIRERERUVETQqBXr14WiZKllJg4caJZkrPSRKUyny5rMBhgtLEIiDWpqakOJZMLCAhAcHCwWVl2djZOnTrlcCz2aN68uUVyA2uT9/Pr6tWrSE1NNStzd3dHuXLlCtRublQqFSpVqoTvv/8es2fPhlptfnweHx+PI0eO5LqtTqdDnTp1zMqklNi7d6/T4yQqLaSUmLrplM2Fk/KiVanwdq/GEEKgW/NQ6LQas8fPXE/EpbjUPLYmIqLixKBY/mYs/emaqKzo2KgiapX3dWhbKYEpa47D4ISEi4qi4JdffrF6jNa+fXsEBgY63EflypXRokULq3WmTZtWqAnZfHx8sGbNGrRv395qAj4pJf7++2/07NnTaoI9APD09MSiRYvw/fffw9vb2ymJ/e4nhIBKpULjxo3h6enp9Pbz078QAvXr18e6deswb948JpIrQlq1CoM7N4Ott1pqhh7bj1xwTVA2MJlcGSWEwPz58/HJJ5+YfTjem1Qu55aZmYmYmBicPHkS+/btw969e3Hy5EnExMQgMzPTrO79SeSEEBg/fjzmzZtXKB/CRERERERERERERERERK4UFRWFkydPIjw8vKhDISIiomLiwo07OH7xptU6Qgg8EVYHft7uVusRERGR60kJJKZmW5S7qVTwcNPksgWVBkIIaLVaPPjgg4iIiMDatWstkj0QEREREZVkFSpUwJNPPmkxpy8qKgqbN28u1AnjRUGtVsPPz8+sLC0tzeZEdGuOHz8OvV5v93Zubm546KGHzMqklFi4cGGhP+9CCLRv396ifNeuXcjMzHS4XSklNmzYYBF/9erV4evrWDKG/BBCYMCAAWjTpo1FPP/880+e23Tu3NnsvS+lxIIFC6AoSqHFSlSSpWYaMG/7OUDaPw9cCKBDw4qoXeHuZ0E5Xw+E1TY/x2JUJFbsvVTqvnuIiEojo5G/l6j0ctOo8E7vRjaTQuXlzPUkRJ2LL3AcV69eRVRUVJ6PCyEwatQoi4Tp9lCpVHjxxRfzzPMjpcTmzZtx+/Zth/vIDx8fH6xduxYdOnSwmVBu586d6NGjh83jeI1Gg9dffx3Hjx/HoEGD4O7u7pR8RkII+Pr64plnnsHOnTtx4MAB+Pj4FLjd/PYthIC/vz969uyJzZs348iRI+jSpUuB3gdUcEIIDOzUFCph/XWQUuL3dVHF4piH75gy7pNPPsGuXbvQuHFjU0I44L8PmntvAOyq06RJE+zatQsff/xx0ewcEREREREREREREREREREREVEhklJi6fZj+RpQPbxHGBdjJCIiKoYUKZGeZbAod9ep4abhd3dJotVq4ePjk+vNz88PVapUQbNmzTB48GDMmDEDZ86cwZ49e9CrVy9oNEwcSERERESlixACn376KbRarVm5oigYOXIk7ty5U0SRFQ4hBKpVq2ZWZjQacf78eYfak1Ji/vz5DscyaNAgi/PBS5cuRUJCgkNt2iMsLAzu7uYLm8TGxuLAgQMOt2kwGDBr1iyzSdFCCPTs2RNqtdrhdvNDpVLhqaeesii/detWrpO0hRDo27evxfMfGRmJS5cuFVKURCWXlBJroq7idmqWQ9sLIfBhv6ZQqe7+z6lUAsM61jNL0iIlMH/HORiVok+sQERE1t1Otlx8h1dKqLQQQmBg21oI8NY5tL2iSExZ82+BkkVJKbFs2TJoNBp4enrmeitfvjw6dOjgcB/Af8drfn5+efajKAo2bNhgsa212Dw9PeHh4WFXLN7e3lizZg06depks91Dhw7h6aefRmpqqs39q1atGubPn4/o6Gh89tlnaNy4Mdzc3MzyH1nbXggBNzc31KxZE8OGDcOaNWtw+fJlLF68GA8//DC0Wq3Ndjw8PKzuk6enJ7y8vODl5QVvb2/4+vqiQoUKqFmzJpo1a4YePXrg7bffxuzZs3H48GFcuXIFq1atwuOPPw6dTufwODMhBLy8vKzG5ci1UbVabXOfS+PYuJoVAlAvNNBmvb+PXUJCquOJ/J2FV70JrVu3xtGjR7F48WL8/PPP2LNnj+mxe/9JbWX5zNGmTRv873//Q//+/UvlPzkREREREREREREREREREREREQAYjAoWbT0KW8NEA3zc0aZhFZfERERERPYxGBUkZVhOjnLXqLnSewnz3nvvYcyYMbk+ljMhRKVSQaVScYwzEREREZUJderUwQsvvIDffvvNbP5fTEwMBgwYgNWrV8PT07MII3SuFi1aYO3atWb7GhERgYcfftjuY4Dr169j6dKlDicJ6NKlC/z9/c2SxyUkJOC7777DZ599VqjHJEFBQWjdujW2b99uVv7ZZ59h8+bNdid/k1Ji+/btOH36tFm5EAKDBw8u9OMrIQS8vb1zLc9Lo0aN0KRJExw9etRUlp2djffeew9Lly7l8T7RPYyKxHdr/4WjOVHqVvRFm3rlzMq6tQiFu1aDjOz/FnA4cz0Jl+JSUbuCb0HCJSKiQiQBZBuNRR0GUaHy9dBiaMc6+G7tcYd+/2w8HIPYxAxUDHD8WPrVV1/Fyy+/nOfjKpUKXl5eDrefo0KFCrhy5YrV49r7E9ADwIgRI/Dcc8/luY0Qwu5zCd7e3li/fj0yMjLyVd/NzS1f9YQQqFKlCj788EOMGTMGt2/fxtmzZ3H8+HFER0fj1q1byM7OhpQSWq0Wnp6eqF69OmrUqIF69eqhatWqCAgIgEajsfvYVqPR4ODBg/k6b5CTvE4IAY1GA7VabXa90tnH1QEBATaTqd+fhD4/WrRogRs3blitk9vxe0mnVqswrHsrvDd9k9XXOy1Tj8gjF9C3XSMXRmeJyeTIZODAgRg4cCDOnz+PtWvXYseOHabMldbezFWrVkXz5s3RoUMH9OzZE7Vr13Zh1ERERERERERERERERORyKtXdW1ny//sbFhYGtVqN8PBwhIeHF3FQREREVNQu3khA9LXbNut1C6sLL4/8DXQkIiIi1zIaJfR6xaI80EcHtYoJx0qSihUrlsoJCkREREREjhJCYNKkSdiyZQsuX75smiMopcRff/2FJ598EsuXL4evr2+JT7gshECnTp3w2WefmcqklFi0aBE++eQTu44VDAYD/ve//yE9Pd3hePz8/DB8+HBMmTLF7HmfPHkyunbt6lCCu/xSqVQYPXo0IiMjzfqOjIzEqlWr0KdPH7v6Tk1NRXh4OBTF/Nj5gQceQKNGeU+Qzum7oPsppTRLCpfTZq1atfJsW61WY/z48ejbt6/ZcxAREYEFCxbgueeec9rzL6Us8f8/VLadjknCkUt3HNpWCODt3o2hVZuPIQrx0+GR+iHYeuy/RBOKlNjwzzW83r1hgeIlIqLCZTBaXi8hKk2EEAjv1gA/bziJbIP97/dMvQFz/jqHMX2bOHQc4EgiNkcJIeDj42P3djqdDjqdzunxaLXaXJPXOUNOkrby5cujfPnyaNu2rdU8Sc44hnP0+XUFIQR8fZ2fxFmtVhdKu8WdEAJPd2iCsTM3Q2/M+30lpcTs9QfRp23DIj1PUMZmeFB+1KpVC2+88QZWrlyJixcvIiMjA+fPn8fBgwexc+dO7Ny5EwcPHsT58+eRmZmJS5cu4c8//8Sbb77JRHJERERERERERERERERUqkVFReHkyZNMJEfFQmJiIs6fP48DBw7g77//xt9//13UIRERlSlSSiyLPAbFxgqzQgi83PtBTiYjIiIqptKzDcg0GC3KvXQa8OubiIiIiIhKOj8/P6xYsQJeXl5m5VJKbNu2DS1btsTOnTstEoXZS0qJ69evFygBW0E1a9YM/v7+ZmU3btzAxIkTrU4iv5fRaMRnn32GiIiIfG+TGyEEPvjgA5QrV86sPCsrC3379sWRI0ccal9KiYyMDBiNlsex93r88cfxwAMPmJ2XVhQFI0aMwOnTp/Pdd1ZWFoYNG4bz58+blatUKkycOBEajSbPbTMyMrBgwQKkpKQU6Lm8efMmlixZYtH/gw8+mOc2Qgj06NEDjzzyiNlzYDQaMWrUKCxfvrxA73kpJbKzs02JGolKKiklft5w0ua1rrwEeOnQr3UNi2tgKiEwvFM9s3NrEhIpGfoCfR4QEVHhM/JzmsqAaiHe6NiwkkPXAaUEZm497VAiOnItIUSeNyJ7VQnxQ6Ma5W3W+/vYRSSmZrogorwxmRzZ5Obmhho1aqBFixZ45JFH8Mgjj6BFixaoUaMG3Ny4YjIRERERERERERERERERkStIKbFixQo8//zzqFGjBoKCglC3bl20adMGjz76KB577DGr2x85cgR79uwx3WJjY10UORFR6WRUFCzaZnvCXzl/L7SsG+qiqIiIiMheWXojlFy+zisFeoJTCYiIiIiIqKQTQqB58+ZYuHAh3N3dzR6TUuL8+fPo1KkTunfvjh07diA9PT3fiX6klNDr9Th37hzee+89NG/eHMnJyYWxG/ni7e2N/v37m00Ml1Lim2++wU8//QSDwZDntlJK3L59G8OHD8eECROgKAo8PDysJkuzJSAgANOnT4darTbrJy4uDh07dsTMmTORlZWVr+dbSomEhAT89ttvaNmypc3n2c3NDdOmTYNWqzUrT0xMRMeOHbFlyxarydSklIiPj0f//v2xcuVKsxiFEOjduzc6d+5sNQaDwYBXXnkFderUwUcffYTz58/DYDDY9f6KiYnBU089hYSEBLP+mzVrhjp16ljdXqvVYt68eQgMDDQrT09Px+DBg/Haa68hPj7erngURUFsbCx+++03NG/eHF27djWLjaikSc7QY9mei3Akb5AAMLhdLfh5anN9vHuLKqge4gMhACEk/D10GNSuJpN3EBEVZxIw5nbBhKiUUQmB9/s0cfh3yZXbadh/Ns7JURFRcaZSCTzXubnNz43UDD12Hj1vtU5hc/xMFhEREREREREREREREREREbnE4sWL8dFHH+HChQsA4NBq3QsWLMB3331nut+vXz8sWbLEaTESEZU1l2MTEH31jtU6AgK9Hq4PDx2HaRERERVXyRn6XI+xfDxynwhLRERERERU0ggh0KtXL6xcuRKDBg1CUlKS2XGQwWDA5s2bsXnzZoSEhKBNmzZo3749GjdujEqVKsHT0xNCCEgpkZ6ejps3b+Ls2bM4cOAA/v77b1y+fBlGo9Hi2EoIAQ8PD1SqVMll+/nhhx9i4cKFSE1NNZXr9Xq89dZbWL58Od599120atUK3t7eAICMjAxcuHABy5Ytw5w5c5CQkAApJXQ6HX799VeMHz8ely5dcjieXr164cMPP8QXX3xhSt4mpURycjJeeeUVTJkyBSNHjkSPHj1QsWJFeHh4QAgBRVGQmZmJuLg4REVFYdmyZdi2bRuSk5Ph5uaWr/4feughfPTRR/jkk0/M+o6Li0P37t3RvXt3jB49Gk2aNIGXlxdUKhWys7MRGxuLJUuW4Pvvv0dcXJxFIrmqVati5syZZknyrLl58ya+/PJLTJo0CfXq1cOTTz6Jzp07o27duvD19YVOpzO9v3LeY5cuXcKiRYswY8YMi/erRqPBd999l69Ef9WrV8eyZcvQq1cvs0SJer0e06ZNw6JFi/D0009jyJAhaNiwIXx8fKDVak2vQXZ2NlJSUnDhwgX8/fffWLNmDY4cOYLMzExIKZkUi0q8tQevIDE926FtNWqB8G4N8vw/8HbX4MDXvTB/x3mkZ+kxqF1tVAvxKki4RETkAkaj5fUSR8apERV3D9cvh2rB3rh4K8XubRVF4od1x9GuQXkeExCVEUII9GnfCGNmbIQ+l+/Key3cegy9HmlUZJ8PHKVIRERERERERERERERERPZRq+/eypL/39+wsDCo1WqEh4cjPDy8iIOisiArKwvh4eGYPXu2aWCeEMJikEF+Bu29/fbbmDp1KrKysgAAq1evxp07dxAYGOj8wImISjkpJdbsPQ1FKtYrCuCFJ1py8CgREVExlpiWDQkJwPz7ulKgZ9EEREREREREVAiEEOjatSv27duHZ599FkeOHDG7vpTz961bt7Bq1SqsXr3abNv7693/9/19CSHQvn17/Prrr6hXr56zdydPoaGhmDBhAt566y1TAjUAUBQFu3btwq5du6DRaKDT6QDcvRZnMBgA/Lc/arUakydPxsCBA/HFF18UKB6VSoWPP/4Yer0ekyZNgtFoND0mpUR0dDRGjx6Nd999F56envD394dGo0FWVhZSUlJMCdAcSeAhhMAHH3yA+Ph4/PTTT2YJ5YxGI9auXYu1a9fC3d0dQUFB0Gg0SE5ORlJSEhRFyTU5YKVKlbB582YEBQXZFYuUEnq9HsePH8eJEyfw5ZdfQqPRwMvLC/7+/qYEeRkZGbhz5w4yMjJy3W+1Wo3PP/8cbdu2zfdz0LFjR6xduxbPPPMMbt++bWpTSomkpCTMnj0bs2fPhkajgbe3N3x8fKBSqaDX65GcnIzMzEyL9whRaWBUJH7ecBKOvq0fql0etSr45vm4EAJBPu54o0dD030iIireJO5+PxCVBW4aFV7v3gDvzD3g0O+hjYev41ZSJsr7ezg/OCIqlkJD/FAnNBAnL8fnWUdKia3/nENqRjZ8PHUujO4/qiLplYiIiIiIiIiIiIiIiIioBIqKisLJkyeZSI5cQkqJZ5991pRILmfiTc7ECXsnjlSqVAl9+/Y13c/OzsbKlSsLI3QiolJPkRILNh+xOaA0xM8TzetUdE1QRERE5JCUDD1ym8oa7KPjJFciIiIiIipVhBCoV68e9uzZg0mTJiEwMDDP4557r0UpimK6WbtGJYSAWq1GWFgYNm/ejC1btqB+/fouPbYSQiA8PBxvv/02VCrz6bM5cev1eqSmpiI1NRV6vd5sf3Q6HX744Qe8+uqrUDtpgTW1Wo0vvvgC8+bNy/U5z3mOU1NTce3aNVy6dAk3btxAampqrknd7O17ypQp+Oqrr6DT6SwSA0opkZGRYer3zp07MBqNuSaSCwsLw549e1CnTp0Cvab3vg6JiYm4dOkSoqOjER0djatXryItLc1iv4UQ8PHxwS+//IJ3333Xrv6FEOjQoQMOHjyI9u3bW31fJCQk4MqVK7h06RJiYmKQkpJi8R65t12tVmtKhEdU0pyPTcY/F247tK0QwLtPNYZaZft/MbfF8oiIqHhSFInkjGyLcn6OU2kkhMCgdrXg6aZxaPtMvQEr911iwmmiMkStEni2U7NcxxbcKzE1C1Gnr7kkptwwmRwREREREREREREREREREVExNG7cOERERACAKYmcTqfDyy+/jD///BNHjhxBgwYN7Gpz4MCBpvYAYNOmTU6NmYiorLgen4zjF2/arNe5VS146LQuiIiIiIgcdT0hDbhvyLcQQIC3e9EEREREREREVMjc3d3xzjvv4OzZs/j+++9Rr149qNVqh5JECCGgUqlQvnx5vPTSSzh06BB2796NTp06QaNxbFJ+QanVanz99deYOXMmgoOD87VfQgg0bNgQO3bswKuvvmqRcKygVCoVnn32WZw4cQIjRoyAt7e33QnRVCoV6tSpg0mTJsHHxyff26rVaowePRqHDh1Cly5doNVq8923EAIVK1bEjz/+iL///htVq1bN97ZeXl748ccf8dhjj8HHx8ehhFJCCLi7u6Nfv344evQoXnrpJYdeGyEEqlWrhq1btyIiIgItW7Z06D2fk0Cufv36GD9+PE6dOmX39Vqi4kBKiVnbzsCgKA5tX97PA50eqOTkqIiIqKhJSBiMjn03EJVEIb7u6NEyFI7kS5QS+HnjKRgUJpMjKiuEEOjXsUm+zkss3HqkyJJNFs3ZOCIiIiIiIiIiIiIiIiIiIsrTpUuXMGXKFFMSOQB46KGHsHz5clSuXNlUz96V7rt27QovLy+kp6dDSom//vrLqXETEZUFUkps3H8GehuDqIUQeL5rK67STUREVMylZRiQ2zjuCv4erg+GiIiIiIjIRYQQCAwMxOuvv45Ro0bh4sWL2Lp1K7Zt24Zjx44hLi4OGRkZMBgMZttpNBp4enqiXLlyaNiwITp06IBHH30UderUgYeHR4HOh9apUwcLFy40K2vYsKFDbalUKgwbNgx9+vTBwoULsWjRIpw6dQppaWkwGo2mfQkMDERYWBheeeUVdOrUCTqdzqyN7777DikpKaYyjUZjVsceQghUqFAB06dPx5dffomNGzdi+fLl+OeffxAfH4/s7GzTdUGVSgWtVougoCA0bNgQjz/+OLp37446derYlQzu3r4bNWqE9evXIzo6Gn/88QfWr1+P8+fPIzU11fScCCHg5uaGkJAQtG7dGoMGDUKnTp3g5eVld59qtRrDhw/HsGHDkJqairNnz2Lnzp3YuXMnTp8+jRs3biAtLc3sPaZSqeDm5obg4GA0adIEvXr1Qs+ePVGxYkWnJPjTaDTo1asXunXrhujoaERERGDTpk2Ijo5GQkICDAaD6TUQQkCj0cDX1xehoaFo1aoVHnvsMbRp0waVKlWCRqPh+X8qsTL1RizceQFSOpJEFBj6aB14uKkLITIiIipKUgIKE2NRGSKEwBs9GmP5vssOJX2Kvp6EU1cT8UD1wEKIjoiKo5oVA1GzYgDOxtzOs87dsYXRyMw2FMkitEwmR0RERERERERERERERET2Uanu3sqS/9/fsLAwqNVqhIeHIzw8vIiDotJswoQJyM7OhhACQgg0adIE27dvh7u7e4Ha1Wq1aNGiBXbu3AkASExMxLVr1xAaGuqMsImIygQpgUXbjtocSOrvrUPrBlVcFBURERE5QkqJK/FpuT7m7c5h1kREREREVPoJIaDValG3bl3UrVsXo0aNgtFoRFZWFjIyMpCWloasrCwAgKenJzw8PODh4QE3Nzeo1WpTG84QEhKCZ5991iltAXfjCggIQHh4OF599VVkZGQgJSUFycnJUKlU8PPzg4+PD3Q6Xa77oFKp0LNnT6fFc29cwcHBGDJkCAYPHgy9Xo+0tDSkpaUhPT0dQgh4eXnB09MTnp6e0Gq1pu0KSqVSoX79+hg/fjw++eQTZGZmIjU1FcnJyVAUBR4eHvDx8YGXl5fTkqWpVCr4+vqiZcuWaNmyJd544w0oimJ6j6WmpiIrK8u0315eXvDw8HDqft9Po9GgYcOGaNCgAcaMGYPs7Gykp6ebbkII0/s95zVQqVRMHkelxt7oW7iRkO7QthqVwIjH6/L/gYiolFIcSKhFVJK1rBWEWuV9cPZGst3bKlJixtbT+Gl4G/42IiojNGoV+nVsjK8W7oC1b8ybiak4cTEWreq7fuwgRzkQEREREREREREREREREeVTVFQUfH19izoMKgNWrVoFIQSklBBCYM6cOQVOJJejVatWpmRyAHDq1CkmkyMissOdlHREnYmxWe+x5jXh7eHmgoiIiIioIBLTs3ItD/Z1zjEYERERERFRSSKEgEajgUajgZeXF4KDg4s6pALLWbwpJ1FZhQoVijokEyEE3Nzc4ObmhoCAAJf2m5MwzdPTE+XKlXNp32q12tR3UFCQy/rOLRYhBNzd3eHu7o7AwMAii4XIVaSU+GXDKTiaKyisTgiqhfg4NygiIioWpASUXL4fmF+OSjOtWoVRTzTAO3P32/1elxJYtucivh4SBi93beEESETFihAC/To0wdd//A2Z25fm/5MKsOLvE2hZL9TlySaZTI6IiIiIiIiIiIiIiIiIiKgY+eeffxAfH2+avNCuXTs0a9bMae1Xq1bN7P7Vq1ed1jYRUVmw899LyMgyWK0jhMDgTs258jAREVEJcCsp06JMCAF3rboIoim5KlSogDp16uT5+P3HovZSqVSoVasW0tPT86zj7+9foD7yq0qVKkhKSsrz8dBQ108KICIiIiIiIiLKj4TUbGw+anvRpNwIAG90bwS1iuc9iIhKIwkJo5XEOESlkRACAx6pgbELo5CpV+zePj4lC3+fuIluLbmYb3GRs4AzUWFpUK0cKgX64Fp8cp51JCRW7DiOz17sDK3GteMOmEyOiIiIiIiIiIiIiIiIiIioGDl79qzZ/ccff9yp7QcEBJjdT07Oe0ADERGZk1JiwabDkDaWI/bUadC+WQ0XRUVERESOkgBup2RZlKtVgLe71vUBlVBCCEyaNAlfffWV1ToqlcrhPjw8PHDo0CGrv8MK0n5+CSGwbNkyq3FwggoRERERERERFVfrD19Fmo1Fk/IS4K3DE82YKIWIqLTSGySyDZbJtHjKm0q78v4e6NCoEjYfvQYbw4EsSAlM23IKXVtU5vWhIialRMKNaCTERKNmq558PajQ6LRqPPFgHfy+4R+r14wvxibiys1E1Koc5MLogMK/Yk5EREREREREREREREREpYtaBajVZex299JqWFgYGjZsiKlTpxbxi0ClWVxcHACYBhlUq1bNqe17enoC+G9ye3p6ulPbJyIqzdIy9dhx9KLNeq3qVoK/t4cLIiIiIqKCkBJISs+2KNeq1dBpXbtCeEmnUqmg0WjyvKnV6gJN2hBCQK1WW+3DVcnkbMVR0H0lIiIiIiIiIioMiiIxffNpu5OkAHcTCT3duhp8PDTOD4yIiIoFg6LAoDjwJUFUwqmEwP+6N3R4+7/+vYE7qZaLV5HrSClxJ+Y0VnzeDet/fAHRe5fbXCiUyFFCCAzp3NxmPUWR2LD/jMvfi0wmR0RERERERERERERERESUT1FRUTh58iTCw8OLOhQqxVJTU83u5yR/c5bExEQA/yWr8/HxcWr7RESl2b8XYpGQmmm1joDAkCdaQqViAhEiIqLizqgoSEy1TCbnrlVDo+Z3ORERERERERERlR7Xbqch6ly8Q9sKITCiU30m0CciKsUURTL5EpVZ7RtVQIivzqFt07MN2Hg4xskRUX5JKXHn2ims+KIb0pNuQTFkYfOvI3F230p+plGhaV63Mnw9rX9mSEgs3f6vQ8m8C4LJ5IiIiIiIiIiIiIiIiIiIiIqRoKAgs/s5yd+cJSbGfOBScHCwU9snIiqtpJRYGnnMZj2tRoUurWq7ICIiIiIqKEWRyDQYLMp9PbXQqjnMmoiIiIiIiIiISgcpJVbuv4Rso9Gh7WuE+KBpjQAnR0VERMWJIiWYdonKKk83Nfo/XAOO5M2VEpi++QwUhf9BrialxO2rJ7Dii27ISI5DTtYuxZCFTb+8xIRyVGh8PNzQ7oFqNuv9c+46ElIyXBDRfzjKgYiIiIiIiIiIiIiIiIiIqBgpV64cAJhW9D579qxT29+5c6fZ/QoVKji1fSKi0spgVLBuzxmbgwzrVglGxSBfF0VFREREBZGRbURaluUEWi+d1nRMRkREREREREREVNIpUmJu5FlAOnDOSwDDHqvDxReIiEo5RcKUiMkM8zBRGSCEwIhO9R2+Phh1/hZuJKQ7OSqyRkqJ21eOY8UX3ZGeEm/xWWVKKLf/TyaUI6cTQmDAYw/Y/MzIyDTgwKmrLorqLh61EREREREREREREREREZFdhEoNoS5jN5UaABAWFoaGDRti6tSpRfwqUGlWr149s/u7du1yWtvx8fHYvXu3aQCDWq1GWFiY09onIirNrtxMxMXYBKt1BAT6tG0ItYrJZ4iIiEoCvVGB0ahYlJf3cwe/zomIiIiIiIiIqLS4eCsVJ68mOrStViUwsG0NLr5ARFTKKVLeTShHVEY1qOKPuhV9HNo226Bg9cErTFrmIlJKxF88ihUTeiAz9Q5EHk+7YsjCpqkjcO5ABF8bcrrHmteCVmM9dZuExNLIf136/mMyOSIiIiIiIiIiIiIiIiKifIqKisLJkycRHh5e1KFQKVa/fn1UqVIFwN1BL3v37kV0dLRT2p4yZQoyMzMB3F0Zr1WrVvD29nZK20REpZmUEhujomG0MbBLqIC+7RtzMg0REVEJkZSeDWMuX+++nlrXB0NERERERERERFQIpJRYvPM8DIrlogr50aJmEKqFOJZYhYiISo7MbCNyHRHB4Q9URmhUAs+1rwNHhvxICczZfpYJGV1ASokbp3dj5RfdkZly++6Tb4Upodx+JpQj5yrn7436VUNs1tt66Byy9UYXRHQXk8kREREREREREREREREREREVMz169ICU0pSM6O233y5wmzt37sSUKVMghDANiunbt2+B2yUiKgskgBU7TtgcgFjB3xt1qwa7JigiIiIqsPQsA6S0nEQbGuRVBNEQERERERERERE5n0GRWLTzAqS0PzOKEMDwx+pDxURCRESlXka2AQoTLVEZJoTAs+1qQuPgD5+jl+7g+p10J0dF95JSIubULqz65hlkZCblezujPhObfhmBc1GrmFCOnEalEujTtoHNRWdv3EnB+Rt3XBQVk8kREREREREREREREREREREVO6NHj4ZWqwVwdwDMhg0b8PHHHzvc3l9//YW+ffvCYDCYygICAjBq1KgCx0pEVBYkp2Xi4JlrVusIIdCpVW24azUuioqIiIgKKj4lK9dyfy83F0dCRERERERERERUOC7cTMHZ2GSHtnXXatD7wSo2EyQQEVHJZzAqd1faIyrDqgR7oWn1QIe21RsVrD54hcnKComUEtdO7sTqyc8gOyMZws6n2ajPxKafh+N81Gq+RuQUQgg81a4xbB0qKRLYsO+My953TCZHRERERERERERERET0f+zdd3gUVdsG8PtsSS+EkBCSQCD00EsoIr2oqKBiRRBRRAUFEZVPxa6v+oqoCBYsIAqKCBaQJtKREnrvpJIQkpCebJvz/YHJy7It2Wzq3r/LvSSzZ2ae3Z2dtnPuIaLyUQn3fACIjY1FTEwM5s2bV80fAtV10dHRePzxxyGlhBACUkq88847uP/++3Hx4sUyT+f8+fOYNGkSbrrpJmRmZpZOSwiB559/Hr6+vpX4KoiI6o79Zy6ioNjgsN09AzqwMw0REVEtciVfByktt92R9X25TSciIiIiIiIiolpPSoll/1yAoihOjX9D61A0CPBycVVERFQTmRQJ/jJC7k4lBMYPagVR3qQyAFICizafgcKcMpeTUiLp2GasnHUP9MX5Tk/HZCjG2rmP4NzelQyUI5doFdkAwf7e9htJiRXbjqGqFjneBpeIiIiIiIiIiIiIiIiIqIzi4uIQEBBQ3WWQm3jvvfewY8cOHDhwoDQEbtmyZVixYgWGDBmC3r17Iysry+yilvnz5yMrKwuJiYnYsWMHjh07BillaYAccPVueEOHDsULL7xQXS+NiKhWkVJi+ZajDi8i9NJq0CumcRVVRURERK5wOafY6vAgP88qroSIiIiIiIiIiMj1TIrEjzvOwZl4ICGARwe3hIo3XSAicgtGE4OViIQQGNG9CZ5dsBs6Y/nDeA/FZyH1SiEig3mTX1eRUiLp6Casmv0ADLoCiAomcpkMxVj76Xjc8vRCRHe/jTcYowrx8tCgf6dm+GXrMbvtDp9LRU5BMYIcBc+5gKrS50BERERERERERERERERERETl5uPjgz///BNNmzYtDYOTUsJoNGLdunV4/fXXkZycXNpeSoknn3wSL7/8Mr788kscOXIEiqKYBclJKdGmTRv89NNPvAiGiKiMjCYF6+POOGzXvllD1K+CC76IiIjINaSUSL1SaDFcCCAk0KsaKiIiIiIiIiIiInKt5MwCnL2Y69S4vp4aDO0U4eKKiIiopirUGeFE9ihRndMoyAc9WoY4Na7BqGDVvkSHN6ykspFSIvHI31j54f0wFOcDLnpfTYZirJk7Huf3reJnRRUihMA9Azs6vB67UGfEvtMpVVITw+SIiIiIiIiIiIiIiIiIiIhqqLCwMMTFxWH48OGloXDXBsNdfyFLybBr217b/tZbb8XOnTtRr169qn4pRES11sWMXCRcyrbbRgiBUf3bMaiTiIiolrmUW2QxTEqJIF/PaqiGiIiIiIiIiIjIdaSU+GNvIoyKcwEZfVo3RJAfz5MREbmLKwW66i6BqEZQqQTGD2oFZy4BkgCWbDsHxpNVnJQSCYc3YNXsB2DUW94grKJM+iKs+XQ8zu9bzUA5qpAbO0RBq3Ec4fbbtqNVsqwxTI6IiIiIiIiIiIiIiIiIyketds8HgNjYWMTExGDevHnV/CGQOwkODsaqVavwxRdfoGnTplbD4qw9gP+Fy4WHh2Pu3Ln4448/EBAQUM2viIiodtl6OB4mB51shACG92rNMDkiIqJaJjXLMkxOpVIh0EdbDdUQERERERERERG5jiKB77eehTN5BUIADw9qBRV/+yIichsGo3Rqm0FUF93cJRKeZQiHsmbfuSxk5hW7uCL3IqVE/MH1+POj0TDqClFZK6ergXLjcGE/A+XIeSH1/NA8vL7dNlJKbNh3DiZFqfR6GCZHRERERERERERERERERFRGcXFxOH78OCZPnlzdpZAbmjhxIs6cOYNly5bhgQceQERERGlYnLVHQEAARowYga+++grnzp3DpEmTGHJERFROUkos3+L4rqAhgb5oHh5cRVURERGRK0gA2QV6i+EalYCvF8PkiIiIiIiIiIjcUV5eHk6dOmX2uHLlSnWX5ZTLOUU4luBc7d5aDYZ0bOTiioiIqCYzVEHADVFtERLghU5RDZwat9hgxKajaS6uyH1IKXHhnxX48+MHrwbJVTKTvgir54zDhf1rGChHTlGrBG6/oY3D67MvpF5BamZepdejqfQ5EBERERERERERERERERERkUuoVCqMGjUKo0aNAgAkJycjJSUFWVlZyM7Ohre3N4KDgxEaGoqWLVtCpeI95oiIKqJIZ8A/xxIctruxfVN4efBSLCIiotpESon03CKL4R5qFTw16mqoiIiIiIiIiIiIqtuGDRtKf48vMWfOHDz11FPVVJHz/jqUCp3RuWCg3q1DEeTn6eKKiIioJjPY2mYwW4nckEoA4wY2x56zlyBRvhv4Sgn8uO0c7undlDf/LScpJU5t/xEb5j8Fk1FXZfMtCZQbPmURmnW9mZ8blYsQArf1ao0Pftput53RJPHPsUTcF1qvUuvhFYxERERERERERERERERERES1VGRkJCIjI6u7DCKiOuvcxSxk5VmGzFxLCIG7+rfjhYRERES1jEkBCoqNFsN9vDTw1DKYm4iIiIiIiKimO3LkCLZu3Wox3M/PD2PGjIFazbB4Kj8pJaSs/ak5Ukr8tOMcnHkpQkiMG9gCKv72RUTkVvKKDdVdAlGNIYTArd0a45kFauidCOfdciwN+UVG+PtoK6G6uklKiRPbluDv+U9BMemrfP4mfSHWzHkIw5/5Hk0738TrwKhcOjYPh7+PB3ILbIcgSkj8uvUo7h3QoVKXL4bJEREREREREREREREREVH5qFRXH+7k39cbGxsLtVqNyZMnY/LkydVcFBEREVUmKSX+3n/WYYchtUrgxg5Nq6YoIiIichm90YTcIsuOUX5eGqhV7BxAREREREREVJMpioKpU6di06ZNFs+p1WrExsYiJiamGiqr26SUMJlMUBTzQA2tVsuwhRomr8iAHScvOTWuh0aNwR3CXVwRERHVZFJKXM4ptv4kN/HkpsLr+6BdZD0ciM8q97g5RXrsv5CJ/u3CKqGyukdKieNbfsDGr6dUS5BcCaO+EKs/Hotbn/keUQyUo3Lw8/ZEp+Zh2HY4wW677UfioTeY4OlReZFvbtbDg4iIiIiIiIiIiIiIiIjIeXFxcTh+/DiD5IiIiNyAlMDv247DQZYcmoUFoVF9v6opioiIiFzGYFSgN5gshjfw94KKHQOIiIiIiIiIarSkpCTs2LHD6nOKomD+/PkObxZDzpkwYQKaNm1a+mjWrBkSExOruyy6zt5zmcgrtryRQll0iqqP0EBvF1dEREQ1nd5o+ZsJkTtTCYF7+zQD4NxxxdIdF3hMUgZSShzbvAgbv366WoPkShj1hfjz47FIOLSenx+VmRDAyD4xDgMIL10pRNLlnEqthWFyRERERERERERERERERERERERE18kv1uHguVSH7QZ3aw61mpdhERER1TZ5RQYYFcsOAEF+nmCWHBEREREREVHNJaXEDz/8AIPBelCWlBI//vgjCgsLq7gy95CdnY3U1NTSR1paGkwmhs/UJFJK/LLzgsMbJlkjhMT9N0ZDxfNjRERuR29UrAyVcDZIi6i2E0Lgjp5NoVaV/5ogKYE1B5JgMFn7XlEJKSWOblyITd9MhWJyLgi5MpQEysUf/IuBclQmQggM7d7S4XUGiqJg66ELlVoLr2IkIiIiIiIiIiIiIiIiIiIiIiK6zsmEy8gtdHzH29tvaOvwrqJERERU8+QVG6BYufg/or5PNVRDRERERERERGWl1+vx9ddf2+3Uf/nyZWzevLnqiiKqQQwmBWsOJDs1rkqocFu3Jvzti4jIDRXrGQ5LdL1moX5oHOzr1LjJmQVIzChwcUV1h5QSR/7+BpsXTKtRQXIljLoCrPlkDBKOMFCOyqZ5eH0E+XnbbSMBrNp5olKXKYbJEREREREREREREREREVH5qNXu+QAQGxuLmJgYzJs3r5o/BCIiIqpMUkr8tfeswxtse3lo0K1VRNUURURERC51OacY1jb2DevZv8CbiIiIiIiIiKrXvn37kJiYaDZMo9GYhV9JKTF37lwoilLV5RFVu8SMAiRnOhdc0jTEH1Ehfi6uiIiIaoNL2UVWhop/H0TuSatWYUT3JnAmZ1eRCtYdTGEQmRVSShzZ8DU2L5heI4PkShh0BfjzozFIObiBnyM55O2pRfdW4Q7b7TqeBJ3BWGl1aCptykREREREREREREREREREdUxcXBwCAgKquwyqpd58802rw1999dUyt60M1uZPROTupATW7D4F6SBNrlXjBggO9KmiqoiIiMiVLucWA9Ky50dksK9Z53MiIiIiIiIiqjmklPj000/NOvILIfDiiy/im2++wcWLF0uHb968GampqYiI4E1hyH1IKbHuQAoUJ8IuhABG9GgCjZrnxoiI3JHOaKruEohqHCEE7r6hKT5de7zc40op8Ms/F/DkTW0YyXgNKRUcXj8fWxbNgFQqL1DLVYzFBVj5yYO448XfEdaqF39HJpuEELi1Vyus33vW7hWH6dkFSErPQcvIBpVSB8PkiIiIiIiIiIiIiIiIiIiIqsDrr79u9UISa2FuttpWBobJERFZyi/S4ciFS3bbCCEwrHtLqHiRIBERUa108UohIIBrr+QWAMLqMSiWiIiIiIiIqKa6cuUKVq9ebRYmFxwcjOnTp6O4uBizZs0qfU6n02Hp0qWYNm0aO/yT25AAlu+MhxNZcgCAu3o25feFiMhNGYxKdZdAVCN1bhqMej4euFKgL/e4+85nILfQgHq+HpVQWe0jFQUHl3+Abb+9AymVq2nGuPbnWgkBQEB1zUjymmdKRrl6e9B/h6D0iWv2Y833aW0Nh9k4uC72r6StAok/PxmL259bhtBmnbm/TDYN6tYCQiUgFdsHZFIBth2OZ5gcERERVa207wchwN+9LowUvvnVXUL1Uemqu4LqIVSO29RFJjf9vN2ZUFd3BdVCXjlX3SVUH3f9nrvrSTiVG59MVrnpttydP3N35a7rNzfdhwHgvscq7krFZd1d5GoKqrsEIqpBru3Y4OiiEunsFd5lxItaiIisO5WUgfwixxeB3tKzFdelREREtZCUEimZBRadaiWA0ECvaqmJiIiIiIiIiOyTUmLlypXIy8srHSaEwMiRIxEQEIBHHnkEs2fPhslkKm0/f/58PPXUU/Dw4LWX5B7yiwzYH5/h1Lj1fDzQMSrIxRUREVFtoTeaLIZV7pVrRLWDr5cGN7QOxZ/7k8s9boHOiCMJWegbE1YJldU+JkMxGve8DaN73lraV0pA/JvhdjVGTpT+u+T61v89f/V/V0PjxDUhcmqNFiq1h3m70vFKiH//swyTE9f8u7StWZN/p6hiTBfZ16xRMBoE+CA923bfEQmJP3efwvhbulXKdYdcSomIiIiIiIiIiIiIiIiofNTqqw938u/rjY2NhVqtxuTJkzF58uRqLopqq5If/8sSFFeZAUWVHVRHRFRbSSmxYe8Zh+28tBp0jObFnkRERLVV6pVCi2FCAA38GSZHREREREREVBMpioJPP/3U4uZdTz75JIQQaNGiBTp06ICDBw+WPn/mzBkcPnwY3bt3r5IaS2q7/rfY0vCBSr5BjZSy2uZNNcPhhCzkFRmcGrdPm1D4eTF6gIjIHUkJ5BUZq7sMohpJCIF7+0Tjz/1JuD5krCx+3ZOAG9s25P44AI2nDxo0aVfdZRBVGi8PDbq0aIR1e8/abbf7WCL0BhM8PVx//MUjOiIiIiIiIiIiIiIiIiKiMoqLi0NAQEB1l0G1VJMmTcp8QVB52hIRkWtJAH/tPeMwdLNlZDDq+XtXTVFERETkcokZlncDVwkBf29tNVRDRERERERERI6cPXsWhw4dMhsWHR2NDh06AADUajWeeOIJPPnkk2ahbp9//jm+/vrrSvv9VUqJ3NxcHD16FNu2bcORI0eQkJCA4uJiAIBWq0WjRo3QunVr9O7dG507d0ajRo2g0WgqXJOUEnq9HqdOncLWrVuxf/9+nDt3DgUFV897eHh4oEmTJujRowcGDhyItm3bwtPTs1zz1ev1pa/FaDTiypUrFjVcuHABDRo0cDgttVoNHx8fCCEgpURBQQEURTFr4+3tDa3WNednpJTIz8+3+M3Hz88PKpXKJfOoSaSUWL0/2bmRBXB372a8ToGIyE0pUiK3WG8xnFsFoqsGtGsErVoFg6l8N/CVElh3IAX/HSuhUfMbRVTXCSFwa++2WLf3HK5ehWjdpSsFSM3KRdOw+i6vgWFyREREREREREREREREREREVSA+Pr5S2hIRkWsV6Qw4dP6S3TZCCAzu2hwqdqghIiKqlRQJZObrLIZ7adUMkyMiIiIiIiKqgaSU+Oqrr2AymUqHCSHwyCOPlAaPCSFwxx13YNq0aSgqKiodb8WKFfjoo49cfuM4RVFw+PBhvPvuu1i7di3y8vJK52lNSUiXWq1GdHQ0Ro8ejfHjx6Nx48blDvCSUiIrKwvz5s3DN998g+TkZEgprc57586d+PnnnyGEQGRkJKZOnYrHH3+8NNTNkcWLF2PSpEmlf+t0ludUhg8fXqZwtp49e2Ljxo2l833nnXfw8ccfm7V55pln8J///McloWbnzp1Dly5dYDQaS4eFhITgxIkT8PX1rfD0axpFSqzalwQH90uySqsS6N+ukeuLIiKiWsNgdGIDQuQmwoK8ERXij7NpueUe99ylXKTnFiE8qO7tfxKRpQFdmkGlAq7LTTejSIk9J5IrJUyu7sWmExEREREREREREREREREREREROSk+9QpyCoodthvSvblLOjIRERFR1TOaFGTkWm7vfTzV0Gp4eTURERERERFRTVNUVIQlS5aYhaVptVo88MADZufqQ0JCMHDgQLNhOTk5WLNmjctqkVIiLS0N9913H2JjY7Fs2TLk5ubaDHO7djwpJYxGI06fPo033ngDnTp1Qn5+frnmbzQasWDBArRq1Qqvv/46EhMToSiKw3krioLExEQ899xz6NixI/bs2WN3nBImkwnFxcWlj+vHkVJCr9ebtbH10Ov1peMJIfDoo49aTH/BggUoKCgo13ti6zV/8cUXKCgoKJ22TqfDvffeCx8fnwpPvybKytPhTGqOU+NGhfgjvH7dfF+IiMgxKQGTvdQbIjenUQkM7xoJZy4T0ptM2HMmw/VFEVGN1KxRfQT6etltI6XE6l2nynRMXl682oGIiIiIiIiIiIiIiIiIyketcs8HgNjYWMTExGDevHnV/CEQERFRZdlxNAHSwTXSWo0KXVpGVE1BRERE5HJ6o4LCYqPF8GB/T2jVDIslIiIiIiIiqmm2b9+OtLQ0s2Hdu3dH48aNzYapVCo89dRTZsOklPj0009hMpkqXIeUEjt37kTnzp2xfPlyGI1Gi87fQgirD2vTMhgM5eo8XlRUhIcffhiPPfYYsrKyrI7raN5SSly4cAGDBg3C0qVLK6XzellFR0ejb9++ZjWmp6djw4YNFZ52QUEBfvjhB7PXp9Fo8Pjjj9fZmwXtv5AJvdG5IKCbu0RAo6qb7wsRETlmUhQU6ix/NyGiq4QQuLNnlHMjS4GVexOrdb+biKqOt6cWHZo1dNhu1/HESgly1bh8ikREREREREREREREREREdVRcXBwCAgKquwwiIiKqJFJKrN1zChL2L+CMCq2HBoE+VVQVERERuVqBzohio2UH8gb+3nW2MzERERERERFRbaUoCj766COz8AUhBCZPngy1Wm3Rvl+/fggJCUF6enrpsD179iAxMRHNmjVzug4pJTZs2IA77rgDRUVFFvWoVCq0bdsWI0aMQGxsLBo2bAi1Wg2DwYDExETs3bsXW7ZswfHjx1FcXFzuMAmdTodRo0Zh7dq1Vufdrl073H777ejVqxeCg4OhUqmQl5eHPXv24Oeff8aRI0cgpSx9FBYWYvz48QgODsaQIUNsnhNp3bo1Hn30UQCAyWTCypUrkZmZaTb/gQMHlum9bd68udl8VCoVpk+fjk2bNpUOk1Ji9uzZGDFiBFQqVbneo2v99ddfZssAAPTs2RPR0dFOT7Mmk1Liz31JTo0rBHBb9yY8L0ZE5MaMJgm9kUFXRPZ0bFof3h4ap4IXNx1NhdEkodVwf4uorhMAhsa2xLbDCXavQUxIz0ZWbhFCg/xcOn+GyREREREREREREREREREREREREQEwGE3Ye+qiw3a92zWBugIdmIiIiKh6ZebqYFIsL9wOr+8NduEgIiIiIiIiqlnS09OxZcsWs2F+fn64+eabrbb38fHBqFGj8MUXX5SGrhmNRnz33Xd47bXXnA7MOnPmDO69914UFhaaDRdCoF+/fpg9ezY6duwItVptMY8+ffrg/vvvh6IoyMzMxKpVq/DJJ5/g/PnzZZq3oiiYMmWK1SC5gQMH4oMPPrA578GDB+OFF17Ali1bMGHCBCQkJJROQ6fTYfTo0Th06BDCw8OtzvvGG2/EjTfeWPr3yJEjsXLlSrMavvjiC7Ro0aJMr+X6+gYOHIjw8HCkpKSUDtu1axcuXLiA5s2bl2ma1zOZTJg9e7bFe/Xss89aDSCsC0yKxN+HL6KcGYUAAC+tBt2ig11fFBER1RomKWFSlOoug6hGC/T2QMeoeth1OqPc46ZkFuBSThEig30roTIiqkmEEBjStQVe/XYD7N3PVm9QcPTCJQxycZgcr2gkIiIiIiIiIiIiIiIiIiIiIiICkJaVh7SsPLttBIDhvVo73dmMiIiIql9GXpFZZ+ISzcMCqqEaIiIiIiIiIrJFSolly5ahuLjYbPjw4cMRFBRkdRwhBCZOnGh2Hl9KiW+//RY6nc6pOnQ6He6//37k5OSYDVer1XjjjTfw119/oWvXrtBoNDZ/PxBCQK1WIzQ0FOPHj8fevXuxevVqeHh42J23lBIrV67EN998Y3Y+Q61W4/XXX8fatWvtzlsIAY1Gg0GDBmHPnj3o1KlTaTspJTIzMzFlyhQoNgJkhBClDwBQWbnZTkmIXVke1/Py8sITTzxh9pzRaMT8+fOtnr8piwsXLmD37t1mw0JCQjBs2DCnplcbXM7R4dwl+79x2dI2MhD1fD1dXBEREdUmUkooVm7CczUFx7ntMVFdIwRwW7cmcOZyIaMiEXfmsuuLIqIaqVXjYHh7ahy2+3v/WaePe21hmBwRERERERERERERERERlY8QgErlXo9/r/6IjY1FTEwM5s2bV80fAhEREVWGg2dTYTLZv9u2SqVCt9YRVVQRERERVYakzAKrnZcjg30ZGEtERERERERUgxiNRnz++edmnauFEJg0aZLdY/h27dqhZcuWZsOSk5OxZ8+ectcgpcSCBQtw8OBBszpUKhXefPNNvPzyy9BqteWaphACWq0Wffv2hZeXl922hYWFmDp1Kkwmk9n4//d//4eZM2eWed5CCISEhGDNmjVo2LCh2ev7/fffceLEiXK9BlcRQmD8+PFm74OUEt999x0KCwvLPT0pJb788ksYjUazeTz00EPw8fFxSc010aGETBiM9n/jskYI4ObOkVDxlBgRkVsr0ptgtBomR0QlhBAY1jnSqXGlBFbtT3J5aBQR1UwBvl5o3sh6AH4JKSU2HzwPV68WHEfYERERERERERERERERERERACAuLg4BAQHVXQa5iQULFiAv7393Dr/33nsRFhbm9PRSU1OxbNmy0r8bNGiA0aNHV6hGIqK6REqJDfvPAhCwd2ftBoHeiAjm/gAREVFtJaVEfHq+xUXZQgCNG/hVT1FEREREREREZNXRo0dx6tQps2GNGjVCbGys3fE0Gg0ee+wxPP/882aBDfPmzUPfvn3LFSZfXFyMd9991yLQbtiwYXjhhRegUqnKPK3yklJi6dKlSExMNJt3t27d8Morrzg174YNG+LDDz/EmDFjSl+TyWTC3Llz8dlnn1VL0H6jRo0wdOhQrFy5srSm9PR0bNiwASNHjizXtAoLC/H999+bfV4ajQaPP/54nb2JgJQSaw8m2/l1y76bukTU2feGiIjKpthggmJ1S8LtA9G1WocHIsBLi5wiQ7nH3XIsFUZFQqvm94qorlMJgX6dmuHIhXS77U4mXEaRzgBfbw/XzdtlUyIiIiIiIiIiIiIiIiIiIiKXOHr0KB599FFMmzYN06ZNw8cff4zQ0NAKTTM0NBQfffRR6TTHjRuH+Ph41xRMRFQHKFJi28ELkA662nRq3gieHryHJxERUW2WlFFgMUxKicj6PtVQDRERERERERFZI6XEvHmQq/aRAAEAAElEQVTzLELcHnzwQXh5edkdVwiBe++9F1qt1mx6q1evRmZmZrnq2L59O5KSksyGeXh4YM6cOdBoKvf3ApPJhE8++cTiPZg1axY8PT2dmqYQAnfeeSciIyNLh0kp8dtvv6G4uLjCNTtDpVLh+eefNws0k1Ji9uzZUBSlXNP666+/kJ5u3mG/Z8+eiI6OdkmtNZEiJf4+fNGpcb21GrRvHOTiioiIqLYxmhQIBscROeTrpUH7KOf2nZIzC3E5t8jFFRFRTSSEwMAu0XCU2Z1TqENSerZL580wOSIiIiIiIiIiIiIiIiIiohrm22+/BYDSTgFTpkyp8B3t1Wo1pk6dCiklpJRQFKV0PkREBBQU6XEu9YrdNkIIDIttWUUVERERUWU5dynXYphapUKwv3OdsGsiKSXyiwxQFPtBudePU1BsgKmcnbSJiIiIiIiIKkNeXh5WrFhhFqSmUqnwyCOPmIWO2RIREYFevXqZDSsoKMCvv/5qNk17pJRYsGCBRfthw4ahefPmZZpGRSQkJODYsWNmw6KioixeV3l5eXnhjjvuMBuWnp6OxMTECk23Inr06GHxnu7cuRMXLlwo8zRMJhM+/PBDs2FCCEyfPr3Cv7fXZDkFBpy/lOfUuG0jAxHg4+HiioiIqLbRGZUy7x8RuTMBYHjXxg4DoqwxmEw4HG//uiQiqju6toqAStg/DlUUYO/pFJfOt+4e+RIRERERERERERERERFR5VCp3PMBIDY2FjExMZg3b141fwhU1/3555+lHSCEELj//vtdMt0HHngAKpUKQghIKbFy5UqXTJeIqC64kJqFgmKDw3Y3dmhapk5qREREVDMpUuJiVoHFcE+Nqk51nD2bmosmjy/FlG92wmhyHA4npcQ/J9PRbNLPeH3p/gp1GisJMNfpdMjOzsbly5dx6dIlpKenIyMjAzk5OdDpdDCZTOycRkREREROK7l5jsFgQF5eHjIyMpCenl6635mXlweDwVDajohqn3Xr1iE7O9tsWExMDFq0aFGm8VUqFZ5++mmzc/pSSnz++ecwmUxlmobBYMC2bdvMhgkhMGHChCoJJ9u2bRuU60Lfb775Znh4VOwchhACQ4YMsXhvDh06VKHpVoRWq8WUKVPMajIajZg/f36Z1+MJCQnYvXu3WfvQ0FAMGTLE5fXWJCdSslFsKNsyfS0hgKGdIqDiz15ERG4vK09X3SUQ1QpCCAzpGO7UuFIKrDuYzHMURG6iUf0ANAj0cdBK4u99Z126XtC4bEpERERERERERERERERERHVcXFwcAgICqrsMquMuXryIM2fOlF4k3717d4SFhblk2g0bNkTPnj2xc+dOAMDhw4eRlZWF+vXru2T6RES12e4TyYCD67K8tBq0iAyumoJqqZLOucXFxcjMzERKSgpSUlJw5coVGI1GCCHg4eGBRo0aITg4GOHh4QgKCoKXlxeEEE4F9SmK47ukl4SpVrWS96OoqAjp6elISUlBUlIS8vLyoCgK1Go1goODERERgcjISDRo0AAeHh4MLKRaryzfS3uuXR9U5PtQEmhVWRytW8oy//Kun8rzmqpr3VfTGUwSGVY6RdXz9YC3Vl0NFVWO+v6eCPTxwOfrT0JKYPb4nrC1s2NUFPxzMh23/mc9CnVGtI0MKvf8FEVBWloatm/fjvXr12P//v24ePEicnNzYTQaIaUs/W5rNBr4+/ujUaNGaNu2Lfr06YM+ffqgZcuW8PHxcelym5eXh2+++aZM35u77roLTZs2rdD8Lly4gF9//dWpcVUqFTw8PBAYGIjmzZujadOmCA4OhkajKfd7UlxcjPnz58NoNDqcZ8lnotVq4e3tjfDwcAQGBiI0NBSBgYHw8fFxqgYiIiJyTEqJH3/8EWlpaeUeVwgBtVoNLy8vhIeHo1mzZmjcuDH8/PycCjfat28ftmzZUu7xAECtVsPDwwOhoaFo2rQpmjRpgqCgIKjVapfvQ5Scdzp+/DjWrl2LjRs34uzZs8jKyoJery/d71OpVPD09ERQUBBat26NgQMH4qabbkJMTAw8PT25b0NUCyiKgk8//dTsHJMQAk888QTU6rIfvw8dOhSBgYFmoXSHDx/G6dOnERMT43D8rKwsXLp0yWyYVqtFz549y1yDs6SU2LRpk8V70K9fP5esx6Kioizmd/z48dJj+KpWcpO1F198Efn5+aU1LVq0CK+++ip8fX3tji+lxDfffGN2LCyEwEMPPeRw3NpMSonNR9Pg7OnYmzpHcLtIRETQGUyOLpcgon+1Cg+Er6cG+cX2f4OxZuvxNCgSUHP3i6jO89Cq0blFI6yLO2O33e7jyTApEhoXrRgYJkdERERERERERERERERERFSDHD9+vPTfQgj06NHDpdOPjY0tDZMDgKNHj6Jfv34unQcRUW0jpcSmA+chHVwe3bhhAAJ8vKqoqtpFURQkJyfj999/x4oVK3Do0CHk5uaWduC9vrNbCZVKhYCAALRo0QJDhgzBrbfeis6dO5c5SEZKiWeeeQbr16+32SYyMhLr1q0rVwfDilIUBSkpKfj111/x888/49ixY8jNzS19H6y9HyqVCiEhIejRowdGjx6NIUOGoH79+rWmE1dZgsNqy2uhipszZw6++OILp8ZVqVTQaDQICwtDixYt0LNnT/Tp0wdRUVHlDjM6ceIE7rrrLqfqcEQIgQ0bNiAiIsJmm/379+PBBx+0O53ffvsNbdq0KdM8pZRYs2YNnn/+eZhMJpvtVCoVnnzySTz11FNlmq67KSw2Iq/IYDE8JNALanX5Qzdqqvp+ntj81nAMeHU1vvjrJHIK9UjIKLBoJyXw0cpj+HrDSRTqTPhofC88cGN0mb9rOp0OGzZswKxZs7Bz507o9XqH2wS9Xo/CwkJcunQJBw8exE8//XS15vr1MWTIEEyYMAF9+vQpDZt1lpQSK1aswLPPPuuwJiEEjh8/jq+++qpC8zxx4gSee+65CgdqAoBGo0GTJk1wxx13YMKECWjVqlWZg2EKCwvxwgsvQKezDE4sy7xL/u3t7Y2QkBB0794dI0aMwODBgxEWFuZUQA0RERFZklLik08+wZ49e5yexrXbbz8/P3Tp0gUTJkzAyJEj4e/vX+Z9my1btmD69OlO13FtLR4eHmjevDnuv/9+PPzww4iMjKzwOQEpJdLS0vD555/jm2++QVpaWmmIvy16vR55eXlITEzEhg0bMHPmTISFheGBBx7ApEmT0KxZM56rIKrBEhMTsXv3brNhXl5eGDVqVLm+uwEBAbj99tvxww8/lK4zFEXBV199hdmzZzucVkpKikVQd0hICIKCyh/EXl5SShw7dsxi+MWLF3H69Gmb49ib3rUOHDhg0caZgFNXCg4Oxp133mn2eV26dAkbNmzAyJEj7Y5bVFSEhQsXmr1OjUaDiRMn1un1vQTw9+EUp8b10KjQrnHlL8tERFTz6QwmCGntljSMmCO6nr+XFq3D62Hf+Yxyj3smNRf5xQYE+nhUQmVEVNMM6ByN9XvP2j1WT0jPRl6hDkH+3i6ZJ8PkiIiIiIiIiIiIiIiIiIiIapDz588DQOkd39u2bevS6bdu3drs73PnzjFMjojcntGkIO5UksN2vWOaQK2qux2OnKEoCuLi4vDaa69h06ZNMBgMDsNTrn3eZDLhypUriIuLQ1xcHN577z00aNAAt99+O5555hm0b9/eYSevixcv4tSpUzafL0tNriKlxNGjR/Hyyy9j3bp15Xo/TCYT0tLS8Mcff2DlypXw9fXFHXfcgVdeeQUtW7as0Z3dpJT4/vvvsW/fPpttXnrpJTRs2LAKq6LqlJmZafd7WRZHjhzBX3/9hS+++AIqlQqtWrXClClTMHbs2DIHTup0Opw+fbpS1gFCCBgMloFk1youLrY7fyFEmYOepJRYuXIl7r//fhQVFdlsp1Kp8Pbbb2PSpEk1er1RnTLzi2EwWX4mjYP9UJc280IINGngi81vDseAV1ZjyY5zJc+YtVMUidkrj0KjEvhofE9MurlNmZYdRVGwbds2TJ48GcePH6/Q96xk3MzMTCxduhQ///wzGjdujH379qFBgwZOT1dRFHz++edlqk1KiV9//RUfffQR/P39nZ6nK5TUazAYcO7cOcyePRtz5szB3XffjY8//hghISGV9v2+9r2SUqKgoAAFBQWIj4/H8uXL4enpiaFDh2LmzJno3r07Q+WIiIhqgGu333l5edi6dSu2bduG0NBQvPvuuxg7diw0mqrpQlhSi06nw/Hjx/Haa6/h3XffxcSJE/HWW285vZ9VVFSEWbNmYdasWcjLy3Nq37MkeO7ixYuYPXs25s6di6+++gpjx451qiYiqlxSSixatMji3Evfvn0REhJSrmkJIfDkk09i8eLFZjf7+PHHH/HOO+/Ax8fH7viXL1+2GBYeHl4l61aj0YhLly5ZDH/uuefw/PPPOzVNR+vQnJwcp6brKkIIPPvss1iyZInZzRQ++ugj3H777XaPQzdt2oTU1FSzYb169UJ0dHSl1VsTFOtNOJJ0xalxo0MDUM+XQSZERARk5JXvxiRE7kwIYGincOy/kIHynqIo0Blx/lIeujQLrpziiKjGEELghvZN4CiYtVhvxLmUTHRvE+mS+fLXWyIiIiIiIiIiIiIiIiIqH5XKPR8AYmNjERMTg3nz5lXzh0B1WW5urtnfrr6rff369QGgtON5dna2S6dPRFQb5eQXISUj124bIQQGdmnOYJ5/SSlx+fJlPPDAA+jTpw/Wr18PvV5f4cCmkukuWLAAffr0QUFBgYsqrny5ubl44okn0K1bN6xatapC74eUEvn5+Vi8eDGGDRsGo9Ho4mpdb926dZgzZ47Vx6effsp9DnKalBImkwknTpzApEmT0KFDB/zzzz9VFhJZE0gp8fvvv5cpSO69997DjBkzoFarq7DC2iU5s9Dq8tOyUUA1VFO5SgPl3hqOqAb+ELDcj5HAv0FyvTDp5rZl2tcpLi7G008/jcGDB1c4SM4aKSVSUlLsLu9lER8fbzfo9HpXrlzBxo0bKzTPyiClhMFgwE8//YTOnTtj37591bIOlFKiuLgYq1atwg033IDRo0cjIyPDrdbHREREtYWUEpcuXcKECRMwevToCu9XVaSOoqIizJkzB7169UJ8fHy59h2klDh//jz69OmD1157Dbm5uS7Z95BSQqfTISkpifsyRDWUTqfDN998Y/YdFULgqaeecirUumvXrmjSpInZsPT0dGzZssXhuNbC3OrVq1clvxWYTCYUFhaaDSsJx1QUxalHyfjXPq515YpzoWSu1L59e3To0KH0bykldu7cifj4eJvjKIqC2bNnWywz06dPr/NB6BezCnGlQO/UuDe0CeVNlIiICACQXaCzEXUjcP1NaojcnRACgzuEOzeyBP45aXmMQUR1U5vGIfBwFEYvgd0nHN8Et6zq9hEwEREREREREREREREREZELxcXF4fjx45g8eXJ1l0J1WGWHxVzfIaC6OpEREdUkZ1OyoDcodtsIAXRr5eTFoHWMlBK7du1C586dsWzZMphMpkoJkVEU+59JTSGlxOnTp9GjRw989dVXMBgMLns/SkK02LGZ6CopJeLj4zFkyBAsW7bMLb4bUkr8+uuveOCBB+zuu6vVarz//vtu0UG2ohIz8nH9oiME0DqiajqCV7WSQLlNb9yCkAAvK88D74+NxaSb25Tp9efm5uLmm2/G559/XqO3UVJKLFy4ECaTqVzjzJs3r8bug0gpkZaWhmHDhuH48ePVWofJZMLPP/+Mrl274sCBAzV2OSAiInJ3iqLgl19+wejRo2EwGKqtDiklTpw4gQEDBuDy5ctlHufQoUPo1asXDh48yP0NIjcTFxeH5ORks2EeHh4ICAhAampquR/p6elo27at2fSklPjkk08cHgNaO/atyhD/mnqMWpnUajWeffZZs/MUBoMB8+fPt7k9SEhIwPbt282GNWzYEEOGDKnUWmuC/RcyoDiznRTA0I4RdfJ8GBERlY+UEjqDtXPp8t8HEV2vfZMgaNXl/01WAvj7yEWe5yByE/X8vRHewN9uGwmJrYfLdxMOexxE1xEREREREREREREREREREVFVCg4ONvu7rB2ryiojIwPA1QsBhRCoV6+eS6dPRFQb7Tqe6LCNr5cWjUPrVX4xNZyUEn///TfuvPNOFBQUuP0FriWdoQcNGoT09HS3fz+IqoKUEsXFxRg3bhzCwsLQt2/fOtvhUUqJFStWYMyYMSguLrbZTq1W47///S+eeeYZBsk5IKXEieQrVp9rEWb/Iu7aTAiBqBA/PDqoFb7++zQy8oogpYBGJdCrdQjGDWhZpu9RYWEhbrvtNmzfvr1M27ySaapUKnh5ecHDwwNSShgMBuj1+tIw9crYfur1evzwww/lnva2bdtw6dIlNGrUyOU1uYKUEtnZ2bjvvvuwd+9eeHlZBgRWZS3JyckYMGAAVq5ciX79+tXZ9TG5HyllacD1tWHRQghoNBqoVKrSbS6XeyKq6aSU+P333/HVV1/hySefrLb1lpQSiYmJePjhh7Fy5Uq7QUxSShw/fhxDhgxBVlZWmfc9hRDw8vKCp6dn6Tq8uLi4dHyetyGqHaSUmDt3rkWIml6vx6BBg5xej1kLZdu6dSvS0tIQHm77RjIajQZCCLN1SGXfHOxa179elUqFsWPHokmTJpUyvxYtWlTKdMtDCIGRI0eifv36yMzMBHB1ufjuu+/wyiuvwNfX16y9lBJff/21WXCqEAKPPPIIfHx8qrT2qialxMYjqU7l/KiFQPfmDVxfFBER1Up5RVW3f0NUF9T380RYPR8kZuSXe9wDF7JgNEloNTy3TFTXadQqdGkZjgup1q9RKHHgdApMigKNC8LrGSZHRERERERERERERERERERUg4SEhAD4X8eAo0ePunT610+vZH5ERO5KSoltZbi7Z8uIYPh6e1RRVTWTlBK7d+/GHXfcgYKCAoftSzrx1qtXD9HR0YiOjoa3tzeklCgoKEB8fDySkpKQmZkJRVFqZYfepKQkDBkypExBckIIqFQqNGrUCDExMQgNDYVGo0FRURESEhJw/vz5Wv1eEJWHo06/ZfkO6HQ6PPzwwzhy5IhFB9K6QEqJX375BWPHjoVOp7PZTq1WY9asWZgyZQqD5MroZEqOxTAhBCKD695ydC0hBN4e3Q2PDW2Nga+uQUpWAT4a3xNP3NQWqjL0VVEUBZMnTy5TkFzJ9n/kyJG477770KFDBwQEBECj0UBKCZPJhIKCAiQlJWHnzp34888/sXv3buTm5rpsG3jgwAEkJjoODL6eTqfD0qVLMXXq1EoJWhFCYOzYsYiOjjYbnpOTg4SEBOzZswcpKSl234eScJWlS5fioYcecrrO4OBgNGzYsHSaJpMJ2dnZyMvLKw2wdPR5SCmRn5+PESNG4J9//kG7du2cqoXqJr1ej3Xr1lkN7XCkJLQtODgYISEhCAkJgY+PD1QqVaV8N6WU0Ov1OH/+PHbu3IkdO3bgxIkTuHTpEnJycmA0GkuPbwICAlCvXj00a9YMHTt2RM+ePdGpU6fS/fvy1JeUlIT9+/eXqW3Tpk3RsWNHp16/lBJbt25FdnZ2mdoPHjwYfn5+5Z4PEVWORo0a4dFHH4VG879ugIqi4OLFizh9+jT27duHwsJCh/sPr7/+OsaMGYOAgACn6tBoNJg4cSJCQ0PNppuZmYkLFy5gz549yMjIcFjHunXrsGvXLvTp08dmu5ycHIwYMcJhkJwQAmq1Gr169cITTzyBG264AcHBwdBqtaUhxleuXMGRI0fw22+/4c8//+TNAIhqgczMTKxevdpieEngrysVFxdj6dKleOaZZ2zuZ1kLG79y5Urpjbsqk0qlglarNRsmhMDrr7+OqKioSptvTQhM9vf3x+jRozF37tzSz/3SpUvYsGEDRo4cada2qKgICxYsMFs+tFotHnvssRrxWiqTIoFdp9OdGre+nwca1fd2cUVERFRbXbxSaGVo3d6OElWERi3Qs2WIU2FyqVcKcaVAh9BA7osR1XVCCPTr2Ay/bjtu95xGSkYecvJ1CA6seCA6w+SIiIiIiIiIiIiIiIiIqHxUqqsPd/Lv642NjYVarcbkyZMxefLkai6K6qpr7/YupcTatWtdOv01a9ZACFF6YUJldjQgIqoNjCYFB86mOmzXKyYKqjre6ciRrKws3HvvvSgstHYh+f8IIeDp6Yl7770XkydPRrt27eDt7W3RaasksOHSpUvYunUrvv/+e+zYsQNFRUW1olOvXq/Hfffdh7S0NIcdm728vPDQQw/hmWeeQbNmzeDhYR5MWPJeJCUl4bfffsNXX32Fc+fOORW8QVQbeHp6YunSpWjcuLHZ8JJAhBUrVuCXX35BQUGBze+XlBLx8fH45ZdfnA5TEkLgnnvuQd++fZ0at379+uUeryyklPjxxx/xyCOP2A2S02g0mD17NiZPnswguTJSpMTZtDyL4R5qgQYBXtVQUdUSQiAqxA+b3xqOrcdS8WD/FmXav5FS4s8//8SiRYscbqM9PDwwbdo0vPDCCwgKCrL53fT390dYWBhiY2Px9NNPIzc3F3/99Rc++ugj7N6926nXd2298+bNs1lrSU3WnpdS4ptvvsFTTz1lFtjiKmq1Gu+99x7CwsKsPq/T6bBq1SpMnDgR2dnZdteBc+bMwZgxY6B28g717733HsaPH282zGg0orCwEImJiVi5ciUWLlyI8+fPOwyFycvLw6hRo7B3714GUFGpvLw83H333dDr9U6NX/JdFULA19cXLVu2xB133IEHH3wQTZs2dcm2T0qJhIQEfPnll1iyZAmSk5MdhpRcuXIFCQkJOHToEH777bfS4LuoqCiMGDECDz/8MNq0aVOmYLmtW7dizJgxZao1KioKJ0+ehJdX+bdX6enpuPXWW8scCn7ixAm0bt263PMhosrRvXt3vPnmm1afk1IiPT0dL730Er777ju75xEyMjKwbt063HPPPU7V4evri//+97/w8bHeobGwsBDffvstZsyYgaKiIpvTURQFc+bMwQ033GB1PakoCp577jlcuHDB4fmWFi1a4Ntvv0Xv3r1t7hMFBgaiadOmuO2221BYWIj169fj1VdfxbFjxxy8YiKqDlJK/P7772Xab3HV/L744gs89dRTFqFtJazdmOvixYswGAzw9PSs1Po0Gg0aNGiA9PT/hYUpioK0tDQ0bdq0Uudd3YQQmDRpEj7//HMYjUYAVz+vjz76CLfffrvZ8cCmTZuQlpZmNu6NN96IJk2aVHndVa1AZ8D5S5bnusqiQ1R9eGmdO6dARER1T6HOWN0lENUqQggMbN8Iv+y6gPJe3mIwKTiZks0wOSI30bNtY4dtig1GXEjNYpgcEREREREREREREREREVFViouLQ0BAQHWXQXVc+/btERoaisuXLwMAkpOT8fPPP+Pee++t8LSXLVuGpKSk0g5agYGB6N69e4WnS0RUm+UV6pCWZb+jjRACfTs2dSqoqK5QFAXTp08vDVewRQiBrl274vvvv0ebNm3svmclIWtRUVEYO3YsHnzwQVy+fBmLFy/G7NmzkZ2dXQmvxDWklPj888+xe/duh+9HTEwMli1bZvf9KHkvWrZsieeeew5Tp07Fhg0b8PzzzyM3N7dS6pdSwmQylQ5Tq9UQQtSK5bykfkVRSv9dUrsQorQjYW14Le5Kq9Vi8ODB8PX1tXiuW7duuO222/Dyyy/j5ptvthtiJKXE/PnzMXbsWKc/76lTp+KGG25watzKUNKJecqUKaUdZa3RaDT4+OOPMWnSJC7r5aA3KEi7YhlsEejrAV9P97isWgiBJg18MaZ/izIvO0VFRZgyZYrDkNPAwEAsX74cgwYNKtdyKYRAYGAg7r77btx55504cOAAZs6c6fSynZOTg5UrV9pcdwQHB6N+/fo4c+aM1TYnTpzAyZMn0b59e6fm74i97a2XlxdGjRqFRo0aYdCgQXZDuI4ePYqMjAw0bNjQqTr8/PwsQlfUajU8PT0RFBSEjh074vnnn8fSpUvx7LPPIisry+76+PTp03j77bfx7rvvcr1ELlGyvJUEFu7fvx8HDhzA22+/jTvuuAOzZ89GeHi4U8ublBKXL1/Giy++iMWLF0Ov1zsdaC2lhMFgwNmzZ/HRRx/h448/RkxMDFavXm0RnFsRiYmJ2Lx5M26++eZy17dgwQKHoeBEVPNZW98JIRAWFob58+dDr9djyZIldrfXy5Ytw9133+30ttrefoyvry+eeuop1KtXDw8//LDdfcfNmzejuLgY3t6WnaYPHz7sMMRYCIGePXvizz//LHPId0k46R133IHhw4fj+++/txt6R0TVw2Qy4fPPPzdbB1TG8cW10z979iyOHDmCrl27Wm0bHh4OrVZrdnyWkZGBrKwsNGrUyOW1XUulUqFFixY4fvy42fBdu3ahV69elTrvmqBly5bo3bs3tm3bVjps586diI+PR3R0NICrv13Mnj3bYrsxffp0t7j5QnJGAQr1JscNryME0D/GetA9ERG5p/xChskRlVfPVqFOj/vPyQz0bRvG31OI3EDziPrw0Kqh09vf1h44exHd20RWeH7ucdUDERERERERERERERERERFRLTJkyBAsWbIEQghIKfF///d/GDx4MIKDg52eZlZWFmbMmFE6TSEEhgwZ4hYX0RMR2ROfdgU6g/2ONkIAHaOdC+moK44dO2a3QzJwtVPf0KFDsWLFCqsBUY6oVCo0bNgQ06ZNw+OPP44lS5ZYBJzUFNnZ2Xj77bcdvh+dOnXC33//XeaOzSXjeXh4YPjw4Rg0aBB++uknm9trRVFw4MABm0EzKpUK3bt3L30fpZSIj4/HDz/8gPXr1yMpKQkGgwFqtRqRkZEYPHgwxo4dixYtWtjdR5BS4uTJk6WBf0ajESdPnrT7un7//Xf07dvX5vOhoaGIjo62ebG0yWRCSkoKNm7ciM2bN+P06dO4dOkS9Ho9FEWBEAIajQb+/v6IjIxE69at0blzZ3Ts2BHNmjVDQEAAVCoVL8auJYQQaNGiBf744w907doVOp3OZtujR4+ioKAA/v7+VVhh5ZBSYt68eZg2bZrDILk5c+bgiSee4DJdTtmFeuQWWa4zI+r7Qqt2n2Oj8iw3UkosX74cCQkJdtt5eHjg559/LneQ3PXUajW6d++OP//80+lp/PXXXzbDWIUQGD9+PG644QbcddddVtsoioKvv/4aH330UbV8x4QQ6N27N4YMGYLVq1fbbGcwGJCQkOB0mFxZ6vD09MTYsWPRt29fDB8+HKdOnbIbUDNv3jxMnTq10gMVyH1JKaHX67Fs2TJs2LABS5YswbBhw8r1XVUUBb///jsmTpyIzMxMp0PkbNUnpcSxY8eQmprq0jA5KSXeffddDB06tFzHScXFxZg3b55LXycR1TwajQbvvPMOli1bZjeM9tChQzCZTNBoKqdLoRAC999/P9577z2L4KNrZWZmIisrCxEREWbDpZR444037B4PCSHQrFmzcgXJXT++p6cnHn30URiNRh5TEdUwp0+fxsGDB82GaTQaLFiwAK1bt67w9BVFwUsvvYSNGzeahRd//vnnmD9/vtV1QlBQEMLDwxEfH186zGg0YseOHbj77rsrXJMjgwYNMgtMl1Ji5cqVmDJlSp3/nVetVuO5557D9u3bS1+/wWDA119/jXfeeQdCCCQmJmL79u1m44WHh2PAgAHVUHHV238+E1IqAMq/PesXw/ASIiK6SgJIz2PYNlF5RTf0g4+HBgW68oUxSgnsOpMGoHJuakRENUugrxfCgvyQcCnbZhspJbYfScCEW2MrfJzGMDkiIiIiIiIiIiIiIiIiIqIaZurUqViyZEnp3wkJCbj11luxZs0aBAUFlXt62dnZuO222xAfH292ocHkyZNdUi8RUW2291QKrl4ebZuPpxYRIYFVU1ANJKXE+++/b7cjLwC0atUKy5YtcypI7lpCCPj6+mLChAkVmk5lkVJi8eLFyMzMtNsuKCgIf/zxh1Mdm0t4eXlh3LhxNp83Go0YOXIkUlJSrD7v5+eH1NRU+Pr64sqVK3j22Wfx008/Qa/XW4RJJCUlYdeuXXj//fdx77334pNPPkH9+vVtXqT47LPPYu3atWV6HVJKzJgxw26b8ePH45tvvrE67r59+/D8889jx44dMBqNDoMwjh49irVr15bW7uXlhebNm+P555/H2LFj2UGuFmnTpg369OmDjRs32myTn5+PjIyMWh8mJ6XE3Llz8eyzzzoMkps7dy4mTpzIZdkJF7MKYVIs1yFtwuuBb6d1iqLg448/dhig+vTTT2Po0KEuWy6dDZRVFMVuaJIQAmPHjkVUVBQCAwNLg1GvJaXETz/9hP/85z/w8fFxqo6KUqlUGDlyJNasWWP3vU9ISECPHj0qtZZrA1tiY2ORlZVls21BQQG+/vprzJw5k+soqlRSSmRlZWHkyJFYsWIFbrnlljItc4qi4L333sOrr74Kk8l+qHhNtHPnTpw9e7ZcQSrr16+3ebxARHVLeHg4IiIicOHCBZttMjIyYDAYKi1MDrh6zDJw4EC7YXKKoiAjI8MiTO7y5ctYt26d3f0flUqFhQsXOvVbybWEENBqtRWaBhG5lpQSX3/9NRRFMRveoUMH3HfffS5bd5WEyV073+XLl2PWrFkIDLT8HUCj0WDYsGH46quvzALd5s+fj7vuuqtSA91KbuBScrOwEjt37kRaWhrCw8Mrbd72XP9ZlIQqV4YhQ4YgLCwMqamppfNasGABZs6cCW9vb3z99dcwGAyl7UtC5L29vSulnppESoltJy9ByvIff2tUAq0j3Pd3LyIiMiclrN6Ar2TrzjO9RNb5eWnQNNQPx5Kyyz3uwQtZMJoktBp+w4jqOo1ahQ7RDe2GyQFXr2FUFAm1umLrhbodO09EREREREREREREREREridU7vkAEBsbi5iYGMybN6+aPwSq62JjY3HrrbdCSlnaOWDPnj3o0KED/vjjj3JN648//kCHDh2we/fu0mmVdDzo379/Jb0CIqLaQUqJHYcvwFEfpyYNg+Dr7VE1RdVAubm5WLlypd3OYGq1Gl9//TUCAgJcNl8hRI0MITGZTPjiiy8chuq88847iIyMrPD8Kvo+KIqC06dPo3v37li0aBF0Op3N2qWUMBgMWLJkCbp164azZ89WWifAslAUBbNmzUKfPn2wZcsWGAyGctVT0omxqKgIR48exZ49eyqxWqoMQgiHIUkln3FtpigKPvnkE0ybNs1ukJynpycWLFjAILkKOH0xB9dv+IUAOjW1HZ7p7lJTU3HkyBG7bQIDA/HSSy/ViPcwJSUFO3futPl88+bN0bp1a/j7++Omm26y2S49PR3//PNPZZRYZo72I6SU0Ov1VVQN0KxZM8yaNcvu5yylxHfffecwhJjIVXQ6HR566CGkpaU5bKsoCt5++2288sorZQ6SK9kXV6lU0Gg00Gg0UKvV1XasYjQaMXv27DLvE5tMJrz//vvVuk9PRFVHrVajQYMGdtsYjUaLkCZXE0KgcePGDtsVFxdbDNuwYYPV4ddOe8CAAejTp0+N2PckItcqLCzEkiVLzPZdSoLBnA0ct6ZXr14IDQ01G5adnY1169ZZbS+EwMMPP2yx3tmyZQtOnDjhsrpsadmyJVq1amU2rLi42GHwe2WyFuhpL3i8Iry9vS3OhV26dAl///03ioqKsGDBArP3QavVYsKECW6xnVAksPfsZafGbRjojfp+ni6uiIiIaispJYqthMkRkX0qIdCjRYhT46ZlF+FKgc7FFRFRTXVjh6YO2ySlZ6NQZ3DYzhGGyRERERERERERERERERERlVFcXByOHz+OyZMnV3cp5AbmzJmD4OBgACi92P3ixYu488470apVK7z++utYt24dUlJSUFhYCOBqJ4uUlBSsW7cOr7/+Olq3bo0777wTKSkpZhfR16tXD59++mnVvygiohpGkRIHz19y2C62dQRUbtDxyJa9e/ciLy/PbptevXqhd+/eVVRR9UpNTcWpU6fstgkJCcGYMWOqvcOalBKbN2/G4MGDER8fX+bOhVJKJCYmYtCgQUhJSankKm3X8Ntvv+HFF1+EXq9nAIYbc2Vn4ZooOzsbH3/8MZ577jm7wTaenp744Ycf8OCDD1b7uqW2klJi/4VMSJi/fxJAhyjLTtB01e7du+0GgwkhMHLkSKsdyaualBI//vgjDAbrF9kLITBu3DhotVoIIfDEE0/Y/D5JKTF37txq3f4kJCTYfV4IAS8vryqq5ur87rvvPoSHh9ttFx8fj9TU1Cqqimq7qKgoDBs2zOwxZMgQxMbGon79sgV9ZmVl4eWXX7b7fZVSYvHixXjzzTcdhigJIaBWq9GpUye8/vrr2LRpE86cOYOkpCQkJycjPj4e+/btw88//4ypU6eiffv28PDwqJLts5QSS5cuLXNQx7FjxxAXF1fJVRFRTWEymXD5sv1AGY1GA5WqcrsTSilx/vx5h+28vb0txvvtt98c7n9Nmzat0l8DEVWPLVu2ID093WyYp6cnRo0a5dJ9LW9vb4tpSikxZ84cm/uK3bp1Q/Pmzc2GGQwGTJ482eYxqKtoNBpMnz7dot65c+fi2LFjlTpvW6yFnycmJlbKvIQQeOSRR+Dh8b8b/kgp8eGHH+LHH380O/4UQqBv375lCjWtC4r1Rly4lO/UuB2i6kOr5vaUiIiuMikSOYWWNy4R/z6IyDohBPrGNIQQ5f8tyWBScD7NuX05IqpdhBDoGdMYjraqhToDLmbmVnh+PNIjIiIiIiIiIiIiIiIiIiKqgZo1a4ZffvkFGo0GwNULCoQQkFLi7NmzeOuttzB8+HA0adIE/v7+UKvV8Pf3R5MmTTB8+HC89dZbOHPmDKSUZuN6eHhgxYoVaNmyZTW/QiKi6lesMyLx0hW7bQQEerVr7LbBPVJKrFu3zm4bIQQee+yxOh/4VGL//v12A59KQnV8fX2rsCrrioqKMHr0aFy8eLHcYThSSqSkpOC+++6DXm/ZeaCy6XQ6TJs2zeF7be1BdcvJkyftPi+EgFardXr6OTk5MBqN5X64KmDqiSeewAsvvGB3Wffy8sKSJUtc3nnaHe2/kGkxTCUEmof5V0M1NZ+UElu2bHG4vI8ePbpGLJtGoxHffvutzXpVKhXuvffe0lpjY2MREhJic3obNmxAZqblMlMVFEXBr7/+6vC9j4qKqqKKrvL29sbdd99t9/NWFAWHDh2qwqqoNnvrrbewdu1as8f69euxa9cuJCQk4Oeff0Z4eLjdZU5KiWXLluHKFdvHdomJiXj66aftbm+Bq/sVvXv3xs6dO7F371688sor6N+/P6KjoxEWFoaGDRsiMjISXbp0wd13342PPvoIBw8exPnz5/HJJ5+gTZs2lR5wlJubi8WLFztcP0gpMXv2bIevmYjqjqSkJIeB8KGhoWZhPJXBYDBg8+bNdtuo1Wo0aNDAbJjJZML+/fvtjufl5YVevXpVtEQiqoEURbF6I6wePXqgYcOGLp2XEAITJkyw2MeMi4uzGeqt1WrxzjvvmO3rSSmxdetWvPbaaxXe57K3byeEwOjRo9G0aVOzmouLizFq1CiHQaJlpShK6Q3M7BFCoH379hbv3/r16ystkD0yMhJDhgwxG7Z9+3ZMnTrVYp7Tp093m9DRtOwi5BY7F2bYu1Woi6shIqLaTJESBqOVUF3574OIbOoW3QDOxi4eiM9wbTFEVGO1CA+GRm1/XSElcDw+3W6bsnCPI2IiIiIiIiIiIiIiIiIiIqJaqH///li9ejVCQkJKL4S/NhjO0ePaQBUpJUJDQ7F69Wr079+/Ol8WEVGNcelKHvKKHIRkCaBby4iqKaiG2rNnj91OYCqVCgMGDKi6gqqRlBK7du1y2CnujjvuqBGhOoqioKCgoHS/wMfHB507d8bAgQPRvXt3BAQEOAzm2LlzJ77//nuL11yeEDdboW/2xj148CCSkpJsTi8mJgazZ8/G1q1bcfToUZw8eRI7d+7EH3/8gbfffhu33HILGjZsCJVKVSM+C3JORkYG/v77b7ttPDw8EBwc7PQ8xo4di9atW5f7sXPnTqfnWUJKiVOnTjkMkvvxxx9x5513clmuIINJ4kxKjsVwHw81GgZ6V0NFtcOxY8fsPq/RaNC+ffsqqsa+o0eP4syZMzafb9OmDZo1a1b6t4+PD2677Tab363CwkL89ttvldYZ3hYpJTZu3OgwhMXDw6PKw+SEEBg+fLjdNlJK7N+/v8rfN6qdPD09re4bqlQq+Pn5YdSoUdi+fbvDbX1BQQHi4uKsPqcoCqZMmYLc3Fy701CpVHjppZewadMmxMbGQqPRONz2CiGgVqsRERGBp556CocOHcKGDRvQs2dP+y+8AqSU+PTTTx0GPl++fBm//PILv4tEbsJgMOCFF16A0Wi0265Tp06VGrAjpcTChQvt7pMBQIMGDVC/fn2zYUVFRbh06ZLd8aKiohAYGFjhOomo5klNTcXmzZvN9l2EEJg8eXKlrLfatWtncTxlMBiwaNEiq/tPQgjceeedGDRokNk+opQS77//PmbMmAGdTleuGqSUKC4uxm+//Ybi4mK7bb29vfHll1+a3dBFSokzZ87gxhtvxOHDh53a75NSwmg04vDhw7jvvvvw1VdflWm8zp07WwTrLV26FOfOnauU/U+VSoXnnnvObJ4mkwkFBQVm7Ro1auQ2v1UAwInkbKfebwGJHq1CeK6RiIhKmRQJg8lKmBwROdS4gR+8tJpyjyclsPNUOs/fErmJ4EAfBPnbvyZBSoldxxMrvF4o/xqJiIiIiIiIiIiIiIiIiNybgLM30qu9/n29sbGxUKvVmDx5MiZPnly9NZHbGDx4MA4cOIDHHnsMq1evBgCHgS3XKrmw4Pbbb8eXX36JsLCwSquViKi2OZFwGY6uv9JqVGgSVq9K6qmJjEYjLly4YLdNYGAgQkJCHE6rIhe71aROTadOnbL7vEqlQkxMTBVVUzYqlQrjxo3Df/7zH4SEhEClUkFKiZycHPz3v//FBx98YDPMSkqJN954Aw888AB8fHxKh3/22WfIz88HcHU5efLJJ7Fr1y6bNcybNw99+/a1+XxQUJDZ3/ZCDPv3748///wT3t7eVpeNW2+9FQCg0+lw+vRpLF++HIsWLarUDvPkesXFxZg0aRJycizDv67VtGlTBAQEODUPKSUyMzORmZlZrvGEECgqKnJqntZqsMXb2xs//vgjRowYUaPWg7VVXpEel/MsO2eH1fOBjycvqbbGZDIhOTnZbhsfH58yBXoYjUZs27bNYcDJ9Tp06FCm41gpJebPn2/zOyWEwNixY8063gshMH78eCxYsMDmNL/88kuMHz/ebLyKUhTFZp2FhYX45ZdfMGXKFIfvVceOHS1CWKpCixYtSkPubUlMTKzCiqguE0KgadOmePHFF/Hcc8/ZXO5KQpCHDRtmsc08deoU1qxZY3eZFULgxRdfxJtvvun0PqMQAh4eHhg4cCC2bdsGg8Hg1HSu5eXlBYPBYLGvfu7cOWzfvh2DBw+2Op6UEt999x0KCwstntNqtQDgkvqIqGqUrL+srccURUFKSgqef/55rFixwuG67v7776/QsUXJjWysDc/Ly8Nnn32GN954w2EdgwYNgqenp9nw/Px8h2FKUVFRLt0vI6KaQUqJH3/80SKMzdfXF0OGDKmUeWq1Wjz00EN48803zdaz3377LWbMmAEvLy+LcTQaDRYuXIjY2FikpaWVjqcoCmbPno2NGzfigw8+wI033ggPDw+r61spJUwmEy5evIiff/4Zn332GdLT03Hx4kV4e9vuVC6EwJAhQ/Diiy/i7bffNqv5zJkz6NWrF5544glMmTIFjRs3tnmDjZLx9Ho9UlJSsGrVKixcuBCHDx+GoihlDkVu0qQJWrVqhRMnTpQOy83NRZ8+fTB16lQMHz4cDRo0gI+Pj8V6W61Ww9fXt9zbo969eyMqKsrm7xVCCDzyyCNWP7u6SEqJfefLd06zhEqlQtsIhrMSEdH/FOqM0BmthMnVsp+mHF2P4Irf2qSUUBQFRUVFyM7Ohk6ng6IoUKlU8PHxQb169cxuoFFT6qbK4+elQUR9H5xNs38zE2sOxWdBkYCaHzFRneeh0aBlRH1czi6w227f6ZQKz4tXPhARERERERERERERERERlVFcXJzTIQVEFdGoUSOsWrUKhw4dwgcffIC1a9ciKyvL4XjBwcG47bbbMH36dLRv374KKiUiqj2klIg7ZT8cBQBC6/mhnq/9O4PWZSaTCQUF9i9ka9CggUUHYGvy8vLwyCOP2Awts+X1119Hp06dyjVOZZFS4uLFi3bbaLXaMoXqVBUhBJ544gnMmTPHIkAnKCgI77zzDgIDA/HSSy/ZvFA9OTkZ27dvx7Bhw0rHbdq0aenzUkpER0fbDZMbNGgQWrduXaaapZR293Uef/xxs2C765VcUO/l5YWOHTuiQ4cOeOmll5CRkVGm+VPlUxQFWVlZ0GjML2MtCSDYuXMn3njjDRw4cKBMYQh1MUzA09MTP/30E26//XZ2EnGR+Mv50FvpDBUTGQS1iu+xNVJKh4EeAQEB8PDwcDitoqIi3H777VaDjexZsmQJ7r//foftCgoK8Msvv9hcZ2g0GqvhKV27dkVYWBhSU1Otjnfw4EFcuHABLVq0KFfdtphMJkyZMgVRUVFmw3NycpCcnIwDBw7g8uXLZeo8Nm3atGoJSvXx8YFKpYKiWOlc+K/s7OyqK4jqPCEEbr31Vjz//PN2vxunT5+2GCalxLx58+yGMwoh0KtXL7z66qsu+05pNBqL/RxnDBs2DEVFRfjrr78snnv//fcxcOBAqzUXFxdj7ty5Fu9Xyb5TXFwcTp48WeH6iKhq7N69G88884zZekVRFKSmpuLUqVM4ceIE9Hq9w/2Hhg0b2gyhLIuCggJMmjQJDRo0KB1WEtAdHx+PQ4cOITc312EdKpUKU6dOtdgvKywstLt/UfIaiKjuMRgMFuHgQggMHjzY4gYUriKEwOjRo/H222+bna9OTk5GXFyczZtihIeHY+3atRg0aBCysrLMQt0OHjyIYcOGITIyEoMHD0bPnj3RqFEjaDQa6PV6JCcnY9++fdi5cyfOnz8Po9EIKSV8fX3LXPOrr76K7OxszJs3r3SdKaVEUVERPv74Y8ybNw9t2rTBgAED0LlzZwQHB0Oj0cBoNCInJwenT5/GgQMHcOjQIaSmptoNO7dHo9HgpZdewkMPPWT2HqSnp2PmzJmYOXMmVCoV1Gq1xf5qz549sXHjxnKf7/Lw8MCUKVPw7LPPWq1Zq9Xi0UcfdavzaLtPZzi8YZI1gb4eaODvHqF7RERUNkaThKJYCQ7/9/+1YesqpcTSpUtx/Phxq8/7+vpi+vTpTp+zMxqNOHXqFJYsWYLVq1fj/PnzKCgoKN0nE0JApVIhMDAQbdq0wd13341Ro0YhIiLC4TnHw4cPY/ny5VafE0Lgueeeg7+/v1N1U+VTqwQ6RNVzKkwuMSMfRXoj/Ly0lVAZEdUkQgDdWkfin2NJdtudSrwMg9EED63zvzExTI6IiIiIiIiIiIiIiIiIiKiW6NSpE3744QcAwJEjR7B7926kp6cjMzMTeXl58Pf3R/369REWFoaePXsyQI6IyIG9J1McdpRqHRkMrabqQzpqCr1ej6KiIrttyhIgA1ztFLhy5Uro9fpy1TBp0qRyta9sjur39PQsU7heVYmMjMT7779vM2xLpVJh2rRp+P77721eXA8AixYtwtChQ6usM569+Rw4cAD33XdfmWsRQsDT0xMRERGuKo8qqLCwEJ06dYJWa35hvMlkQl5eHgwGQ5k6svr7++Pxxx+vk51E/fz80KVLlzr52qrLvnPWAyVvaBNSxZXUHlJKh4EeKpWqXMupM53Uy2Lr1q3IzMy0+XynTp0QGRlpMdzb2xv33HMPPv30U6u1mUwmLFiwAG+//bZLvo9SSpudwspKCIHOnTvjrrvuqpZ1RFk+85ycnCqqhtxFcHAw1Gq13XWStRBsnU6HFStW2F33qFQqfPLJJ2U+rqlKjRs3xj333IO///7b7LVLKbFlyxacP3/eatjlX3/9haQkyw5Jnp6emDFjBsaNG1epdRORa12+fBlz5syp0DSEEHj77bfh5+fn9DSMRiMWLVpU4TpuvfVWdOvWzeI5R/udAFCvXr0KzZ+IaqbDhw/j3LlzFsMnTZpUqcc80dHRiImJwZEjR0qHKYqCOXPm4MYbb7Q6byEEOnTogK1bt+K2225DfHy8WZialBKJiYlYsGABFixY4PKaNRoNPvroIzRp0gQzZ840CxOVUkKv1+Pw4cM4fPiwy+d9rZKQ4t9++81if7vk3yaTyeqNZcr7+8C18xw9ejRmzpxpdd+/X79+aNKkiVPTro2MisSJi1ecGrd5Q394auvezTmIiMh5RXojlEo6d1+Vli9fjl9++cXqc6GhoRZB7WWhKAq2bt2KF154Afv377cZxlvye0pmZiZ27NiBf/75BzNmzMCwYcPw1ltvoWPHjjZD5Y4ePYq33nrL6nNqtRpPPPEEw+RquN6tGuLX3YnlHi+/2IjLucUMkyNyA0IIxLaOgMD/wlqtuZxdiJyCYoTUc/48rvte5UhERERERERERERERERERFSLdejQARMmTMBLL72EDz/8EPPnz8eHH36Il19+GY8++iiD5IiIHDCZFJxKSnfYLratZegImRNCMOzoGjXpvRBC4OGHH4avr6/ddh4eHpg2bZrN2qWU2L59O4xGY2WUaUEIgYYNG9qs5+OPP8aMGTNw6tQpFBYWwmQylXbWpNpBSokrV64gPT3d7JGZmWnWCdYeIQTefPNNNGzYsAoqrnpZWVkYOnQoUlNTq7uUOkFKiV2n0nH9oiUA9GgRUqPW3TWJEMJm56YSRqOxTMEflUlRFMybN8/m80IIPPLII1ZfixACDz30kN1t4A8//ACdTueyeitCCIEGDRrgp59+qrbw2pLtrj3BwcFVVA25i/z8fIfrmutDagHgwoULuHTpkt3x2rVrhy5dulSovsrUu3dvtGnTxmK4wWDAnDlzLL6PJpMJ77//vsVwIQSGDx+Oli1bVmq9RFTzCCFw3333Ydy4cdW63yuEQHR0NL755hurgfeO9juBq8F6RFS3SCkxb948i32X4OBg3HDDDZU6b7VajQkTJlisG9euXYusrCyb4wkh0LZtW+zbtw/jx4+Hh4eH0+tXIQQCAwNt3gjEVt3Tp0/Hrl270LNnz3KHvFurQavVonfv3hg0aFCZx9NoNFiyZAnee+891K9fv0p+JwgJCcHIkSMt5iOEwPTp08u0LakrCooNSM8udmrcLs2CwVNhRER0rewCvdVgG/Hvw13l5eXhkUcewZAhQ7B3794ynRsvURL0++eff6JHjx548cUX+Vt2HSWEQLfmDSBE+T9fRUqcTc2thKqIqCbq2LwRHB2MGUwKEtMrdtMu9zkyJiIiIiIiIiIiIiIiIiLXUKnc8wEgNjYWMTExdjsoExERUe2QX6xHWla+3TYCArFtGrt1wIxWq4WXl5fdNkaj0a0ufHZ0t3K9Xl9loWtlcdtttzlchoUQGDhwoN3OdpcuXUJ+vv3vjCv17t3bZt16vR6zZs1CTEwMGjdujA4dOmDkyJF46aWXsHTpUhw/fhx5eXk27wxPtV9J+NPkyZPr7DpaSonTp09j4MCBDJRzAZMisfO0ZYisWi3QolFgNVRUO6hUKvj52b/reW5uLvR6fRVVZF16ejo2bdpkc53v4eGBESNG2FxftG/fHpGRtgOEk5OTsX//fpfUWhFCCERGRmLDhg3VGgaVk5MDk8lkt029evWqphhyG//884/DMLno6GiLYfv27bO7PyiEwF133VWu8I6qptVq8dxzz1msw0rCLnNyzDsVnThxArt377aYjhACzz33nMPjGSKqW1QqFcaMGYOFCxdW6/dfCIGYmBhs3rwZDRo0sNrGz8/P4fo4IyOjMsojompUUFCAXbt2oWHDhggLCyt9PPjggw5vkFFRQgiMGjUKkZGRZvP28/PDxo0bHY4bFBSEr776CgcOHMD48eMRHBzsMFCt5Hk/Pz/0798fixYtwvHjx+Hj41Pu2jt37oxt27Zh06ZNuP322xEQEFCmQLeS4PjAwED0798fc+bMwenTp7Ft2zZ07dq1XHV4eHjg+eefx4ULF7BmzRpMmzYNgwYNQqtWrRAREWH2vpY86tevX655XF/7mDFjLIaHh4ejf//+Tk/3el5eXhZ1l/czqmypVwqhM9o/NrdGCCC2OW+sQERE5nQGk/XQOPnvww1dvHgRN954IxYtWlSuELnrSSlhMBiwbds2/m5dhzUP84dKOBffdOBCJpcNIjcRERIIT62D36MkcPRCWoXmw1+BiIiIiIiIiIiIiIiIiIjKKC4uDgEBAdVdBhEREblAWlYeivT2A7+ECmjTJKSKKqqZNBoNfHx8kJWVZbNNZmYmDAZDjQ5gcBUhhM1OzyV0Oh1yc3NrxH6jVqtF06ZNy9Q2NDQU3t7eNgPj9Ho9srKyEBQU5MIKbWvbti1atWqFU6dOWb14WkoJKSWysrKQlZWFEydOYOXKlWYdMjt16oQxY8bgzjvvRIMGDdhBrg4QQkCr1WL69Ol44403KhyGIITArbfeih49epR7XGuBNa5WEig3YMAAbNq0CeHh4ZU+z7oqt8iAxIwCi+ENA70REuBZDRXVDiqVCo0bN8bx48dttiksLERWVpbD0LkS1tbFFekkI6XEihUrUFxcbLNN9+7d0ahRI5vPe3h44J577sHs2bNtbnM+++wzu0GnlUkIAQ8PD9x///2YNWtWaUBBdTl48KDd54UQaNmyJbe75DJ5eXl46623HIbCde/e3Wy5k1LiwIEDDtcxAwcOrPHL66hRo/D8888jMzPTbHh2djaWLVuGCRMmQAgBKSVmzZplNXgvJiYG3bt3r6qSiaiaCSEQERGB9957D/fff3+1nbMRQsDX1xdPPfUUZs6caTcYytfXF97e3jAYDDbbnD17FkajEVqttjLKJaJq4Ovri0OHDlkMV6vVVbKPFh4ejrNnz1oMt3fTjevbxcTE4Ouvv8Ynn3yC48ePY8+ePTh+/DiSk5Oh1+shhICXlxeioqLQtm1bdOnSBa1atUJAQECZ52OLRqNBv3790LdvX+Tl5eHUqVPYu3cvTp48ifj4eOh0OgBXj3vr16+P6OhoxMTElN6kw9fXt0wBdPYIIRAQEICbbroJw4YNAwAoimIzhLwi85NSYv369RbTe+yxxxzeFKc8br75ZiQkJJgNq2m/f5y6mOP0+ZQOUc4H+hERUd10Odf2+XV3lJGRgYEDB+LMmTMM+aIyCfb3QoC3BlcKbJ/TsEZK4FB8puOGRFQn+Pt4IqSeL5LSc2y2kZDYd/oiHhrW1eljZ4bJERERERERERERERERERERERGR2zmZeBnSsn+9GU+tBo2C/aumoBpKrVYjMjISycnJNttkZ2cjOzsbYWFhdqclhEBoaCj0en3psJIwMFsdy2qitm3bYs2aNTafVxQFZ8+eRWRkZBVWZZ1Wqy1zJzpPT0/4+/vbDJOTUqKoqMiV5dml1Wrx+eef46abbjJbZhwpCZnLzc3Ftm3bsH37dkyfPh1TpkzBzJkz4e3tXYlVU2UpCZHr378/3n//fXTu3NllHYpffPFF9O7d26maKqpkvZienm6zM4qUEmfOnMGAAQOwefNmNGrUqMYH3tREpy/moNhgua3p3Kw+PDQV67hd13Xu3Bnr1q2z+byiKDhw4ACaNGlidzo+Pj74+++/zQKOFEXBCy+8gH/++cfp+kwmEz7//HOb3yEhBG666Sa7oSQAMGDAAMyePdvqc1JKrFq1Cjk5OahXr57TtZZVyXfc09MT0dHRuOeeezBu3Dg0bdq02r//Ukr89ttvDjvQdenSpYoqorqqZJ8uJSUFY8eOxalTp+y212q1Vrfn9o5jgKvhH2UJX5ZS4ty5cw7XJddr0KABQkIqHlDu7++Phx56CB9//LHZ909KidmzZ2PcuHHw8PBARkYGli9fbvEdFULg+eefh0ajYQdYojqoZP9ACIHAwED07NkTjz76KG655Rb4+PhU6f5Dybx8fHzQtm1bjB49GqNHj0ZoaKjDOry9vREZGWk3yPjixYvIyspCw4YNXVo3EVWfkvDs2j7/kptb9OjRAz169LB7jFoZSgLdYmNjERsb6zCIubKUTFutVldK+FpBQQGWLFli9vo8PT3xyCOPuPR1qVSqal0uHZFS4lD8FTiza69Vq9AkxHa4KxERuafsQr317Yob/hyl0+kwatQoBslRuXhp1WjcwB9XCmzfpNGWY0nZUCSgdsPvG5G70apVaBXZwG6YHAAcPJ0KCec3wwyTIyIiIiIiIiIiIiIiIiIiIiIityKlxIEzqZCwf/FvWH0/+HrV3A5DVUEIgS5dumDXrl022xgMBhw4cAC33HKL3WkFBQXh7NmzFuMOHjwYe/bscUm9lU0Ige7du0MIYffi8bVr16J///7VHvgihChXDSqV/UClawOAKpsQAv3798fixYvx2GOPIScnx6kL9qWUyM/Px7vvvovdu3dj9erVNbojoLsoWTZtLZ8ajQZarRYhISFo164dhg8fjltuuQVNmjRxuJw6W091+fbbb7F27VrMnTvXbqDc2bNnMWDAAGzatAnh4eHVvn6pTaSU2HQ0FbjukmsBYGD78Ooqq1YQQqBPnz4Ot3s//fQTRowYYXe5VKvV6Nmzp9kwKSXat29foTC506dP48SJE3bbvP/++5g3b57dNjqdzu7zubm5WLNmDR544IFy13gtIQSmT5+OVq1aWTxX0lk9KCgITZo0QUREBAIDA6FWq2vMdz4jIwOrV6+220ar1aJ9+/ZVVBHVdgsWLEBmZqbZMCkl0tPTsW3bNuzcuRPFxcUOwzD69u2LiIgIi+lcvHjR7vxVKlWZwoaNRiOGDBmCxMREh22v9frrr+OVV16p8HdYCIGnn34an332mcX66vTp09i9ezduvPFGLFy4EAUFBRbjN2jQAHfccYfD9TkR1UxRUVF44YUXoNVqzYYLIaBWq+Hl5YWIiAhEREQgLCys0gLktFotXnnlFas3E1Cr1fDw8EBISAgaN26M8PBw+Pv7Q6VSlbkWlUqFnj172g2T0+v1+OuvvzBmzBinXwcRUVWo7mO46p5/ZVm/fj0uX75sNmzgwIEWxwLu4OCFTMeNrAjy9UA9H54fJyIicwXFRqvDS84i1c09C0tSSsyaNQvbtm1zeA6tJMy3Q4cOaNWqFby8vJCUlIQzZ87gwoUL0Ov1PA/nRlQCaN+4Hg4nlD9MLikjHzqDCT6ejH8icgddW0Xg7/3n7LaJT8uC0ajAQ+tcSDvXJkRERERERERERERERERUPkJ19eFO/n29sbGxUKvVmDx5MiZPnlzNRREREVFFHDhjP1QAAFo3bgCN2s32e64jhMDQoUPxxRdf2L3Y+euvv8bNN99st5OaEAKenp5mwzw8PODj4+OyeqtCz549oVKpYDKZrD4vpcTSpUvxxhtvWLzeqqYoSpkD4KSUMBqtdxQoodFU7SWHQgiMGjUK/fr1wyeffIKFCxciLS0NUspyX3wvpcTGjRvx5Zdf4qmnnqqzHSprCy8vL/z111+IjIy0eE6lUsHX1xdarRZeXl7QaDR1+vOKiIjA7NmzoVKpMGfOnDIFym3evJmBcuUgAaw9kAwpr3u/BDCgXSO+jw706NEDnp6eKC4utvq8lBKrVq3CpUuXrAaLVCYpJRYuXGh3WyelREFBgdVwpfLO67PPPsN9991XoVBLtVqN6dOnV/l75QpSSrz77rvIz8+3265du3Zo0KBBFVVFtd3atWuxdu3aCk1Dq9Xiv//9r9XvpsFgsDuuWq22CGeyxZl9UFeKiorCwIEDsW7dOrM6pJR477338MMPP1gNpxVCYPz48fD396/qkonIRTp27Ignn3yy2vdbfXx8MH369Eo7jyOEwIgRI7Bw4UK7x0UfffQR7r///io/R1Ey/xLV/XlQ3cfljcicyWTChx9+aDasJLC9Mm4+UZMpUuJ0aq5T4zYN9YOH1r3eLyIisk9KiaRM2+d83WlP9MKFC3j33Xcd3tgiMjIS7777Lm677Tb4+/ub7a+bTCZkZmZi5cqV+O9//4uzZ88yVM4NCCHQtXkwftxxHuX9uPOKjcgu0DFMjsgNCCHQrVW4wxv/ZOQWIq9Ih2Ctc+dhuTYhIiIiIiIiIiIiIiIiIiqjuLg4BAQEVHcZVEtt3brV6vB+/fqVuW1lsDZ/IqK6zqRInEnOcNiufXTtC/moDDfccAO8vLxQVFRk9XkpJVavXo34+Hg0a9asiqurepGRkYiMjERCQoLNNgkJCVi3bh1uv/32au3sqdPpkJeXh8DAQIdtCwsLkZtruwNaScBXVRNCIDQ0FG+//TZmzpyJ06dPY/Pmzfjnn39w9OhRpKamoqCgoDQoxN4Fl1JKzJ49GxMnTqz2oD93p9Fo0Llz52pZpmoijUaDWbNmQQiBOXPm2AzGklLi3Llz6NevH9atW4fmzZuzQ3kZ5BcZcCghy2K4n5cWLcJ4jO9ISEgIevfujU2bNtlsU1BQgNdeew2ff/55lXbgLi4uxuLFi6usI9aePXuQnJyMJk2aVMn8ahIpJdauXYt58+Y57Ej3xBNPuF1Hfqo+KpUKM2fORJcuXaw+r1ar7Y6vKIrDQOWaQqVSYcaMGVi/fr1FmNyGDRvw+OOPIykpyWI8T09PTJo0qdL2GRh0Q1WJy1vd169fP/j5+SEvL89mm4MHD+K3337DqFGjqmw5MJoUrD2QjJ92nEdOvgF924XhkUEtEezvyWWRXE5nMOG3PQn4dXc8inQKhnaKwNgBzRHgreXyRm4tLi4Ou3fvNtsfaNq0Kfr06VONVVWPYoMJF7OcC81v1yTIrUKBiIiobPKLrN+QwZ22GVJKvPzyyygsLLTZRgiBvn374tdff0VQUJDV/XONRoOGDRvi0UcfxZgxY7Bw4UK8+OKLlVk61RAdo+r/u69avm+OSZFIySpEeH3+bk7kDto0CYGj1EmdwYTUjFwEBzBMjoiIiIiIiIiIiIiIiIiIqMYaMGCAxUVkQgirHVatta0MtuZPRFTXFesMSM203SkVuHp5Z9eW4eygh6shMn379sX69ettttHpdJg0aRJWrlwJjaZuX5am1Woxbtw4vPXWWzbDVKSUePrpp9G3b18EBQVVcYX/oygKTp48icjISIdtL1y4gOLiYpvPe3t7V+trEULA29sbnTp1QqdOnTBlyhQoigK9Xo+8vDxcvHgRe/fuxQ8//IBt27bZDONKSkpCWloaoqKiqvgVENlXEigHwGGg3IULFzBw4EBs3LgRLVq04LbKgaOJ2ciz0hGqXWQ9+HnV7W2WK6hUKkyfPh2bN2+2u91bsGABhg0bhrvuuqvKlsldu3YhNTW1SuYFAAaDAUuWLMGMGTPc6nunKApWrVqFBx98sDS81ZbQ0FDcd999bvX+UPVRq9WYPn06XnzxRavLnBACYWH2A8JNJhP0en1llehyN9xwA1q2bIlTp06ZDTcYDPjll18s1tNCCAwdOhSNGzd2eS1SSmTl67Fg42nsOXMZvl4a3H9jNIZ0DIeagZLkYlJKpGUX4esNp3E4IRP1/Twxtn9L3NA6FCoVtzl1SVBQEO655x4sWLDA5r6noiiYNGkSunTpgubNmzs9Lykl8vLyHN5ESmcwYfTHm/HbnoTSfp6rDybhk1VHsXrmMHSMqs99H3KZ/GIDRrz3F7YcSytd3lbtT8THfx7BX6/egqahflzeyC0ZDAY899xzZufLhBB49tln3fKmIdn5euQXl/83fgGgU1Nut4iIyNKlHNu/EbuL1NRU/P777zaPRYUQ6NatG1atWgV/f3+H0xNCwMvLC48//jhuueUWvPvuu64umWqYZqH+UKlUUJTy3wDpVEoOYluEVEJVRFTTNAr2h1arht5gstlGSuBMSqbTN8HlLzRERERERERERERERERERERVSEpp9ihP28p4EBG5o8s5hcgvdhAYIATaRoVWTUE1nEqlwsyZM6GyEwggpcS6devw2muvwWSyfcFbXSCEwMSJE+Ht7W23XVJSEsaNGwedTuf0vBRFwY4dO5x+T6WUWLx4cZn2OZYtW2a3TbNmzeDra/tu2PaWDwAuXy6EEFCr1fD29kZoaCg6d+6MCRMm4O+//8ZLL71ks0Ocoii4dOmSS2shchW1Wo1Zs2bhmWeecbjOTUlJwcCBA3HmzBnu19shpcQfexMshgsBjIiNAvvOls3QoUPRvn17u52NDQYDHn74YaxYscJmGKIrSSkxd+7cSp/P9fP89ttvHQaq1RWKoiAxMRETJ07E3Xffjfz8fLvrGyEE3nzzTYeBLEQVJYRA48aNsXz5crz77rt2w6zDw8PtTktRFCQnJ7u6xEqj1Woxbdo0i/WxrfN8QgjMmDHD4b66M86k5qLzs79ixg9x+GVXPBZtOYtb//MXxs/dZrcTFFF5SSmx91wGOkxbgdd/3o/luxLw9d+nMfC11Xjh+zgYTZW/30FVRwiBl19+GV5eXnbbZWRkYPDgwTh9+nS5j4eklMjKysK0adPwxRdf2B1fSok5fx4zC5K7OhxIyynC0DfW4nDCFR6TkUtIKfHS4r1mQXJXhwPnL+VhwKurcSE9j8sbuR2j0YjXXnsN//zzj9nyHxYWhnHjxrllMFpKVgEU6cQ+kADaN66+G8YQEVHNlZnn/O/ZdUHJb+X2brzm4+ODH3/8sUxBctcSQiAqKgpz5851y/0Wd9LA3wteWnW5x5MSOJLEcwtE7iLA1wv1/exfbwZIHDyb6vR6gWFyRERERERERERERERERFQ+KuGeDwCxsbGIiYnBvHnzqvlDoNpMCFHmi8NK2lbGg4jInSVeugLp4G7AWo0K4cHluxC4LrvhhhvQr18/u9sQKSXee+89PPvssygsLKzTF7uGh4fjiSeecPh+rFq1Cvfccw+ysrLK9X4oioKTJ0/i7rvvxujRoysUxPbzzz8jMTHRbpvMzEx88803du+0PmzYMLshFB4eHnbnce7cOcfFXsPZ4Fu1Wo3x48fb/Wz0egdhkm6CAcM1k1qtxn//+98yBcpdvHgRgwYNKnegnDt99iZF4o+4RFz/UoUQuLlrhNsdGzn72Xt4eGDevHl2A5sAID8/Hw888AAmTpyI1NSyXeDubDDblStXsH79ervbLo1GAw8Pj3I/7C0X586dw+HDh52quaZJTExEXl5e6SMrKwvJycnYu3cvvvjiC9x8881o3bp1mQL0hBAYPHiww20wVb26sM4vOZfl7e2NPn36YOHChTh+/DhGjBhhd1sphEDnzp0dLpO7du1y+P6o1WrMmTMHS5YsKX0sXrwYgwYNcuo1OUsIgfvuuw/16tUrU/u2bduiR48eLq/DaFLw4MebkZJdWLqNlRJQFInF287h4XlbGShHLlNsMOG+2ZtwpUBvtryZFImP/zyG5xftgYmBcnVKs2bN8Oyzzzo855KYmIhevXrh66+/RlFRkcNQuJIA0TfffBMtW7bEnDlzYDQa7dZiNEl8sf6kxfHE1WkCmfk6DH1jDQPlyCWK9CYs2XbO6vIGCCRl5mPgq2sQn24/5JmotioJNE9ISEBCQgJOnjyJpUuXYvDgwXj//ffNlnshBN555x34+flVY8XV58zFfKfGE0IgKoS/exERkTkJIL/YPW6gYouiKFi0aJHd3xuefPJJNG/e3Ol5aDQanjuv43w8NQj2t3/NhC3Hk664uBoiqqm0ajWaNAx02O7I+TSn52H/F30iIiIiIiIiIiIiIiIiIioVFxeHgICA6i6DarHyBjwQEVHlOJl4GY7WskF+3gjw8aqSemqS6ztklVCr1fjyyy/RrVs35Ofb7qikKAo+/fRTrF69Gm+//TZuueUW+Pn5WYSZlswnPz8fV65U7oWxmZmZ+OGHHxAY6PhiPGv69u2LBg0amA0TQuC1117D8uXLkZiYaHO7XRIo1759e7z11lu46667EBgYaPX9kFIiPz8fu3btwty5c7Fu3ToYDAZEREQ4VXeJoqIiPPDAA1izZg0CAgIs5vvP4XhMm/QIMjIybE5DpVLhoYcesnuBe5MmTSCEsPlefPDBB+jfv7/Nu7VfP+3k5GQsXrwY48aNQ8OGDe0GhVzP3mcCAFqttszTqqv0BhOWbD+P3/ckQAjgrp5NcV+faGg1vEdxTVASKKdWq/Hhhx9CUayHY5QEyg0cOBAbN25Eq1atHAYuXMopwserjuFIwhU0CvLGpJtj0KVZ/TrbgSXxcgHOpuVaDA8N8ELrcOe2C7WRlBJxZzPw2boTuJxTjK7RwXh6eDuEBHiW+bO/8cYbMX36dIsO3NczGAz49ttvsWTJEgwYMAD33HMPunTpgqCgIPj4+ECn0yEvLw/x8fFYt24dVqxY4dRrWrlyJQoKCmw+HxQUhH/++QehoaHlmq5Op8Ptt9+OvXv3Wn1eSomvvvoK3bp1q/Xfm5dffhlvvPFG6d8mkwlGo7F0nVPW8xJCCERHR+PHH3/kNrYGURSJtQdSsHDzaRQUG9G3TRgm3dIGAT7OdWarLDExMYiNjbUYLoSAp6cnIiMjERkZiXbt2qF58+aoV69euW6U0K1bN7vPSymxbNkyPPXUU3anqVKpMHLkSIvh6enp2LhxY5lqcZXAwECMHTsWn376qd3vqRACzz33nMMgUGckZOTjYHwWrB1YSwks3XEBkMDCp/rBQ6t2+fzJvRyOz0LC5Xyr4UqKIvHpmhMAgA8e6gGNmsczdYEQAjNnzsS6deuwb98+u+dcrly5gscffxxvvfUW7rnnHtxyyy2IiopCYGAgTCYTcnNzkZycjJ07d+LPP//EgQMHoNfry7yfYzApyMrX2XxeSiAjrxhD31iDv167BR2jgmr9PiJVnyK90UGIx9VAuQGvrsamN29Bs1B/Lm9Up+Tl5SEmJgZFRUUA/ndMev06WwiBvn37YsyYMW75HZBS4tTFK5Cy/K/dS6NGsL9nJVRFRES1maJIXM4tru4yqlVmZiaOHTtm83kPDw9MnTrVLfc9qOw0aoHmYYFIyigs97jn0/KhSAk1lzGiOk8IoH2zMOw5mWK33emkDKfXCwyTIyIiIiIiIiIiIiIiIiIiqgIXLlyolLZERFQ+UkocOX/JYbvGoYHQatyr07veaMJ3m85ixe54aFQCDw1siTt7RJV2xm7ZsiXmzp2LRx99FCaTyeZ0pJQ4e/YsHnjgAQQEBKBTp07o0qULWrZsCU9PT+j1esTHx+P48eOIi4vD5cuXK/V15eTkYPz48U6NK4TAli1b0LdvX4vn/P39sWzZMgwYMACFhbYvCJZSIjU1FY899himTZuG2NhYdOvWDdHR0dBoNLhy5QrOnj2Lw4cP4/jx48jLy3NpqKyUErt27UK3bt3w1ltvoX///ggICEBBQSEWLP0Dr74yE4Zc+9+Jnj17IiYmxubzQgh06tTJbg3bt29H27Zt0bNnT0RERECt/t/3y8fHB2+88YZZAI1Op8PLL7+MN998E/369cODDz6Ivn37IiwsDB4eHlYv1lcUBSdPnsTEiRNt1qLRaBAeHm739dZ1eqOCuz74G2sOJJcGMvwel4iVexOxaEp/eDLwo0ZQq9V49913AcBhoFxqaioGDRrkMFAuPj0ffV5ehbScotLwlx+2nsMnj/TCxKGt61wnGCklVuyOh0mxXKcO7hAOLzdZ1qWU+HbTaUya/w+MJgkpgTUHkvHd5rPY+PotaB5WtgACIQTeeOMNnDhxAn/88YfdbZWUEkVFRVizZg3Wrl1bOr5KpYKiKDY7hJeVyWTCZ599ZnN8IQSGDx/uMGDRVu1PPvkkJkyYYHX6UkosX74cH374Ifz8/Jyqv6YwGo0wGo0VmoYQAs2aNcOGDRsswnep+kgp8cayA3hn+SEo/64D1x5MxndbzmDDazcjIti3miv8n5dffhkPPPCA3TYV2T61bNkS9evXR2Zmps02e/bswblz59CyZctyT786tp1CCDz99NP44osvoNfrbbYLCgrCXXfdVSk15hTooUgFgPVpSwks/ecCIICFkxkoRxWTkWe/Q/u1gXKzHuoBNQPl6gQvLy/88ccf6N27t8PAeCklkpKSMHv2bHz00Uel+50ASvc9nd3v1GpUCPb3Qk6h/YCvzHwdA+Wowrw9NPD39kBmnu0Aw5JAuYGvrsHmN4ejaagflzeqUxRFsXkODLi6L9y4cWP89NNPbh1mfjTZuRv0BAd4wseDsQJERGROkRI6veVv/yVHUe6wt3ny5Em759m6du3q9r8vU9m0bhSAzUdTyz1eem4R9EYF3h48r0VU1wkh0DG6kd2bdQJAalYedHojfLzKf5MoHvURERERERERERERERERERFVgaioqEppS0RE5Xcs3nGYXJsmIXCnfnhGk4IHP96MFbsTSsOlVh9IxkMDWmD+4zdCq1FBCIGxY8ciPT0d//d//2e3UxdwtTNvTk4Otm7diq1bt1bBq6haQgh0794dixcvxgMPPIDiYvsd3KWUyMvLw8aNG7Fx48Yqq7Fk3ufOncODDz4IjUYDLy8v6HQ6GAwGhx2qNRoNPvjgA2g09i837NGjB7Rarc0L7aWUuHjxIn799VeL54KDg/Hqq69a7QBYVFSEdevWYf369VCpVAgJCUF0dDSaNWuGqKgo+Pv7Q1EUJCUlYe/evTh8+LDd19WyZUuEhYXZfS113R9xiWZBcsDVwI9fdsVDQuL7KQMYKFdDlATKCSEwa9Ysh4FyAwcOxMaNG9G6tWUwnCIlnvpqFy5lF+Larj96o4Knv9kJAHUuUM6kSCzachbXrw6EAMb2b16nXqs92QV6TF+wBwaj+RuRnJmPQa+vxqY3hiO6YdkC5Tw8PLBkyRI8/PDD+OWXX8oUynFtcJyjfYeySkxMxIEDB+y2efTRR536jIUQuPXWW+Hl5YWioiKrbbKysrBp0ybcfvvt5Z5+XSKEQL9+/bBs2TKEhIRUdzl0jQvpeXj/18OlQXLA1W39qdRsDHp9NTa+MRwR9WtGoJxGo6nU9bGPjw+GDBmCn3/+2eY6y2AwYObMmfjxxx9Lw4dquujoaPTv3x8bNmyw+rqEEBg/fjz8/f0rZf4R9X3gqVGj2GB7vS4lsHTH1RtmMFCOKiI6LAAqIWCys9/xv0A5gVnjYqGuJd9lsi8sLAxbtmzBzTffjFOnTpV539OV+50alcDEIW3w4pI4i+MK8/kCGfnFDJSjCvH2UOOBPi0wb90xu8vb1UC5Agx4dTW2vDUcUSEMlKO6ryQodODAgfjuu+/c+vyuIoELaXlOjds0xA9qNdcXRERkTm9UUKCr2E1Harv9+/fbvXnNiBEjuM9NDgkhENO4HoSAg2M6S7mFeuQWGeDN4F8it9C2qeMbdOUV6pCdX+RUmBzPjhMRERERERERERERERFR+QiVez4AxMbGIiYmBvPmzavmD4GIiIicZTQpuJB6xWG7jtHu1Rlp67FLZkFywNULXBdtPovHPt8OvfFqJ1yVSoXp06fjk08+gaenZzVVW3MIITBy5Ej8+uuvCAqqeR2FNRoN2rVrZxYqZzAYkJeXB71e77AjthACTz75JHr16uVwXmFhYRg0aFClvQdSSphMJqSlpeGff/7B4sWL8Z///AcvvvgiXn75ZXzxxRfYu3ev3delUqnw+uuvOwzGq8uklPhjb4LVC9ilBJbvSsCYjzdDZzBVfXFklVqtxn/+8x+88MILdgNupJRIS0vDwIEDcfLkSYvvgcGoYO/5y5Cw/I4aTRJPf7MTX663HK82O5eWhxPJ2RbDA7090Lt1aNUXVE2OJ2cjr9hg5RmB5MwCDHxtNc6l5ZX5s/fx8cHixYvx5ptvwtPTs9LW+yUdxb29vc2GSymxaNEiGI22O7eFhoYiNjbW6XmHhISgZ8+eNp+XUuLTTz91WUhJbSOEQHBwMObMmYP169czSK4GijuTCYPJyvIpBc6k5WLQa6uRnFlQ9YVVAyEEnnnmGbvrKiklli9fjqVLl9aa7aBKpcL//d//2XxdWq0WTz31VKWto0MCvXFvn2iHAewlgXIPz9sKPfcvyUmtwgIwIKaRw+XtaqDccTz3XRxM1taBVOsIIRAVFYUdO3bgjjvuqJbATyEEpt4Wg2GdIiCEg22EFMjM12HoG2twJPFKrdmmUM0hhMB/xnRF9xbBjpc3AEmZ+ej/ymrEp+dzeatE14ZUGgwG6PV6GI1GKIpS+hy5hlqtRqdOndC5c+fSR9euXTFkyBDMnDkT+/fvx9q1axEeHl7jzsNXJYNJQVqO9fB7R1qEBVg5M0hERO7OYFSgt3IcLQC32G5IKXHkyBG7bfr06ePW+x9Udq0j6jk1nlGRyMjRubYYIqqxmocHOzzfbTIpuJjhXJA4w+SIiIiIiIiIiIiIiIiIiMooLi4Ox48fx+TJk6u7FKqFDh8+XPo4c+ZMdZdDROS2inQGZObaD04QEIhp2tBtLgiWUmL7yTQbzwHfbz2LiV9sh+GaQLnJkyfj77//RnR0dKW8TyUBMq1atULTpk1dPn1XEkLgpptuwt69e9GzZ89Kez+c4enpiWXLlmHIkCFOTUMV1AT3Pjq1TOOqVCp8+umnqFevXo387gghMH78eNx11101sr6q5OdpO0xPSmDFngSMncNAuZpErVbj7bffxowZMxwGyl26dAmDBg2yCJQTAtCobC/7RpPElG93Yf5fp+pER2gpJeb/dQqKYvlahnWOgJ+Xthqqqh5eWrWdZwWSMwsx6PXVOH+p7IFyWq0WL7/8Mvbs2YMbb7wRKpXKZetWIQR8fHxw5513YufOnbjtttvMntfr9Vi4cKHNWoUQuPvuu+Hj4+N0DSqVChMnTrT7mrZv345Lly45PY/apmTfrFmzZvjPf/6D06dPY/LkyfDw8Kju0sgKbw8733spcCY1D4NfX42ULPcIlOvevTt69epl9zttMpnw2GOPYfXq1bVmO9inTx80b97cYrgQAkOHDkWTJk0qbd4qITDvsd4Y2N5xwFdJoNwj87YxUI6colarsPS5gegcVb/sgXKLGChXl9SvXx/Lli3D0qVL0bRpU5fsdwoh0LBhQ3Tu3Nnh9Dy1avzy/CAM7tTIYcCXlEBmvg5D3liDwwkMlKPy8/PSYu3Mm9C1eVkC5QSSMgsw4NXVSLjMQDlXkVKioKAABw8exJdffoknnngCgwcPRteuXdGyZUtER0ejbdu26NGjB0aMGIGXXnoJK1euxMWLF2E0Gt3qc5BSIjU1FcnJyRaPoqLyh535+vpix44d2LdvX+kjLi4O69evxxtvvIGOHTtCrbZ3jsM9FOqMyCnQOzVuq/BAtz83TkRElooNJhiN7n0MnZKSYvM5lUpVqefZqG5p0sDXqf0tKeE25+uJCAgO8IW3h6ObYAqcvZjp1PQZJkdERERERERERERERERERFQFOnfujC5duqBLly64//77q7scIiK3lZVXhCK90X4jATRrFFQ1BdUAQgiE1bMduCIl8P2Ws3js8+3Q/3shuRACN9xwAw4fPowPPvgADRu6JnxPCIGAgADccccd2LhxI44cOYIWLVpUeLqVTQiB6OhobN26FT/88ANatGhRofdDCAG1Wo3u3bvjiy++gEbj6CJC2yIiIvDbb79hzJgxdkOoLAS3gBL7GO6fG4czqbll6gjZvHlzbNy4Ea1bt4YQosLLhKum4e3tjVdffRWff/6523c4FEJgdL8WUNsJFZMSWL4rAWM+YaBcTVISKPfee++VKVBu4MCBOH78eOl3V6tW4c6eUXYDOIwmiae/2Ykv15+s9Z2fC4qN+GHrGVz/KoQAJg5t7VYdZ9tG1kNksK/dzz45sxADX1uNc2llD5QTQqBjx47YtGkTduzYgVGjRiEgIKDc6+6SkLKgoCAMHz4c33//PeLj4/HLL7+gR48eFuvt/fv3IzExsXQ+1h4PP/xwhT/jm266Cb6+vjbnUVxcjBUrVtgNtbP2qGplnee1NarVanh6eqJBgwZo164d7rnnHnzyySc4dOgQTp48iRkzZqB+/fpu9T2qbW5oE4pAb/tBf2dS8zDoNfcIlNNoNPj000+h1doPEi0oKMCdd96JGTNmICsry+H6UEoJRam+jrYeHv/P3n2HN1X1cQD/npvZ3UJpS4FSZhFkW5UNZcgQEVCmuMBXhuIrKCoqUOV1MBwoIAoCKiCo7L33Kntv2jI66V5Z97x/1ERidpqu5Pd5nj6Qm3PvPUlubu4453vkeP/991G9enWjv7CwMEyePNmxY38n+ChkWPdeN7sD5VYcuoWX5+6nQDnilCq+Cmyf2tOtA+WsHdtUlHqU52+/RCLBwIEDcenSJaxYsQLt2rWDUql0+LhToVDgySefxNKlS3H9+nX06tXLrnl9FDKsebe73YFy6TlF6E6BcsRJQT5KbP+4p52BcsCdB3no9PFmxKdSoFxJaLVaHDt2DC+++CIiIyPRqlUrjBkzBj/++CP27NmDs2fPIiEhAffu3cONGzdw8uRJbNy4EV9++SX69euHyMhING/eHJ999hkSEhLK9TixrCQlJaFBgwaIjIw0+Zs8ebLD26P++sC//8r7N6iiycpXQ61z/LvOwNEw3L8UakQIIaSyyy7QQGdyNwUA//vPzXHOkZxsfuA9oHhwHX9/+g0l9gn2U1od3MsSxoAbyTmlUCNCSEXkpZAiyNfLahkOjou3U5y61uN8CzNCCCGEEEIIIYQQQgghhBBCCCGEOER/Y9/WDf6HO8u3aNECJ0+eLNV6EUKIJ7mXlgNb7axkUgHVAn3KpkIVxDOP18IHy+TIzFebfZ5z4Nf9NwAAP41pD5m0uBOXj48PJkyYgNdffx3btm3D4sWLcfjwYWRnZ4NzbvU3T98BTKFQoFatWujcuTOeffZZtGnTBoGBgXZ3EPP390e1atUcfMX2sxU68e+yQ4cOxcCBA3HkyBEsWrQIu3btQmpqKkRRtPl+yGQyREZGon///njhhRfQqFGjEgXJ6Zfr7e2NxYsXY8CAAZg8eTKuXr1q+fNRBgCR7YH63cHlvkjKKkDM1C3YHdsLDar7W/1cGGNo3rw5zpw5g4MHD2LVqlU4efIkUlJSoFarLb7+oCDT8MbIyEgcO3YMO3fuxPbt23Hx4kVkZGRAqy0Og7T1XgqCgPDwcDz33HMYP348ateuTZ0O/9a2YQje7dcUM9adhyiafx85B1YfS8CIOXvx6/jOUMgcC+Hz8fGx+r309fV1aHnOkkqlCA4OtlrGke+4q9evD22ylyAImDhxIgRBwIwZM6x+D0RRxKBBg7B+/XrUq1cPjDH8b/hjOHQlFWcTHgAw/33Q6jjG/3wUjLFKG7rGOcea4wlIz1WZPFejig/aRoWUQ63Kj5dcgt/f7oye07chr0hr8Tjo7oN8xEzbjD2xvVE31M/uz14ikeDJJ5/EqlWrkJOTg/Pnz2PPnj04ceIEbt++jczMTGg0GoiiCEEQIJPJ4Ofnhxo1auCRRx5BdHQ0WrZsicjISEN4mzU1a9bEgQMHLD4vCAJatGhhV92tCQoKwoEDB1BQUGCxTEBAgMm0Nm3a4ODBgxbnYYyhSpUqJa6fLX5+fti9ew9+3XoMP647bHF/D8YAJsDLS4Hv3x6Ati0awc/PD0qlEt7e3pDJZIbO+6TyqOqnwI9j2uGFb/cZwqDNuZ6cg5ipm7E7tjdqVHHv85+WLVviww8/xLRp06z+fmo0GsyaNQuLFi3CwIEDMWTIEDRq1Aj+/v6QSCTgnEOtVuPBgweIi4vDwoULy/BVGNOHZw4fPtxkemke3zzMR1kcKPfMFzux92KS1XNtzoGVh25DAMPP4zpA7uDxJSFV/ZTYPrUnesRuxZmEDKvbmz5QDgBmvRgNiaR0wxVLgjGGn376CXl5eRbLmDtnLg1DhgzBk08+afF5feBsedGHxQ8ePBjPP/880tLScPz4cezfvx+nT59GYmIi8vPzDdcNpFIpvL29Ubt2bbRq1QqdOnXCY489huDgYKeOb/SBcv1n7sCus0ng3Nr8DA/yVOj+yRbsmNILzWoH0fEUcYg+UK7Hp1tx6uYDm9vbnQd56DxlM/Z92hu1q/nS9uYAURRx6NAhvPPOOzhx4oTNa9r/pi+r0Whw6dIlfPzxx/jkk0/w9NNPY/r06WjUqJFbfh6cc/z+++8oKCgw+34tX74c06dPh4+Pe59nlIekzELngiMZQ50QP9dXiBBCSKVXUKQpvnBj4b6Ru+OcQ6PRWHxeLpeX2bU2Uvn5KKXw85LhgZl7lNZwDly7X9zGxh3PHwghxqRSCSJCA3E33XqI5KX4VKeWzzgNN0AIIYSQh+Tk5CAgIACZZ1bA38/yyPPuiPmElncVyo/goRczWMVtHFKqdI5diCBugHlmwzueebO8q1B+PPV77qkXSwXro4m7tVIeRbvC8uTP3FN56v7NQ49hAHjuuYqnEmhb9xQ5ufkIavIssrOzK/UohYbrhzu/hL+PsryrU6Zy8osQ1O09NGzYEBKJBOPGjcO4cePKu1qkknm4c1Lz5s1x6tQpq2X1WrRoYbUsIYQQx/y8+QRem7na6iDawQHeSFz1HpRyz7mPxjnHngtJePbLnchXWQ6aYQx4qXN9/PB6e8ilpsf1nHMUFBTg7t27uHr1Kq5evYq0tDQkJSVBp9NBLpejRo0aCAoKQv369VG3bl3UrFnTEM7gaMNYzjm0Wi1E0XJIRknpQ1ScwTmHSqVCUlISrl69iitXriAtLQ337t2DRqOBXC5HrVq1UKNGDTRp0gQNGjRAlSpVIJVK7Xov1Go16tati3v37pl93tfXF0lJSYbgLn1j+OvXr+PgwYO4cuUKEu7cx7aTN1DIfMGr1AVCGgNeVYq7CzxUh/Agb7sC5f79+jnn0Ol0Vj8jfeCFueXqmziqVCpkZmbi/v37uH37Nm7dumV4rNFoIJPJUKNGDVSrVg2NGjVCo0aNEB4eDrlcTg2uzdCJHB8uP4FZ6y9YDhhC8SYw8MnaDgfKabVa6HQ6K8u1/Jm7kiiKVjuAACX7jpfX+vVBNvaQSCRGoZTpOUXoOm0zLtzJshrAIZUwfD+yDV6rhIFyaq0OLd9Zh8t3s4ymMwZMG9QSHz3XotK9ppLinOPQlRT0+Wy71UA5AKhZ1dvhQDlz6wOKvwP6Pz1BEAx/ADzusyhrOlHEe7/G4ZtNl2zu73u1rIk/3omBl5zGq3cHnHMsP3ATI+cdtBooBwANqvthT2wfhFdxbZvRBw8eIDw83Opv1sqVKzFo0CCXrtcSjUaDIUOGYM2aNXaFL+j3TxKJBF5eXpBKpYbje5VKZVfQSGxsLD7++GOL+7ply5bhhRdesDj/uHHj8N1337l0XymKIh5//HGLg1cwxnD58mVERUXZvcy8Ig362REoV7x8YGi7uvj5jQ6QSz34Phlx2oPcInSL3YJzCZk2tzdBYBjfqzFmvhQNiae2M/IA+n2x/tqD/rE+aF4f4O2qfWm+SmNnoBwAcAT7K7Fzai80jaBAOeK4zPwi9Ph0C07dzLBre6sV7It9n1CgnL1ycnLw9ttvY+nSpTYHAnGU/trXK6+8ghkzZlTqdiLmaDQaNGvWDFeuXDH7PGMMGzZsQJ8+fcq4Zu5v2f6bePG7vXbsE4xJBIaEHwYh3M2DxAkhhDhu86k76Pv5Dpvn2AHeMtxfOLTCXj/mnGPQoEH4888/zT4fEhKCxMREk5B0nU6HRx991OJxTUBAAO7du1eqIbnWrhFKJBLcuXMH1atXL7X1E9fRiSIefXs1rt6zHhBlTr/oCKye1JXO5QjxAJxzjJq5Gou3WB9kvEX96ohbMM7ha9sV85eaEEIIIYQQQgghhBBCCCGEEEIqoLi4OLdr6E3Knr0dEahhECGEuB7nHJfiU60GyQFAjWB/yDysUztjDF0erY4173VD/y93Iq9Ia7Yc58DSvTcgisCPY0wD5Rhj8PHxQVRUlCF8wNxvn6t+5/Sd8ioqxhiUSiXq1KmDOnXqoGfPnqX6fthTH7lcjiZNmqBJkyaGuuw4ew8DZ+5CfpHW4uAGSVkFiJm6xaFAOcaYofN2SeoMAEqlEtWrV0f16tXRunVrAKW7bbk7icDwv2GPgYFh5vrzFgOGOAf+OpoAYK9DgXJSqdQowKy8CIJg0inEHdbPGHN6ucH+Suya1hvdY7fibMIDAOa/M1odxxuLjoAD+E8lCpTjnGPr6bu4ci/L5DmlTIqRXSvPa3ElxhjaNQrFpsk9bAbK3X2Qjy5TN2N3bG/UczJQ7uEAJn14BykfEkHAlyOiAcBqoBznwJbTd/H8rN34Y2IMvBTlvw8nJcMYw7AO9cAYw6tzD1gNlLuenIMuUzdhd2xv1HDjYAGZTIbffvsNw4YNw7p162xeo9M/r9VqkZubWxZVrLR8lTKsf78bnrEjUI5zYMWhWwBAgXLEKVX9ioO5esRuxZmEDKvbmyhyzNlyCQAw88VoSCQUKOeO9MeeZXUO6qOQYc273e0MlGN4kKtCt9gt2DGlF5rVpkA54pggHyW2f9wLPT7dilM3H9jc3u6k56HTx5ux99PeiKRAOYs450hMTETv3r1x+fJli9cYBUFAjRo18OijjyIqKgqRkZGQy+XQ6XS4f/8+rl+/jgsXLuD27duGsOGH16FWq/Hjjz+iWbNmGDt2bFm+xFJ35swZXLt2zeLznHPMmTMHvXr1KrVBHDwR5xy3UnIdDpIDAC+5BL5eFfdeCiGEkPKTW6ixGSTn7qwdr7g6dJi4N4ExRFT1dSpMLjEtD5xbbK5BCHEjjDE0iQwBY8zqb8z99BxotDpI5I6dV9NZOCGEEEIIIYQQQgghhBBCCCGEEFIGvL29Df8vLCy0ax5qjEYIIa53/W6azTJ1wwIheGALTcYYYv4OlPNVSi02UuUc+HX/Dbz+w0GroRQPL/fff56sIr0f+vV3b14Df77bFT5Kyx2vOQfu/x0odz0pp0Icp1Sk97IykggM04e1xjvPPApBsPze6QPlRszZC5VGV4Y1JKUl2F+JHVN7omlEEJiViFWtjuPNRUfw046rFeI7b48ijQ7vLI0zmc4Y0P+JCFQP8iqHWlUMDwfKWfudBxjuPshHzNTNf3dQrhyfPbFMHyj3Vp/GNvf3W07fxfOzd6NQbT5YmFQujDEMbV8Xi8aahkAb4QzXk3MQM3Uz7mcUlF0Fy4GXlxdWrVqF9957DzKZrFSPHxljHhWo6aOUYd373dC5SXWbHR71gXKvfn8Aai0dXxLHVfVTYvvUnn8Hc1kvqw+Ue/eXOOhE29cwCLGHPlCua/PqYMxWOCmQnlOE7p9swfnETDq+Jg4rDpTriVb1qtjc3gCGOw/y0HnKZiSk5dH2ZgbnHNeuXUObNm3MBskxxlCjRg18+umnuHLlCq5fv46NGzdi9uzZGD9+PEaPHo1x48Zh+vTpWLlyJc6dO4eEhAT8/vvv6NSpk8kxJuccOp17He9wzvHDDz8YvXfmrs3u378fycnJZV09t3cn3bmg60BvOZQyCo4nhBBi6k56XnlXoVzpB2OzRK1WQ6VSlWGNSGVXJ9TPqflSsoug1dG1K0I8RcOa1cBtDIebXVCE/CKNw8umMDlCCCGEEEIIIYQQQgghhBBCCCGkDAQGBhr+f/fuXerAQQgh5UDkHAkpWTbL1a8ZXPqVqaD0gXKrJ3WFj8J6oNwv+4oD5TR2BMqRio0xhh5/B8p5y60EXvwdKNd16hbcSKaAIXcgERj+N+wxuwPlXpyzjwLl3ESwvxK7pvXGo3YEyr1RSQLlOOdYtOsabqbk4N9VlUoYpjzf0uNDJ/WBchvtDJTrQoFybkMiCJgxIhr/tTNQbtAsCpRzF4wxDOtQz+5AuS5TN+FeRn7ZVbAcyGQyfPbZZ9i3bx+aNWvm0t8GxhgEQUC9evXwzTffYPz48R712+NrCJQLo0A5Uuqq+imxc2ovhwLlJlGgHHEhfaBcTDPbgXIAQ3pOEbrFbsEFCpQjTgjyUWLbR73Qql5V+wLl0vPQZSoFypmTmpqKnj17Ijk52SQMzcfHB19++SWuXr2KyZMno379+pDL5WaD0vTTpFIpQkJCMGjQIOzevRtnzpxB37593TpUOCcnB2vWrDF6//z9/dGuXTuj90mlUmH58uW0DbrYrRTnAn+qV/GG1Mr1gNLAOQfnHIWFhbh//z7Onj2LrVu3Ys2aNVi9ejU2bNiA48eP4/bt28jJyYFOp3Pp9iKKIvLz8xEfH4+jR49iw4YNWL16NdasWYONGzfi2LFjuHLlClJTU1FYWOjy9RNCSGXAOUd2gWlIDf/7zxPow4Qt0Wg0yMnJKcMakcouspqvU/PlFqqhorY3hHiMOtWDwGD9HE2jFZGR4/ggUBQmRwghhBBCCCGEEEIIIYQQQghxDAMgMM/6+/t+bXR0NBo3boy5c+eW60dAKqcGDRoYGh8XFBRg7dq15VshQgjxQFqtiOQM6x1tGBgaRYR4VIf/f2OMoWvTcKx5rxt8FFKL5fSBcv+hQDm3oA+U+2uSfYFyMVM243pSDnWucgOOBMr9eTSeAuXcyMOBcta6BVWWQLl7GQWYsuKUSZAcwNH/iUjUr+5fHtWqcBhjaE+Bch5JIgj4ckQ03rIjUG7z6bt4ngLl3IajgXIxUzfjfobjnVMqE8YY2rRpg+PHj2P9+vXo0KGDISjEmWVJJBJERERg3LhxOHr0KC5duoQ333wTfn5+pVD7iq04UK47OjepToFypNTpA+Wa165iV6Dct5sv4d2lFChHXMdHIcPaSd3Rtbl9gXIPclXoGrsF5ylQjjihiq8S2z/uiVb1qti1vSWk5aEzBcoZUavVeP7555GQkGASJFe7dm0cPnwY77zzDnx8fJw6LhQEAY0bN8aaNWuwZcsWREREuOV9hq1btyIrK8vwmDGGgQMHYt68eUYhepxz/PTTT9BoTANqiHNEDqTkFDo1b40gb5vHS64iiiISExMxd+5cdOvWDbVr10ZkZCRatmyJ3r17Y8CAARg4cCD69euHJ598EvXr10eNGjXw2GOPYfLkyThx4gRUKpVT+y7OObKysrB48WJ07doVNWrUQL169dC2bVv069cPAwcOxIABA/DMM8+gTZs2aNKkCWrVqoWaNWuidevWeOmllzB//nycPXsWubm5EOm4kRDiAe6ku/fAEvaoVauWxec457h161YZ1oZUZowxRIb6OXXcVajRIb+Ijp0J8RTBgT6QSqzvLLgI3EnNdnjZllv6EUIIIYQQQgghhBBCCCGEEEIIMRIXFwd/f+p8TpzTrl077Nu3D4wxcM7x2muvITs7GwMGDKDtihBCyki+So2cApWNUhz1a1Qtk/pUZIwxxDxaHasndcWAGbuQr9KaCef5J1COMWDB6+0hsxZOQSq8hwPlBs7YhQK1+UAHzoF7WQXoOnULdn/SG/XD/NyyY6Qn0QfKcQ7M3nABomi+o54+UA4AfhnfCQqZleBBUinoA+W6TtuMC3eyzO7rgX8C5QDgte5RFe47r9GKeHnOAWQVqIB/jeDtJZfif0NbQ6hgdS5PDwfKPf3ZduQVmf+dfzhQbk9sb9QNpf19ZScRBMwYEQ0A+HbTJav7+y2n72LQrN1Y9U4MvOTU7aCy0wfKAcDIeQehthQG/XegXJepm7Antg/Cq3g7vU5vb2/MnDnTauf7Fi1aOL18V5DL5Xj66afRu3dv3L9/H/v27cOOHTtw+vRp3L9/HwUFBVCr1YbygiBAqVQiMDAQtWrVQvPmzdGhQwdER0ejVq1akMlkDu0nW7duja+//tri882aNSvR6zOHMYYJEyYgNTXVYpmQkJASraM4UK4b+n2+A3svJVs8vgD+CZQDgJ/f6AC5lI4viWOq+imxY2pPdIvdgnMJmVa3N1HkmLPlEhgDZrwYDYlA1zBIyfkoZFjzbnc8O2MHdp9NthLTXbzPS88pQrfYLdg1tRcejQii42vikCAfJbZ91AtPTd+CUzczwLm17YchMS3PcD5Xu5qvR29vnHN88803OHjwoEmQXEREBA4ePIjw8HCXvEeCIKBbt244ffo0RowY4Vbvu06nw3fffWfyHo4ePRqNGzdGixYtcOLECcNzN27cwLlz5/DYY4+VR3Xdjk4UkZFj6x6XeTWr+ri4NqZEUcSxY8cQGxuLPXv2QKPRWA2E0z/HOUdeXh7OnDmDs2fPYsaMGYiMjMQbb7yBkSNH2t2WQqPR4IcffkBsbCwyMjLsXrdarUZGRgYyMjJw9uxZ/PbbbwAAf39/xMbGYvz48W71PSaEkH97kFdkMs2T9nqMMTRt2tTQns+cvXv3IiYmhn4PiF0iqnqjeBAvx7YXnciRka9CWJDz1+QJIZWHn5ccPgo5srSmv8MPu3k/HTGt6jm0bMZpWAFCCCGEPCQnJwcBAQHIPLMC/n6edcLBfELLuwrlR5CVdw3KB/PQRiA6524ekUqMeWYDO555s7yrUH489XvuqRflBXl516D8eGqDTk/+zD2Vp+7fPPQYBoDnnqt4KoG2dU+Rk5uPoCbPIjs7u1IHRhmuH+76Ev6+XuVdnTKVk1eIoK7vVfrPkJSvGzduoFGjRkYNk/WNzKpWrWo0yn18fLyhkZpcLkd4eHip1Ikxhps3Pfg6CiHE49y8/wBRI76yGJoBAILAcPWXCahHgXIAin+vdp67j4EzdyGvSGuxHGPAS53rU6BcGVOr1ahbty7u3btn9nlfX18kJSXB19fXoeVyzrHtzD08N9NyoBxQ/LnXCPLGrlgKlHMXOpFj8rITVgPlgOLP/rkn6+CX8R0pUM5NpOUUotu0LTifmAlrnRukEoa5o9pgVLeKEyjHOcenf55B7KrTJuEhjAFvP90UM198rMLUtyLhnOPA5RT0/dxaoBwAcNSs6oO9sb1RhwLl3IJOFPHuL8cxZ/Nlm/v73i1rUqCcG+GcY9mBmxhlLVAOABhHw7AA7I7tXaJAucqIcw5RFKHRaKBSqVBYWAhRFMEYg1wuh1KphFwuh0QiAWOM9olW5BVp0O+LHdh70XqgHFC8vxnWoR4WjW1PgXLEKQ9yi+wKlAOKr/281bsxBcoRl8ov0uLZ/+3D7ssJVgPlgOJ9XrCfEjun9qRAOeKUjLwi9Ph0C07fshUoBwActav5Ys8nvVE72HMD5e7du4fGjRsjJyfHaLqvry8OHjyIZs2alcp7o1arcfXqVTRt2tTlyy4Pt27dQqNGjaDRaAzT6tSpg8uXL0Mul+Pnn3/Ga6+9ZrgfzhjDiy++iMWLF3vstudKOQVqhI/6HQVqy/dqLPn65cfx1tOPlkKtis+hHjx4gDfeeAN//vkndDrL9xQcwRjDyy+/jEWLFtncfgoKCjB48GBs2rTJaoico9599118+eWXtP0SQtyWKHI88f56nLz1wGbZAG8Z7i8cWmGvE3POMWjQIPz5559mnw8JCUFiYiIUCoXJc8eOHUObNm0s/oboA3MlktK5ZrVs2TK88MILZp+TSCS4c+cOqlevXirrJq535V4Wmvx3tc3rU+YcmN4H7Rp5cNYBIR5EqxPxyItf4eb9DKvl3h/aCf97rYdD5yR0xZsQQgghhBBCCCGEEEIIIYQQQggpA/Xr1zdpPM85B+cc6enpSEhIQHx8POLj4wH8Mxq2Wq02TC+NP0II8STJD3LArYRlAIBcKkGQv2cF51rDGEO3ZuFYPakrfJVSi7n3nANL997A6wsOQmMtlIJUCowxPNWiBv58tyu85ZYbxXMO3M0sQMyUzbiRnOvSTlqkfEgEhs+GP4aJfR+FIFhujMo58OfR23hxzj6oNK7pHEjKVzV/L+ya1htNI4LArEQfaHUc4xYewU87rlaI7zznHMsP3ML//jpjNkguItgXU55vTh0+LWCMocMjodg4uYfV33mA4e6DfHSeuhm3U2h/7w4kgoCZLz6Ot/o0trm/33z6LgbN2o1CJzqrk4qHMYbhhsAuK91JOMO15Gx0mboJ9zMKyq6CFQBjDBKJBEqlEgEBAQgLC0N4eDiqV69uGBBCJpNBEAT6fbHBVynDuve7o3PjMJtjqHEOLD9wE69+fwBqLR1fEsdV9VNi59ReaFY7yOb2Jooc326+hEm/xEEn0jUM4ho+SinWTu6MmEdqW4nnLsY5kJZbhK6xW3AhMZOOr4nDqvgqsf3jXmhZtwoYsxlfiIS0PHSZshkJaXkeub1xzvHpp58iNzfXaDpjDNOnTy+1IDkAkMvlePRR5wO8OOfQarVIT09HXFwc1qxZg3nz5uH777/H4sWLsXv3bsTHx6OoqKjUP1vOOZYsWQKt9p9zQ33Yl1wuB2MM/fr1g7e3t9E869evNwnxI84p0uigcSKojTEgvKpPKdSo+DOOi4tDy5YtsWrVKpcFyemXrVarbZZTq9V4/vnnXR4kRwghnkDkHNmFpvtazuFUEFZl1aBBA3h5WW4jcvHiRRqslditiq8CUiv3XSxhABLT81xfIUJIhSQRGCJCAmyWu3Y3zeFlU5gcIYQQQgghhBBCCCGEEEIIIcQxTPDMPwDR0dFo3Lgx5s6dW84fAqms5syZg759+xoFypn7e5ilMiX9I4QQT5SYmgNbPYp9lHJ4K2RlVKPKgTGGrk3D8de7XeGjsCdQ7hAFyrkBewPlwIF7WQXoOpUC5dyFY4Fy8Xjxu/0UKOcmgv2V2DmtFx61I1DujUXlHyjHOcfvB29h1PwD0GhN6yEVGH4e1wF+XvS7bg1jDO0bhWLDBxQo52kkgoAZI6IxvvcjdgXKDZ69hwLl3ARjDMM61MNCOwLlrifnIGbqZo8LlCOu46uUYd0H3dHJzkC5FYduYeS8gxQoR5xCgXKkvPl4SewOlAMH0nNV6EaBcsRJVXyV2OZooNzUzUhI97xAuQcPHmDFihUmr7tevXp4/fXXS/2+qTPL1w9G9s0336BVq1aoWbMmnnjiCQwcOBDjxo3Dm2++iZEjR6Jbt26oX78+IiMj8eqrr+LUqVMuDfN6mEqlwtKlS43eR6lUihEjRhheY9WqVdGtWzej+bKysrBt27ZSqZOnycxTQ+fk17d6oOsHUeKcY+vWrejWrRvu3btXLvsWzjkWLlyILVu2eNy+jRBCXEGrE5GZazu4090FBgaiRYsWFp/XarX47LPP6LeG2MVLLoFCZqV9hQUcQGpWoesrRAipsOrWqGqzTHxSpsMBrxQmRwghhBBCCCGEEEIIIYQQQgghdoqLi8OlS5cwbty48q4KqaRkMhnWrVuHJUuWoGXLluCcm/17mKUyJf0jhBBPwznH9bvpNveBIYE+kEkdb9hZVtRFeVAV5JT5ehlj6NYsHKsndYW3XGqxXHGg3HUKlCsjgiCgY8eOeOqpp8z+de3aFRKJ89uzI4FydzMLEDOFAuXchUOBckduU6CcG6nm74Vd03rj0YggwI5AuYU7yydQTqsTMXPtBbw8dz9UGtPfG8aA//Ztgs5NwihM2w6MMXR4JBQbJ1OgnKeRCAJmvvi4XYFym07dwaBZuylQzk0wxjDczkC5a8nZ6DJ1EwXKEaf5KmVY/0F3dLYzUG75gZt49fsDFChHnOJMoNx7v56gQDniMs4EynWlQDnipKq+Smx3NFBuymYkpOd7zPbGOceGDRuQm5trNJ0xho8++ggKhaKcamaZSqXC7Nmz0bBhQ0ycOBHnz5+HSqUyucerf6zT6ZCSkoKlS5fi8ccfR9++fZGQkODyz/jYsWO4c+eO0bTWrVsjIiLC8JgxhjfeeMPoWgznHHPmzCm1kDtPkpGrBufOHbNU83dtmBznHKdOncLgwYORm2v7GhFjDBKJBCEhIXjyySfRs2dP9OzZEx06dEDt2rXh5eXl1DW8goICTJ8+3er6GWOQyWSoVasW2rZta1h3ly5dEBUVhcDAQAiCQNcQCSEeSavjKDJzf48xm2P0uRXGGF5++WWLvwWcc6xYsQJxcXFOHWNxzlFQUOAxx+CeTi6VwFdpuU2NNYkedK5GiKdjjKFOWJDNcilZ+dA6eO3auT0QIYQQQgghhBBCCCGEEEIIIYQQQpz24osv4sUXX0RSUhJOnDiBu3fvIicnB0VFRQCA2NhYMMbAOUdYWBhef/31cq4xIYS4h1tJGTbL1KzmD6ECtozmnCM/4z7WzhgAudIXAz7cCKnctZ1/bGGMoWvT4kC5gTN3IV+lNTv6qT5QDgAWvN4OMmvhFKREJBIJli1bVqrreDhQ7rmZu1CgttDpjwP3sgrQdepm7IrtjfphftT5qpLTB8oBwOwNFyCK5huu6wPlAOCXNzs6NdI6qViC/ZXYOa0Xuk7bgot3MsG5+e+yVscxbuERcA681j2qTL7znHPceZCP1384iO1n75v9HWIMiGkajk8Ht6b9kAMYY2jfKBQbPuiBvp9vR16R+d/5hwPl9sb2Rp1Q2t9XdhJBwMwRjwMA5my+bHV/v/n0XQyevQcrJ3aBl5WAYVI56APlAGDUvINQWwqD5gzXk3MQM3Uzdn/SG+FB3mVYS+IufJUyrPugO575fAf2XUq28BtTjHNgxaFbYALDorHtIa/Age+kYtIHynWL3YJzCZlWtzdR5Phm00UAwJcjHoNEoGsYpOT0gXLPfrYXuy8nWInpLt7npeeq0C12K3ZO7YlHI4Lo+Jo4pIqvEts+7oWnPt2C07cyLJ7DF9MHym3Cnk96o3awr9tvb5xzLFmyxCSQwdfXF3379q1wrz8jIwMDBgzA/v37HQ6R0AfLbd26FS1btsSyZcvQs2dPl7xGfSDcwxhjGD16NIR//Xa2adMGoaGhSE5ONkw7fvw4EhISULdu3RLXxZOl5xYCtqNKTQiMwdfLtefwWVlZGDBgAPLy8qyWY4zB19cXr732GkaNGoXIyEgoFArDdqnfbnNzc3H69GksXboU69atsyugDgAOHz5stK39m0wmwxtvvIG33noL1atXh1QqNfpO6HQ6qFQqJCUl4dixY1i9ejX27duHjIwMCnIhhHiEvCINVBTmD8YY+vfvj4kTJ5qEEOtpNBoMGTIEhw4dQvXq1e1eNuccx44dw6xZs7Bq1aoKd/xJXE8qEVDFV4nkrCKH503JLiyFGhFCKqr6NaqCgYFbuXqYXVAElVrr0H0SuoNLCCGEEEIIIYQQQgghhBBCCCGElJPq1aujb9++JtNjY2MN/w8LC8PUqVPLslqEEOK27qZm2yxTN7xqGdTEMZxzpN4+g/Uzn0N+dgoYgJ0/jUOPMT9BEMq2Uz1jDN2aheOvd7tiwIziQDnzdS4OlGMAfqBAuVJTVo3NHQqUyywOlNsd2xv1KFCu0tMHynHO8dXGizYD5RiApRQo5xaq+XsZAjgu3MkErATKvbHoCBhjGNWtYal950XOkZxZiK83XsCC7VeQp9KYrRNjQLPagfjznRjIaTt0GGMMHR4Jxfq/w34oUM5zSCR/B8pxYM4W64Fym07doUA5N2IIlOPAqPnWA+WuJWcjZkrxcV71oLINtibuwUchxZr3uqHP9O04fD0V1tKVOAeWH7gJBmDhmPZ0TkkcVsVXga0f9UTMtM24fC/b6vb2cKDc/4a1hlRC2xspOaVCwK//bYcesQW4mJRmNdQQHEjPLUK32K3YPuUpPBpRpczqSdxDoLcCf0zsii5TNyMxrcBGaX2g3Gbs/aQPIoJ93Pp8LicnB6dOnTKZ3q5dOwQFBZVDjSzLzMxE165dcfbsWZMQK8YY/P390aRJEzzyyCOQyWS4f/8+zpw5g7t374JzbpiHc47MzEwMGDAAK1eudEloXnp6OrZt22ZULx8fH7PL9vb2xuDBgzFnzhxDea1WiyVLlhgGVCPOSc0qsv57YoFCJoGvUuayenDO8e677+LOnTtWA9cYY2jTpg2WLVuG2rVrm/3sGWMQBAFVqlRB165dERMTgwcPHmD+/PmYPXu2zXps3brV6vpnzpyJ8ePHW9zupFIppFIp6tevj/r162PYsGHIz8/HwYMH8d1330Emc937RgghFVGhWgdzl4L1kzzpV7tq1ap48cUXMW/ePLO/b5xzxMfHo0uXLti4cSPq1atn9biGc46MjAxMnz4d8+bNQ+vWrZ2uW0FBAUTRwnVbKxhjdOxVDhgDAn3kTs2blElhcoR4kojQQFi9cA2gSKVFbqEKft4Ku5dLd28JIYQQQgghhBBCCCGEEEIIIY4RhOI/T/L3642OjoZEIsG4ceMwbty4cq4U8QTUoIsQ4qmsdT5xlk7kuJtmO0yuuKFW6dTBWbeOrcO2H16HRpVnaLB99dAqBIXVw+MDPkB5NOPu1iwcf7wTg4Ezd6HQQrAY58CSvdcBAN+NagM5df6v9GKaVsfslx/HGwuPQmclZOZuZgFipm7G9qm9UD/Mr4xrSUrDx8+3xO3UPPx1NN5iGc6BP47cBgfw89j2FCjnBqr4KrByQhfETNuClKwii+W0Oo5xCw+Dc45XYhrCVacxGp2IlOxCHLycgt/23cCBy6ko0ujDzcyvJNhPiV/Hd4aPQgqtzvGONaRYm4ahmDuqLV6ddwBanaVjon8C5XZO7YW6obS/dwexQ1ohPjUPa+MSLZZ5OFBu+dudoaT9vVt4vm0d3E7NxbQ/zoBbOM7TB8q1nbwB3ZrW8KzepMSl8tXmQ8n/jXNg2YGbuJmci0dqBpZupYib4hbPXf9NHyj319F4eCvot424BufAvQz7OoJzDqTlFqH9R5tQq6qPy86riOfgnCM9RwXGYEfYVHGgXOcpm7Dnkz6o7caBctevX0d+fr7RNMYY+vfvX6Fes0ajwaBBg0yC5BhjCAsLw/Tp0zFw4ED4+RmHuWs0Gpw/fx7vv/8+du/ebRQyUlRUhOHDh+Pw4cNo2rSp03XjnOOvv/5CQcE/QYWMMfTo0cNsIB9jDKNGjcL3338PnU5nWMbSpUsxefJkKJVKp+viyTjnSM62FRZpnlzCIHNhWO7ly5fx66+/2gyS69WrF/744w94e3vbvWzGGIKDg/HRRx9h5MiR2Lx5s9XyZ86csViPgIAAjBw50qHvOmMMvr6+6NmzJ3r06IHCwsIKta8ghBBXS8spqlDtIsoTYwwfffQRli9fjszMTLNlOOe4du0aWrVqhfHjx2PkyJGoUaMGpFIpGGMQRRGFhYW4ceMGli5diiVLliA7O7tE77EoiujRowcCAgIcmi8gIABbt26FQmF/+BBxDQagZlUfp+bNyleBc9A1AUI8RLVAXzCBWb4vh+L2julZeQiv6m/3cilMjhBCCCGEEEIIIYQQQgghhBBC7BQXFwd/f/tvyBJSUtRgjxDiiUTOMXPtedzPcK5TjCX5hYVITM2xWe63fdeRXFQxOjN5sUI8kr0eOScXQ9RpjJ/kHMdWz8DpO0W45t8LOl72QW2Fai1kEgGFMB8mB/wTKLft7D14yai5mjtQabQ2xsUF8Heg3OPvrUNogBcoacQ9ZOWrbJbhHPjzyG0cuJQMH4WsDGpFSpvIRWTna2yWKw6UO4Iv1pyDxAUB9Bwicgs1yMhTQ+QiOLdvP5JTqEG/L3ZCYBRgWlJqnQ62s1eKA+WiJ61DSID9HYNJxZZVYN/+ftOpO2g5cQ0YBIi2jw5IJVCk1tq+FsUZEtPz8fOea2VTKeLxOAeOXEvFkWup5V0V4gFEkSMhLa+8q0E8GQfyi7S4cs/2gBiElBgvDpTrMmUT9n7SBxFuGih35swZs9Mff/zxsq2IFZxzzJ8/H7t27TIJkouOjsaGDRtQrVo1s5+PXC5H69atsWXLFsyaNQsfffSRIcANAPLz8zF8+HAcP37c6RA3nU6H+fPnm5wrjB071uI2ExUVhaioKFy6dMkw7c6dOzh27Bg6derkVD0IkJZr+3zdnAAfOWQuGuyHc44ZM2ZAo7F+vbBhw4ZYsWKFQ0FyD2OMITw83GoYHOccubm5Fpfh7+9fogAdQRDg4+NcEAwhhFQWWQVqs0HEzHC91/2OD60JDQ3FF198gTFjxhiF9D5M//vz2Wef4csvv0T16tVRo0YNKBQKpKSkIDk5GTk5OeCcu6TdH+cct27dcni+qlWrUrvDchToI3dqvoxcFXScQ/Cw7x4hnirQVwmZVIDKwiCuwN8DUGQ51oaSWucRQgghhBBCCCGEEEIIIYQQQgghZSAn558AI4lEYrXhcceOHQ2NouvXr1/qdSOEkAqFA8sP3MT5RPOjPTtNUwSobYfhXL6Xi8tbLtksV7o4qiIdgyS/I5NdszjqMBe1yDs6B3HiHRwW24OjYgbncA6XhwOSSoADeUVa5BVZ7sxF3BPnQHJWIYDC8q4KKWM6kSO+VMI37O8wodLocDuVAkDKFkNukRa5RbZDe4l74Ry4mULfN0IIIYQQQiqtvwPlOrtpoBznHOfOnTMJ0ZDJZKhZs2Y51cpUZmYmYmNjTYLkmjRpgq1btyIoKMjmMqRSKSZNmoSioiJ88sknhmVxznHhwgX89ttvVkO5rLl27RouXrxoNC0sLAxt2rSxWp/XXnsNEyZMMKrLd999Z3QfnDgmKSPfqfl8FFIILnrPc3JysHbtWqvhNBKJBAsWLHDJQI0l2VbS09Px4MEDhIWFlbgehBDirhLT8sAYzAbKeSLGGF599VXs2LEDf/31l9XfO845tFot7ty5gzt37pRhLUllULOqc4G6hWodRJEDEhdXiBBSIfkoZfD1UkClttymjgFITM1yaLkVs/UgIYQQQgghhBBCCCGEEEIIIYQQ4mYCAwMRFBSEoKAgdOzY0WrZvXv3Ys+ePdizZw9++umnMqohIYS4OW55FE8jQnm3yuSozeIxSvojGlgJktOTQos+wgY0ZhcAUCtvQgghhBBCCCGEEEIIIfb6J1AuMT3famBGZXTjxg2TaX5+flYH/SpLnHP89NNPyMw0HlxHqVRi5cqVdgXJ6QmCgA8//BAdOnQwCuDinGP69OlQqVRO1e+HH36AKIqGaYwxDB06FF5eXhbnY4xh0KBBkMvlRtO3bduGBw8eOFwPUiy3yPaASeYE+yshuCi/Ly4uzmgQPXNatWqF9u3bu2aFVjDGrH5HCgsLMXLkSDx48MDt9m2EEOIKnHPczyy0ECTH4MhAP+5EKpVi8eLFaNOmDQXgEqcwxlDVV+nUvPkqDVQaO9s2EUIqPalEgiBfy+fWAMDBcTsp06FzGgqTI4QQQgghhBBCCCGEEEIIIYQ4RmCe+QcgOjoajRs3xty5c8v5QyCVFefc8GdN3bp1DX99+/Yto9oRQoibE7X2lSvHMDkGEc3YWbwiWYhQJNsMkgMAxgAlivCc5A/UYomgQDlCCCGegIN+8QghhBBCCCGEENdwz0A5zjmys7NNpvv6+kIqlZZDjUxpNBosWLDA6D1njOGll17CI4884vDyZDIZvv32W0gkxvc5EhMTcfToUYeXV1BQgJUrVxrVTxAEvPrqqzbDVcLCwtChQwejafn5+Vi9erXbbGNliXMgJavQqXmrOBlmYloHjm3btlktwxjD6NGjTbbB0tKqVSuL2yLnHFu2bEFUVBTGjx+PgwcPIiMjA1qtlrZBQgj5270HeeVdhQrJ19cXW7ZswVNPPUWBcsQp1QKUdrX3+TedyKET6TiFEE8hCAwhQbbD7u+lm17bsLpcZytECCGEEEIIIYQQQgghhBBCCCGeJi4uDpcuXcK4cePKuyqkkmKM2dXILD4+HgkJCYiPj8e9e/fKoGaEEOLmOAdEe0bvZQArjzA5Dgm06CDsw1DJb/BHjkMNSxkDApCFwZIVCEIGKF6HEEKIO6IAOUIIIYQQQgghpLQwJKTnocvUzW4TKMc5R25ursl0uVxeYUJBbt26hcTERKNpUqkUEyZMcLqOTZs2xRNPPGEyfdmyZQ5/rnv37kVaWprRtEaNGiEqKsrmvIIgYMyYMUavg3OO+fPnQ6ez534NeRgHR4HKzkGT/iXIR+aaOnCOY8eOWd2OBEFA165dXbI+WxhjeOaZZ6x+VzjnePDgAebOnYuOHTuiZs2aaNq0KUaMGIGff/4Z58+fR05ODkRRdIv9HiGEOOpOunNBpZ7A398f69atQ2xsLLy8vJw+NmOMwc/PDy+88EKFOQYlpa+qr8Kpm5oaHUeB2rljPkJI5cMA1A4JtFkuPjnLoeVSmBwhhBBCCCGEEEIIIYQQQgghhBBShqgRMiGElBNuR+ckxgBW1k2qOBRQ4WlhPfoK66BgKqdGKGYMCMc9DJL8Dm8UgOJ2CCGEVGp/J8dxmA+RY3//EUIIIYQQQgghxEU4Q3xqLjpP2eQWgXKcc+Tn55tMFwShwgR57N27F6IoGk1r1KgR6tSp4/QyJRIJXn31VZMQtz179jgU4iaKIr799luj7YAxhv/85z+QSOwblKdbt24ICgoymnb+/Hlcv37d7nqQYqII5KucC+ELr+LjkjrodDokJCRYLRMQEICQkBCXrM8erVu3xiOPPGKzHOccnHMUFhbiypUrWLZsGUaNGoXmzZujRo0a6NSpE+bOnYu7d++afCcJIcRdcQ6k5xaYTge1NNCTy+X46KOPcO7cOQwePBje3t52H0cyxhAcHIyJEyfi2rVrJiG7xL0p5VKnbmSKnCPfyQBhQkjlVCM4wGaZtKx8iA5co5GWpEKEEEIIIYQQQgghhBBCCCGEEEIIsY9CoYBarQYAw7+EEELKkGhPmJxQxmFyHH7IwQDJn2jGzkJgJWuWzRgQhSvoK1mHv3TPQQu5i+pJCCGElD/qYkMIIYQQQgghhJQ2hoS0PPT+33acmPEMvOSVtwsyYwxKpdJkekUJyeOc4+DBgyb16datm91hbZZ07NgRjDGjZd+7dw85OTmoUqWKXctISkrCgQMHjKYpFAo899xzdgeh+Pn5oX///vj5558NdRFFET/88AO++eYbClRxgFYnIr9I49S8XvKSbU+GOmi1yMvLs1omJCQEcnnZ3ZuSy+VYsGABunbtCpVK5dC8+m0yLy8PBw8exKFDh/DOO+8gJiYGn332GZo3b07bKCHErelEjtQc031nZd7zTZo0CS+88ILZ5xQKBaRSx49tGWOoX78+li9fjrS0NOzYsQPr1q3DmTNnkJKSArVaDVEUIQgCvLy8UKNGDbRp0wYDBgxAu3bt4Ovra/P3pFOnTli7dq3DdbNGLpdDJpO5dJnEflX9FM7NyAGNloJtCfEUjDHUDgu0WS4jpxA6HYfEziaNlfdKDiGEEEIIIYQQQgghhBBCCCGkfJR5yEoF8PfrjY6OhkQiwbhx4zBu3LhyrhSpbIKCgpCSkgKguLE+IYSQMmZ3mFxZNY/mCEUyBktWIJLddtlqGQOicQyZQhB2ij0gwjWdhAghhJAyxYz+IYQQQgghhBBCSBlgDPCSy/D1y09AKavc15YZY/D39zeZrtFoKkSgHOccV65cMZrGGEP79u1LHGAVFhYGPz8/ZGdnG6apVCokJyfbFSbHOcdvv/1mFM7FGEOHDh0QFhZmdz0YYxg9ejQWL15seM8551i5ciU+++wz+Pj4OPCqPJuOc2h1zgWLBPrKXRKKptVqbQ6a5+3tXeYBbG3btsX333+PN954w+FAuYdxzqFSqbB161bs3LkTY8aMwYwZM6BQOBkGQwghFZxWFJFXaLpflwjF+3GdWP7HS45gjCE6OrpUlx8SEoLhw4dj2LBh0Ol0UKlU0Gq14JyDMQaZTAaFQgFBEBz6PaxZsyZq1qxZanUnZU8hk5iEO9uFAVn5NEgxIZ6kRrUAMADW9hY5BUVQabSQ23mdhsLkCCGEEEIIIYQQQgghhBBCCCHETnFxcWYbnBNij8jISCQnJwMAcnJysG/fPnTq1Kmca0UIIZ6CA9zOMLkyia3hqM+uY5DkdwQjzeX5dRImoquwA5m8CuL446AoHkIIIYQQQgghhBBCCCG2eMmlWP1uDLo3Dy/TQCidToe1a9eiqKjI4Xl9fX3Rt29fCILxgHiMMfj6+pqULygogFarhVRavt2rdTod0tLSTKbXrl27xMtWKpUIDg42CpMDgNTUVDRu3Njm/BqNBgsXLjQJvxg7dqzJ+2xLs2bNULt2bdy+fduoHvv370evXr0cWpYn04kcWidDfUL9vVxSB1EUodNZv9dW1kFy+nWOHDkS9evXx6uvvor4+PgSBUZyzqHRaPDdd9/h7t27WLFiBeRyuQtrTAghFUOBSov8Iq3JdMXfQTUFKtPnSDHGGKRSabkfT5KKSyphTrXS4RzILdS4vD6EkIorOMDbZhm1VgeVWgs/b/uCrunXiRBCPNC0adMQGxtreFwRRlMhrtO5c2fs27cPANCpUyfs3bu3fCtECCGEEEIIIYQQQgghhBAAxSNiHz161DDq5EsvvYQlS5agc+fO5V01Qghxf5wDXLRdTrBvBM+SYBDRkp1Ef8lq+CDP5UFyejJo8IxkDbJ0gbjOG4IC5QghhBBCCCGEEEIIIYSYw9jDQXI1yjwQSqfT4c0330RSUpLD89apUwd9+vQxG3JWp04dk2k5OTkoLCyEUql0qq6uotVqkZubazSNMYYqVaqUeNmCICA4OBg3b940TOOc48GDB3bNf/bsWdy6dctoWpUqVdC1a1eH6yKTyfDKK69g6tSphj6MnHPMmTMHTz31lMPhdJ6qUKWFRmfHfS4z9KFAJSWXy6FQKFBYWGixjFqtLpe+qowxdOrUCRcuXMDy5csxa9YsXL9+HZxzp+vDOceaNWvwzTff4N133y2XoDxCCClNuYUaaMwElQb6yFGo1lGYHCEl4CWTQioI0Il2DHr5LyqN4/MQQiqvagHexRdlrJy3aHUisvKKEBzoY9cyKUyOEFLq4uPjzV54fRhjDP7+/ggKCkKTJk3Qpk0bDB8+HJGRkWVTSUIIIYQQQgghhBBCCCGEEEJK2fDhw/HVV18BKL4/lpiYiK5duyIoKAh169aFj4+P2QbIN27cQExMTKnUiTGGXbt2lcqyCSGkYuFWG10ZsNIMk+OQQovOwm50E7ZDDnWpBckBxe3MfHg+BklWYJH2P0hGdVCgHCGEEEIIIYQQQgghhJCHlXeQXGlhjKFJkyaGgb70VCoVUlJSEBQUVI61Kw6q0umMgyIYY5BIXHOfQiaTmUzTaDR21Wvu3LkmAVydOnWCUqmEKDoeaPbMM88gNjbW6PXu3bsXycnJCA8Pd3h5nkgrcrtuc/0bY64LkxMEwWYIY3p6OrRarcu2Y0cwxuDt7Y2RI0fipZdewuXLl7Fu3Tps27YNFy5cQG5ursPhcpxzfP7553jttdfKfZ9BCCGulpGngmhmn1gvzBfxKXnIzCuHShHiJuRSAVIJgzOZjEVqCpMjxJP4eSshMAadlfMULgK5BUV2L5PC5AghFQLnHNnZ2cjOzkZ8fDw2bdqEKVOmYNiwYZgzZ06lvNCydu1anDlzBgAQGBiI//73v+VaH0IIIYQQQgghhBBCCCHEZRhDqSafVER/v97o6GhIJBKMGzcO48aNK+dKkcqmZcuW6N+/P9asWQPGmKHjQkZGBjIyMkw6ZugbMefn52Pfvn0urw/n3G06gxBCiE1cBGBPmJxQWhWAFwrRR1iPJ4UjkDDLna30tXTFHpoxoCp/gCGS5fhZNwo5CHDRkgkhhBBCCCGEEEIIIYRUdvoguT/fca8gOb2WLVuaTOOc48yZM2jUqFE51MiYufvDjgRdWWNuOfZ8vtnZ2Vi7dq3J/Lt370Z0dLRTdSkoKDAJoVOpVPj999/x9ttvu912Vxq0OtGeu1xmVfVTuKQOMpkM1atXR3JyssUymZmZyMnJQbVq1VyyTmcwxiCTydCsWTM0bdoUH374IQoKCpCYmIijR49iy5YtOHLkCJKSkuz6zmVnZ2PXrl147rnnyugVEEJI2bjzIB8Mpq0oIoP9kJRRWB5VIsRtMAZIBOeOcVOy6ftHiCfx9ZZDIZOgQGU9uD3ZgZRXCpMjhJSLf48swDk3uSAoiiJ+++03HDp0CIcOHUL16tXLsooltnbtWixduhQAULt2bQqTI4QQQgghhBBCCCGEEELcQFxcHPz9/cu7GqQSW7JkCRITE3Hy5ElDoBwhhBBTdUJ8odFZbyTliPz8fNzNthEnxxhCq/ghKDjQZevVK8q6ix6q5YjCRQjMSi0Yg0TuDxXzgqzIcmccRzAGRCABz0n/xP6A/0Cq9HPJcoHith3xaflQa61/VoxxCEzwuDxid6YTRdjTp1EQBDjZRppUQJwDIrf92TMG+s67GZHDpH2fOYwxpztGkIrJnv198XedPnt3Yu/+HgCUMim0duwfSOXAOSCK3OmAAkIIIYQQUrk8HCT3VIvyD5KrVq2aU0Fq1kKrHnnkEcjlcqhUKsM0zjk2bNiAwYMHl+trFgQBMpnMaBrnHBqNxiXLLyw0DqFgjEGhsB0qtmnTJuTk5JhMz8rKQlZWlkvqBhS/1h9//BFvvvmmyftATGl1zgYNciikEtvF7MAYw6OPPorTp09bLKNSqXDmzBl0797dJessKX2bDF9fXzRu3BiNGzfGK6+8ArVajevXr2Pu3LlYsmQJVCqVxfeXc45du3Zh4MCB5b6fJIQQV7qfXmhyDZgxIKpGII7fSC+fShHiJgTGIDh536xQo3NxbQghFZlMIoFCLkWByvK1AA6OrFz7gyYpTI4QUuY6deqEvXv3mkzPycnBmTNnsHjxYixdutRw8eX27dvo378/jhw5QhdbXGTatGmYNm1aeVeDEEIIIYQQQgghhBBCCCHE4/j5+eHw4cP4/PPPMX/+fKSkpNicx1WjzxNCSGXBGLB6UjeXBhgkpGSh0YjZ0Fppc8kA/DW5D554pJYL18yRk5mG1bOGITf+Ipi1V8UYAkLrou87f+DivhU4vWGm3WuRSBXwCQpDdlqi2XUwBjzKzuOlnrlo03cEmCA482L+heNcfCa6xW6BWqu2WIoxoEVkVax4uwsCvOUuWC8pT5xzbDiRiDcWHYHGRoigl1yKRWPbo8uj4WVUO1Ka1FodJi8/ieUHblgvyIBG4YH4450YVPVTlk3lSKm6+yAPA2fsRuID6yNdMwa82Kk+pg9tDanEFb8zpDxxcOw8ex8j5x+ExtoBFIrDxBa/0QGdGleuAYOJeSqtDu//GoeVh2/ZLFsv1B8bJndHkI/tMAJS8ek4xy97ruPjFaegs3EtSimXIDzIG9SqmzgrK1+NB3lFgJ1bUZCPDFV86diSOI4DyMhTIatAbWOEgWKMARKBUSg6cSmtWHw9xd5bPYwBMglthMQ5WrE4HNheXoqKEyQnk8kQFxfn9PwSifmwrKpVqyIqKgrnzp0zmr5jxw4UFBTAx8fH6XWWlEwmQ1BQENLTjcNakpOTUb9+/RItW6vVmr0PXb269XN3nU6HOXPmlNn96evXr+PcuXNo3bp1mayvMssusHwfxjoGiYt+Vxhj6NGjB3777Ter28jPP/+Mbt26lft+xRJ9sOKjjz6KefPmYdCgQXj66adRUFBgcZ4rV66UYQ0JIaT0cc7N3/fhHLWr+dJ1P0JKSCII8JJJkQXHj+E0WhGc8wp7LEUIcS2ZVIJAXyUybYTFJaRm2b1MCpMjhFQY/v7+6NixIzp27Ii+ffvi+eefN4xmeuzYMfzxxx8YNGhQOdeSEEIIIYQQQgghhBBCCCGEkJKRyWSYMmUKJk+ejLi4OJw4cQJ3795FTk4OioqKAABLly4FYwycc1SpUgV9+/Yt51oTQkjZYYzB1W0ipRIG0UbnJ4ExRIQEQOKiHsOcc+Q/uI/NM59D3p0LNoPkajRqhz5vL4fStwq0hZkOrcsrMBT93l+LjbOHIPP+NZjrIc24iLNrpqJuo5YIb9SuxA1PE9Py8dysXcjMV8FSAEBxkFwV7JjSC1X8KGTEHcSn5uKz1Weh0YmwFvzgJZdg2Vud0e/xCGrk7AY451hzPAF/HbkNzi1/nowBjcIDsDu2N0IDvcqwhqS0qLU6TFxyEXcy8mHtO88Y8HLnBvjh9XaQSSlIzh3cz8jHp3+egdbK/p4xQCmTYOWELujTuhbt790A5xx/HL6NNccTrO7vAY66of7Y80lv1KxafsELxHU457iQmIlvNl6EaOW8hTHAVynDuve7oX2j0DKsIXEnhWodXp17AH8dvW2zLGPAo7WCsPXjp1DNn8LkiONyCjXo/+VOHLhie1AXxoAOjUKxdHwn+CplZVA74gmycjUYNns/TiQm21VeJmWY/5926BcdAXsDNwnRS8lQof8Xu3AjPdNmeCFjFStIrrhODHK56wcjEQQBQ4cOxfnz543Cr9LT03Ho0CH06NHD5et0pG41a9bE9evXjaZfuXIF7du3L9Gy8/PzkZGRYTSNMYaQkBCr892+fRunT58u0bodwTnHvHnzsHDhwgqxHVZkGp0IDg6Hfx8YIHHhe9u5c2fIZDKo1eaDUTjnWL9+PRITE1G7dm2Xrbe0MMbQuXNnDBs2DAsXLrRYrrCwkEJdCCFuJz4t13QiYwiv4l32lSHEzQgCoJQ7d8+0uC0GIcRTMAb4etm+HpKdV2T3OQmFyRFCKqQBAwZg/Pjx+OabbwzTli1bRmFyhBBCCCGEEEIIIYQQQkhFwBhcnvBS0f39eqOjoyGRSDBu3DiMGzeunCtFKjupVIo2bdqgTZs2Js8tXbrU8P+IiAgsXry4LKtGCCFuJzO30GYHOkFgUMhc05yKc46026exftZg5Gfet1qWMQGNOgxFzMhvIZV7gXOOopwHDq1P4eWHwLB6eGbSn1j1cWcU5pqfX6dVY9NXwzDo0z0IDKvn0DoelpiWh5hpW3A7NRe2guS2T+lJQXJuIiE1F12mbkZCeh5gJWDGSy7BbxQk5zY451h7PAEvfLMXRRrLDdcpSM79qLU6vPr9Aaw4dMvqbyhjwIud6lOQnBtJyixAlylbcD05G9Y6KCtlEvxOQXJug3OOP4/cxkvf74fKyv4e4KgX6o/dsRQk5y70QXLdY7ciLbfI4j5fHyS34YNu6PBIGH3viVN0oohP/zyNNccTYCsEQx8kt2taLwRTkBxxglorYuKSYzh4JcVc5r4RfZDchsk94OdFQXLENXQix6y/ruJkYorN65JAcZDcwjEd8ELHevQ7Sxym0Yp4b+0Z3EjLtLXLAwB4yStWkFxpYoxh6NChmDp1qlH4Feccn3zyCWJiYiCVll8365YtW2LPnj1G9dq9ezdGjhxZos/mxo0bhkHM9Pz8/FCtWjWL83DOsWTJEmi1WqPpYWFh6N+/PwSh5Nd97ty5gw0bNhiC/TjnWLNmDb7++mv4+/uXePnuTKsTbR7TmMPAIJG47ppd9erVER0djUOHDlksU1hYiAkTJmDVqlWQSCQuW7c5rgh4Y4yha9euVsPkSvt1EEJIeUjOLDCZJhUYwug+HyEu4exAlhothckR4kkExhAW5IfzsD4gSmJqtt3LpDA5QkiFNXr0aKMwuX379pVfZQghhBBCCCGEEEIIIYQQQgDExcVRI2ZCCCGkEios0sBWr02FTAIvRcmbU3HOcTNuPbbP/w80RflWywoSKZ4cMBmt+02EINGvmyM/O9mhdXr7h4AxAQEhddDnv8uwdkZ/aFWF5iqHwvwHWPflAAz+dA+UvlUcWg8A3EnPQ5epm+0OkqvqR53+3UG8A0FyyyhIzm1wzrHmWAJe+JaC5DyNI0FyL3duQEFybiQpswCdp2y2GiTHWHGQ3EoKknMbnHP8cdi+ILm6of7Y8wkFybkLCpIjZUknivhg2Ql8teEiRNH6+TkFyZGSUmtFvDb/AH7bf9NmiBcFyZHSoBM5PvrlPGZuOgnRjiQ5mZRh0dgOGN6BguSI4zRaEaPnxmHJgYs2c6YY86wgOb1atWqhW7du2LJliyHEDACOHDmCrVu3ok+fPuXyXjDGEBMTg6+//tqoXvv374dKpYJS6dxxEOccf/31l8n0qKgoeHt7W5yvqKgIS5YsMaoLYwxTpkzB6NGjnaqLuXVERUXhzp07hmlZWVnYvHkzhgwZ4pJ1uCudyMHgeJ6cwAAfF9z70pNIJPjwww/Rp08fo23lYZxzrF27Ft9++y3++9//Oh1EqNPpcO3aNTRq1Mjid3TTpk0IDg7GY4895nQwJOccKSnWgxuCgoI8Zp9JCPEMOpHj7gPTMDmlTAJ/OjcmpMQYcz5M7kGu2nYhQohb8fexPUhrTn6RzTJ6FCZHCKmwoqKi4OPjg/z84obV2dnZyMvLg6+vr93LOHv2LC5fvoykpCSoVCo0adIEffv2tVheo9Hg8OHDuHnzJtLS0iCTyRASEoLmzZujadOmJX5NJZWYmIjjx48jJSUF2dnZCAoKQo0aNdChQwcEBQWVeX20Wi0OHDiAW7duITU1FYGBgWjQoAE6dOgAhaJ8RhXPzs7G/v37cffuXWRnZyMsLAwtWrRAixYtSrRcURQRFxeHK1euIDU1FQAQEhKCRx55BI899liJR1cpKCjAwYMHkZiYiPT0dHh5eSEkJATR0dGoX79+iZZNCCGEEEIIIYQQQgghhJDKixokE0KIa6Rk5sFWLxulTAqpRFKi9YiiDqfWf4Mjf34KUaexXJAxSOVe6P76AjR4sr/x/p4D6sJch9ar9An4e7EM4Y+0R5dXvsHOH8eCizrTwhzISrmJjV8Nw7Pvr4VUbn+HtMS0PMRM20JBch4mwYEgud8oSM5tcM6x9jgFyXkitVaHkXPtC5J7sVN9CpJzI0mZBegydTNuJOfA0u88UNyZ7HcKknMbnHP8ecS+ILl6of7YHUtBcu7C0SC59e9TkBxxnjNBcjun9aQgOeIUtVbE6z8ctDtIrj0FyREXcyZIbuEYCpIjztFoRYyhIDmbBEHAZ599hh07dkCj+ee6vSiKGDNmDE6ePImQkJBSWbcoikhNTUVYWJjZ56Ojo6FUKlFY+M/gMElJSThy5Ai6dOni1DpVKhVWrFhhEgo3cOBAq5/74cOHcf/+faNpCoUC/fv3d9n2olQqMXToUMycOdNQP845vv/+ewwaNKjE/fPcmch58RfZwTS5kgSZWNKtWze0atUKp06dshgoJ4oi3nvvPajVakycOBEymf3HWqIo4tKlS5g4cSJCQ0OxdOlSi2WvXbuGfv36oXXr1pg8eTK6d+8Ob29vh7bZwsJC/PTTTxafZ4yhefPmdi+PEEIqA7VWREaeymR6gLccPko6Pyak5JjTx2AFaittjQghbqlWSIDNMskZudCfFtpCYXKEkAotICDAECYHFAeFPRwm9/LLLxsuBtWuXRvx8fEAgEWLFmHWrFm4cuWK0fKaN29uNkzu/v37mDZtGn7//Xfk5ppvkF2zZk289dZbePPNNy0Gpe3du9fshdqEhASrF6AsXTQDigPbFi9ejG+++QaXLl0yW0YikaBbt2749NNPER0dbXFZetOmTUNsbKxd64+MjERCQgIA4KWXXsKSJUug0+nwxRdf4Pvvv0dysulo6H5+fpg0aRLee+89hy702evh93Lq1KmYNm0a0tPTMXHiRPzxxx9GF9D1GjZsiC+++AL9+/d3aF1ZWVn47LPP8PPPP+PBgwdmywQHB+O1117D+++/D39/f4eWf+3aNXz88cdYv349iorMp8E2aNAA77//Pl5++WW6KE4IIYQQQgghhBBCCCGEeIiIiH9CWMLDw8u5NoQQUvll5RXa7EDs4yUvUYcanUaNPT+/hUv7fgPnVoI4GODtH4K+76xCaL3WJu0JRFGLwtwMh9btW7XmP4tnDI90HIaspOuIWz8bZl8457h35SD2LnwLMaPnQRBsh+glpuWhy9TNFCTnYeIdCJJbRkFyboNzjjXHbAfJgYLk3I5aq8Or39sfJPfj6PYUJOcmkjIL0HnKZlxPyoa1IDkvuQQrKUjObeiD5F78zr4guT2fUJCcu+Cc4+Id20FyQHGQ3IYPKEiOOM+RIDmgOEhu17ReFCRHnKLWinht/gG7g+Q6UJAccTFnguQWjaUgOeIcjVbEaDuD5ADPDZLTa9asGV544QUsWbLEqB/dvXv3MGTIEGzcuBHe3t4uXadarcb06dMRFhaGsWPHmi0THByMtm3bYteuXYZpnHNMmTIFe/bsgVTqWBdwzjn++OMP3Llzx2i6VCrFc889Z/Gz1we6/dsTTzzh0qA9xhheeuklzJ49GzrdP4PhHD9+HImJiYiMjHTZutxN8bE0h7XrNuYwMLvCBhwhk8nw448/om3btlCpTIOI9LRaLSZPnoxNmzZh1qxZaNWqFaRSqcl2yDkH5xwFBQU4cuQIvvrqK+zatQtarRbDhg2zWR9RFBEXF4cBAwagWrVqGDBgAEaMGIFHH30Uvr6+YIyZXSdQ3K/4P//5j8V+u3pdunTxyH0nIcR9Faq1yFdpTaaHBXlBIaP7PoS4grPHDvZcQyWEuJdAX9vtbfIKVX+fx9jet1CYHCGkQsvOzjZ6HBBgPVFTrVZj6NChWL16td3rWLduHYYPH24UWmfO3bt38e6772LhwoXYsmUL6tSpY/c6nHXr1i3069cPFy5csFpOp9Nh27Zt2L59O6ZPn47JkyeXWp0yMzPRr18/HDhwwGKZ3NxcfPzxxzhy5AjWrl1bKoFyDzt//jz69OljcqH7YdeuXcOAAQMwevRozJs3z64D8CNHjuCZZ55Benq61XLp6en4/PPPsXjxYmzcuBGtW7e2q97z58/H+PHjodWannA/7Pr16xg5ciQWLlyI9evXIzg42K7lE0IIIYQQQgghhBBCSKkRhOI/T/L3642OjoZEIsG4ceMwbty4cq4UcWf6QZQIIYS4Rnq29TYBABDoq4TEyWMcLorY9dMbuHxwufnwNj3GULXGI3jmvdXwq1rT7L1rLorQaSx3vjFH7m088BljAp58/mNkptzCjWNrLAbKXTq4HIE1G6J13wlW76MnpuUhZtoWCpLzMAlp9gfJ/UZBcib0ndE45xBFEZxzQ6e1hzuvVbT3jHOOtcdtB8kxCpJzO2qtDiPnUpCcJ0rKLEDM1M24kZwDa7/zCpkEv1OQnNtwJEiubqg/dsdSkJy74JzjQmImun9iPUiOseIgufXvU5AccZ5OFPHhspN2BckxVhwkt3NaTwqSI07RaEW8/sNBu4Pk2lOQHHExncjxsYNBcgvHUJAccY5GK2KMnUFyjFGQHFB8DW727NnYu3cv4uPjja7d7d27F/369cPKlSsRFBRU4veIc47ExES8+uqr2LNnD7799luLZQVBwDvvvIM9e/ZAFEXD/IcPH8bKlSsxbNgwh+qTmpqKSZMmGQXmMcbQsWNHq0Ftqamp2L59u8l8Y8eOheDitiENGjRA/fr1cfXqVcM0rVaLxYsXY9q0aR67jdpSfDjt3HtTGu9py5YtMX36dLz33nuGbdcczjkOHTqEtm3bom7duujUqRMee+wxVK9eHQCQkZGBy5cv4/jx4zh37hyysrIM8zmKc47U1FQsWLAACxYsgJ+fHxo2bIiWLVuiUaNGqF27NmQyGdRqNa5fv469e/fi0KFDKCgosLq+oKAgPP744w7XhxBCKrLMfDW0Zq7VRFTzdbrNBCHEmLOHYPZcUyCEuA/GGKoF2r4Hm1eggY5z2B4ulsLkCCEV2NWrV40C3vz9/eHr62t1nv/+97+GILmqVauie/fuiIyMhE6nw40bN5CcnGxU/s8//8SQIUOMRrLw9/dHz549Ua9ePajVapw7dw67d+82lLl69SratGmDo0ePmlxEZYxBIine/eobgerpp9vr/Pnz6Nq1K9LS0gzT/Pz80KFDBzRq1Ai+vr7IzMzE8ePHcezYMQDFF7w+/PBDqFQqxMbGOrQ+e2i1Wjz//POGILmoqCh06tQJoaGhyM3NxYEDB3Dy5ElD+c2bN+N///sfpk2b5vK66OXk5KB///6GILnGjRujc+fOqFKlClJSUrB9+3YkJCQYyv/www/w8vLCV199ZXW5hw4dQo8ePVBQUGCYplQq0aNHD0RFRYExhitXrmDbtm2GETSSk5PRuXNn7N69G9HR0VaX//XXX2PChAlG04KDg9GrVy9EREQgLy8PJ06cwOHDhw3b0ZEjR9C+fXscPXoUgYGBdr9HhBBCCCGEEEIIIYQQQlwnLi4O/v7+tgsSQgghpMLgnCM1y3aYnFIudbYfDrJTb+HKoZU2g+QimnZF77d+gcLb8mB6Ok0RNGrb9X2YX7BpqIsgkaLH6B+Qm5aIlJsnzM7HuYgjqz5BYFh91It+xmyHojvpxUFyt1KsB8xQkJx7SUjLRecptoPklH8HyT1bSkFyhYWFuHz5slMdxxhjkMlkCAwMhJ+fH3x8fCCVSkutMyLnHDqdDqmpqTh9+jTi4uJw8eJFJCUlITU1FRqNBowxKBQKBAcHIyQkBA0bNkSLFi3QrFkzREREwNvb2+H63bt3z6RN1MMkEgmaNWtmV6dPfZDc8G+sB8mBAVEUJOdW9EFyyw9SkJyn0QfJXb2fDWsHQgqZBL+/3QVPU5CcW3A0SG5PbG/UCqYgOXfAOcfFO38HyeVYDpID/gmS69iYguSIc3Qix4fLTmLWhgt2Bck1qRmIndN6opo/HV8Sx2m0Iv7zw0H8su+G3UFyGylIjriQPkhuhoNBci90pCA54jiNVsTYeSew2I4gOaA4SO6PiZ4dJKcXGBiIP//8E507d0Zubq5hOuccu3btQnR0NBYvXoz27ds7FaDGOUdubi7mz5+Pzz77DLm5uXZd1+zatStatGiBU6dOGaaJoogxY8YgMjISbdu2teuzy83NxXPPPYeUlBSj6VKpFF988YXF18Q5x+rVq1FYWGg03dfXF926dbO5XkdJpVKMHDkS7733nlGo39KlS/HBBx9AqaR7DOZwzmHXl/5fSutrzxjD22+/jZSUFMyePdvqts45B+ccN27cwI0bN7Bo0aLSqdRD6wOK+52eOHECJ06Yv0dmD8YYxowZAz8/P1dVjxBCKoSkTPNBmrWq+ELw7ENGQlyCAWBONkBypm0EIaRyCwmynqMEAHlFami1OsiltnOLKEyOEFJhLViwwOhxp06drJa/e/cu5s+fD8YYpk2bhvfeew8KhcKojFqtNvw/MTERo0aNMgqSe+211zB79myTizvXr1/HiBEjDKFtKSkpeOGFF7Bv3z6jkLhOnTpBq9UCAF5++WUsXboUAFC7dm3Ex8fb+cr/uXirD5JTKpWYOnUq3njjDbOBeqdPn8aLL76ICxcuAACmT5+OLl26oHPnznav0x5//PEH1Go1qlWrhkWLFqFv374mZVatWoURI0YY3uuZM2diwoQJpdaxbt68eVCpVAgICMCiRYswcOBAo+c555g/fz7++9//QqPRACgOcuvbty+6dOlidpnZ2dkYPny4UZBcv3798OOPPyIkJMSobHJyMkaOHInNmzcDAPLy8jBs2DCcPn3aYvjhiRMn8N577xkeM8bw0Ucf4aOPPoJcLjcpO3z4cFy7dg1AcZjh6NGj8fvvv9vz9hBCCCGEEEIIIYQQQgghhBBCCAGQX6SxWSYkyNfZLDlk3L8OUdRZnJ8xAc16/Acdhn8OiUxuoVQxnU4L/lBbBpsYg1xp/v60TOGDZyauwu9TOiPnwR0wM41ORZ0W2+f/B89Vq41qkc2NOqXdSc9Dl6kUJOdp7A2S8yrlIDkAuHHjBqKjo0vUYFof4BYaGoonnngCw4YNQ5cuXeDr6+uSenPOkZKSgmXLlmHx4sW4du0atFqt3XVmjIExhoCAAERHR2Po0KHo3bs3qlWrZrN+nHN89dVX+Prrry2WCQwMxL179+DlZT2Uw94gOcaARhQk51YoSM5z6YPkriVZ/51XyCRYOYGC5NwFBcl5Ls45LiTaDpJjjILkSMnpRNGhILlHawVRkBxxGgXJkfJGQXKkLGm0IsbMjbMrSI6x4iC5P9+hIDk9xhhatmyJX3/9FUOGDEFRUZHhOc45bt++jZiYGMTExGDy5MmIjo6Gt7e3YV5zOOfQaDS4ffs2Fi1ahCVLliA9Pd2h65kymQyLFi1CmzZtjOqUl5eH3r17Y9GiRejfv79RH8Z/1+HWrVsYOnQoTpw4YbRuxhhee+01tG7d2uL6tVot5s6dazJfz549ERQUZPfrsBdjDM8//zw+/PBDQ18/oLivZ1xcHDp06ODydboDDhRfvnEmUM7FddGTSCT44osvoFAo8OWXXxr61boLxhiioqLw/vvv0z6UEOJ27qSbDm7HGNAg3L/UgkgJ8TSCk18mG5dTCSFuyEshA2PM6rUErU6Ezs4dBLXmIIRUSOvXr8ecOXOMpg0dOtTqPPpQuDlz5mDKlCkmQXIAjIK6Pv74Y2RnZxsejxs3Dj/++KPZUQIaNGiAHTt2oHnz5oZphw4dwvLly+17QQ6aMmWKITxMLpdj06ZNeP/99y2Gk7Vs2RIHDhxAw4YNARSPPjJlyhSX10utVsPPzw/79+83GyQHAIMGDcLUqVMNjwsKCvDXX3+5vC56KpUKUqkUGzduNAmSA4ov2o0dOxaLFy82mv7WW29ZXOZXX32FhIQEw+NnnnkGf/31l0mQHACEhYVh7dq16NGjh2HajRs3rDbSnThxotHF7hkzZuCTTz4xCZIDgMceewx79uxBzZo1DdNWrlyJAwcOWFy+o1QqFXJycoz+CCGEEEIIIYQQQgghhBBSsanVapw5cwa7du3CqlWr8Msvv+CXX34p72oRQkiFlZaZZ7NMoI/zndb9q9Yy3xCUMQgSOTqO+BKdXpxpM0gOALSqAnDRgTA5cHgHhll81iswBM9M+hNKL8uDwGlU+Vg3YyDyM5MM0+wNkmtem4Lk3Im9QXLKMgiS0+Ocl+hPFEUUFhYiPj4eq1atwrPPPov69evjq6++QmFhYYnqlZ6ejrfffhsNGjTAu+++i4sXL0Kj0TjUWVRfx8zMTGzfvh2vvvoq6tati0GDBtndhsPa67d3/nXHE+0KkouiIDm3otbqMGreQQqS80BJmQXoMnUzrt7PtvrZK2QS/P42Bcm5CwqS81ycc1y8Y3+Q3DoKkiMloBO5Q0FyTWoGUpAccRoFyZHyRkFypCxptCLGzjvhUJDcHxMpSO7fGGN45plnsHbtWgQEBBi9N5xz6HQ67Ny5EzExMahTpw769u2LmTNnYs2aNTh+/DhOnz6NkydPYvPmzZg7dy5GjBiBqKgoNGnSBLNmzUJaWppJKJs973/z5s3x+eefQxD+ue7COUdOTg4GDx6MTp06YdWqVbhz5w7y8vJQWFiI9PR0HD16FGPHjkXz5s3NBsm1aNECM2bMsFqHK1eu4MqVKybTX3vttVLbdmrVqoVWrVqZTJ8zZ06JBhZxaxX0bZFIJPjkk0/w119/ITQ01G32N4wx1KpVCxs2bLDYp5cQQiorzjkS0nJNz6M5EBlC+zxCXMY9DosIIWWgWoC3zXM+tUaLQpXtwXQBCpMjhFQgubm5OHDgAEaOHIn+/fsbwuEAoFWrVhg8eLDNZbRt2xZvvPGGzXKpqalYuXKl4XFkZCRmzpxpdR4/Pz8sWrTI6ILWt99+a3NdjsrIyMBPP/1keDxp0iTExMTYnC8wMBDffPON4fGBAwdw8eJFl9fv008/RaNGjayWGTt2rFEw2pEjR1xej4e98cYbaN++vdUyw4cPx9NPP214fP78eRw6dMiknFqtxoIFCwyP/f398eOPP1ocvQX4ZwQYH59/Gk3Nnz/fKDBO7+zZs9i/f7/h8ZNPPomJEydarXt4eLhJuKIrt73PP/8cAQEBhr9atWq5bNmEEEIIIYQQQgghhBBCCHGd3NxczJ49Gx07dkRAQABat26NHj16YOjQoXjllVfwyiuvWJ1/y5YtWL58ueHPXKcAQghxVxl5tgOjfL1tB71ZUqVmI4Q3agejYboZg0zpi74TlqN5zzFggn1NtdSFuXZ1PtXjHJArLYd8MMZQtWZjPPXGYghSC6+Rc+Rnp2DbvFEQRR3upOfbDJLD30FyO6ZSkJy7SEjLRczULTaD5BQyAcvGdyqTIDlX0wespaam4t1330WbNm0QHx/vcAdFzjk2b96Mpk2bYs6cOcjLy3NZJ0fOOfLz87F69Wqkpqa6ZJm21rfueCKGfbPHapAc/g6S20NBcm5DHyS37MBNCpLzMEmZBYiZuhnX7mfDWk8W5d9Bcn0foyA5d+BokNxuCpJzG4YguVjrQXLAP0FynShIjjhJJ3J8tPyEXUFyQHGQ3K5pvShIjjjF0SC5dlEUJEdcSydyTPnVsSC5n0ZTkBxxjj5I7uf9F+zKk9IHyfVsSUFy5jDG0KNHDxw7dgzNmjUzeY/01xDT0tKwadMmvPfeexg4cCCefPJJtG7dGtHR0Xj66afxxhtvYNmyZYiPj4dOpzO5PigIAjp16oQBAwbYVac333wT7733nlGgHACIoohDhw5hyJAhqFu3LsLDwxEeHo6aNWuibdu2WLBgAfLz802C5Bo3bowtW7YY9Xf7N845FixYAFE0Pk8MDg5G27ZtbdbbWYIgmITVcc6xdetWPHjwoNTWS0qHIAjo27cvLl68iLfffht+fn4l2vcwxiCRSPD444/jzTfftFq2T58+GDBgAIKCguwOb7Rn3U8//TTi4uJQr169Ei2PEEIqqmtJZga0YkD1IO+yrwwhxAidwRHieXyUcmvNtAAAIgdUGvsGp6VWHYSQMrdv3z5IpVKjP4lEAn9/f3Ts2BE///yz0QXIWrVqYd26dSYXQs0ZPXq0XXXYunUrVCqV4fGYMWPg5WX7JnTr1q2Ngt1OnjyJe/fu2bVOe/3111/Iz88HUHwhbdy4cXbP+9RTTyEoKMjweM+ePS6tm0KhwMiRI22WCwwMRNOmTQ2PL1++7NJ6/Nv48ePtKvfWW28ZPV69erVJmaNHjyIlJcXwePjw4QgNDbW57Jo1a2LIkCGGx0lJSTh+/LhJuXXr1hk9fvvtt+26SNm/f3/Ur1/f8Hjz5s1mw+qc8cEHHyA7O9vwd+fOHZcslxBCCCGEEEIIIYQQ4saY4Jl/AKKjo9G4cWPMnTu3nD8E4mm++OILREREYNKkSTh06BBUKpWhI4P+z5YTJ05gxIgRhr8JEyaUQc0JIaRiUGm0NsvUCPZ3evmCRIq+76xEVNtBUHgFQCr3RnjDNhjy6V7UbtnToc4rOk2RQ61DGWOQKqw36maMIbJFD3QcOh2MmW9/wTjH/StHsOvgYXSZutlqkBxjQAsKknMrCWm56DxlM26n5VoNklPKJVj+Vmc8+0TtSt8RlXOOc+fOoX379oiPj7d7Pp1Oh08//RTPPvssUlJSXBYiVx7sDZJjDGhEQXJuhYLkPJchSC7J+u+8Ui7B7xMoSM5dOBoktye2NyIoSM4t6IPkusVuRaqVIDnGAD8vCpIjJaMTOT5cdgIz19sOkmMMeLRWIHbF9kK1ADq+JI5zNEiufaNQbPqQguSI6+hEjo9+OY8vNzoWJDeiEwXJEcc5EiTHGOCtoCA5ezDGEBUVhaNHj+LLL79E1apVrb5f/74va+maIGMMgiDg0UcfxcaNG7Fjxw6Eh4fbVSeJRILp06fjp59+gr+/v9mQO61Wi9zcXGRlZRndL36YIAjo168fDh48aLNPXF5eHlauXGkSRNe/f394e5demAxjDH379jXpz5mfn481a9ZU6muupaaCf50ZY6hatSpmzZqFW7du4fvvv8djjz0GLy8vu0LeGGOQSqWoV68eJkyYgDNnzuDQoUN44oknrM4bFRWFP//8E4mJidi3bx/effddtGzZEr6+vhAEwa79oL5+QUFBeO6553DkyBGsXbsWISEhtB8lhLglDuBWcp7JdIExhPjTvX9CXIXbFQVOCCGAQiaFYOPUg4NDo7MvTE7qgjoRQojDdHbspBhjeO655zBv3jwEBwfbtdzOnTvbVe7IkSNGj/v162fXfAAwYMAA7Nq1y/D48OHDeP755+2e35Z9+/YZ/t+wYUOEhYXZPa8gCKhbty5OnjwJADhz5ozL6gUArVq1gq+vr11lIyMjDfXIyspyaT0e1qRJE9SpU8eusjExMfD19UVeXvFJ7rFjx0zKlHTbWLRokeHx4cOH0a5dO4vL149SYa/+/ftj5syZAIDCwkKcOXMG0dHRds9viUKhgEKhKPFyCCGEEEIIIYQQQgghxBPExcXB39/5oBlCHJWZmYkhQ4Zg586dhkbz5hpb29Og/s0338SsWbOQl5cHzjl27NiBe/fuoUaNGqVSd0IIqShEzpGTp7JZzlspL1GnEIV3AJ4atwgaVT64qINc6QsmSBxeTkF2qkN9ghgTIPeyfXzCGEOznmPwIOUGzu/4CeZ6W2t1WrwzbwNuaaNgLWCmeQQFybmThLRcxEzdgoT0PJtBcsvGd3KLIDk9zjnu37+PAQMG4MiRI1AqrW/Toijigw8+wOzZs40Gy7TF0vtVnp0i7Q2SAwOiwgOwm4Lk3EZ5BsmVxTZvq+N3aa/D1Ryps6166YPkrt7PhrUeyAqpBL+/TUFy7oJzjr+OxjsUJFeLguTcgj5IrnvsVqRZCZIDAF8lBcmRktGJHB8tP4FZG2wHyQFA45qB2DWtF6r50/ElcZyjQXLtokKxcTIFyRHX0YkcU349j5mbKEiOlD5HguQAwEsuxaqJXShIzgFKpRLvvPMORo0ahV9++QULFizAtWvXIIqiw+fkvr6+6N69O8aPH4+2bdtCJnP8t0cQBLzyyiuIiYnBBx98gHXr1qGoqMiuugiCgEaNGuGzzz7D008/DYnE9v2JXbt24cGDB0bbC2MMr7zySqlvQ8HBwejYsSO2bdtmNH3evHl45ZVXIJVS9/eHleTTKMur0IwxBAcHY8yYMXj99deRlZWF69ev4/z587hy5QqSk5ORnp4OURTh7e2NmjVronbt2mjevDmaNGmC0NBQSKVSh7Y//fevQ4cOaN++PT777DPk5+cjKSkJ165dw40bN3D79m2kpaUhIyMDOp0OMpkM1atXR2hoKJo0aYJmzZqhbt268PHxof0nIcTtiSLHvQzTMDkvuQQBPvJyqBEh5GF0KEKI5/HzVkBgDKKVszfOgazcIqC67eXR2TQhpELQX7AJCgpC48aN0aZNGwwbNgz169e3exne3t6oVauWXWWvXr1q+L+Pjw8aNGhg93patWpl9PjKlSt2z2uPU6dOGS3b0QufDwf1PXjwwGX1AuBQRyIfn38aE+nD20pD8+bN7S4rCAKaNm1qCHS7dOmSSZmHtw0AaNmypd3Lt2fbeHj5DRs2dGiUFnPLd0WYHCGEEEIIIYQQQgghhBBCKia1Wo2+ffviyJEj4JwbGi3/u7OAvY2ZAwMDMXjwYCxcuBBAcRjK6tWr8eabb7q24oQQUsFwDuQUWg+TY2BQyBwPfjNZDmOQK+0bpM0SjarAbNCb5XUKECT2tS1ggoBOI75EdsptJJ7babQeDiCX++KONgTWguSaRVTBjmkUJOcu7qbnI2bqFtxOy7UaJKeQCfitAgbJ6Tum/XugSs45cnJykJaWBq1Wa7WzJeccZ8+exZIlS/D6669bDX5bvHixXUFy+vDfmjVrok+fPujQoQPq1atnGMSxoKAAd+/exfnz57F//36cOnUKmZmZhvWUNs45NpywHSTHDEFyvRBGQXJuQaMT7Q6SG9HRtUFyQHHbtlGjRrm8XZveiBEjMGjQIIvPazQavPzyy8jNzXVouRKJBBKJBF5eXggNDUXdunXRpEkTNGrUCMHBwZBIJKWyb1SpVBg1apRdg7n26dPH6j4sOas4SO5aUg6s/c4rpBKsmEBBcu6Cc441xxIwYs4+60FyjKNuiD92x/aiIDk3wTnH5XtZ6B67FalWguQYoyA5UnL6ILmZ620HyTFWHCS3e1ovVAug40viOI1WxOgFhxwKktv0YXcKkiMuow+S+3KjI0Fy7SlIjjhFoxUxzs4gOcb+CZLr1bImbW8OYowhKCgI48ePx9ixY5GQkIB9+/Zh165duHDhApKSklBUVASNRgOdTgepVAq5XI6AgADUqVMHbdu2Rffu3dGqVSsEBASU+P1njCEyMhLLly9HUlISNm3ahHXr1uH8+fPIzMyESqWCKIqQy+Xw9vZGZGQkunXrhoEDB6JZs2aQyWR21YFzjlatWhn1ZQSK+981adKkRK/BHoIgYOnSpbh//77RdNp+zSt+XzgcjZXjHA7dc3IVxhgkEgmqVq2KqlWr4sknn7R47duVn7l+vf7+/vD390dUVBQA69fdaZsjhHgalUbEgxy1yfQgXwUU0pK3mSCEFB+1iTrnjsHo2IQQzyOVCJBIGLQ6y2U4BwpUpr/fZpfnonoRQojdOnXqhL1797p8uYGBgXaX1Te+BICQkBAIgv0N3sLCwiwuyxXS09ONHj8cDueo7OzsklbHyMMBcbY8fKBamo1cQ0NDnS6fnZ1t1PEKMP48BUFAtWrV7F52SEgIGGOG12tu23h42r+3JVtKe9sjhBBCCCGEEEIIIYQQQkjFMm7cOBw+fNgQRsI5R2hoKN566y306tULkZGR6Ny5M86dO2f3MocMGYKFCxca7o9s27aNwuQIIW6Pcw6txta9d45qAfYPBlaa1IWOBdwIUjkkUvtHCJdI5egz/hesmd4byfFnAM7BwaCGHDvEp5CFILPzMQY0r10FO6ZSkJy70GhFPDdrF26n5VkNklPKJVhWAYPk9BYvXoyePXuaTNfpdEhJScHChQsxY8YMFBUVWVwG5xxfffUVXn31Vcjl5r9P165dw1tvvWVXkFzLli0xc+ZMtGvXDnK53Oz79thjj6Ffv34AisPlzp07h59//hl//PEHcnJyrK6jpG4m52LY1/vsCpLbE9sboRQk5xY455i39bLdQXI/jXFtkBxQHGi9c+dO3Lt3z6XL1XviiSdsrn/btm3IyMgo0Xr032mZTIY6depgxIgR+M9//oPg4GCX7idPnDiB5cuX29zvAMCZM2fw8ssvQ6k0/Y3W6kQM/HIXriblwFoCgT5I7hkKknMbCWl5eOm7/XYFye2J7YVawSULRSYVR6Fah2e/2GVXkNz697ujY+NQ+t4Tp1CQHClL+iC5JXuvOxgkZ/81E0KscS5IrgMFyRGnaLQixtoZJAdQkJyrMMYgk8lQv3591K9fH6+++ipEUYRGo4FWq4VarYZarYZSqYRMJoNcLjcEzJfG+84YQ3h4OF577TWMGjUKWq0WKpUKRUVF0Ol08PLyglwuN1x/dLQOjDFEREQgIiLC5XW3V0hICEJCQspt/ZWJwGD1Or41ZR8lZ1557p9o30gIIf/ILVQjX60xmR4e5AWJhJXJwFeEeAIbl0stkgh03EKIp5FIBAg2zlkYAI3N9o/FXNvKgxBCypFMZv9oUQ+PLOpIQBoAwwjB5pblCvaMImovexqRVXaOfn4PlxdFEQUFBUbPP/x5ent7O3ShUBAEeHv/07nA3LZRkbc9QgghhBBCCCGEEEIIsZsg8cw/ANHR0WjcuDHmzp1bzh8CcXfnz5/H4sWLjQayefbZZ3Hjxg28//77aN68OQICAhxebufOnQ2DNHHOsX//fldWmxBCKiSRc6i1tu+f+/tUjM7seZlJDpWXypQQJPa3mQAAubc/Bk7biZ7jFqNlrzfQbsgnuBU1BUfEdihufmaMMaBZBAXJuZtrSdk4ceuB9WAhmYDfKnCQHAD4+flBIpGY/MnlctSqVQvTpk3DX3/9ZbNt0a1bt5CQkGD2OZ1OhzFjxpi0M/k3QRDwzjvv4PDhw4iJiYFCobD6vuk7efr4+KBNmzb48ccfcevWLcyaNavUOlFyzvHz7qso1Jh2EvmnXsVBcrun9aIgOTeiFTm+23yp3ILk3AnnHJxzqNVqXL16FR9//DEaNmyIH3/80WVt9jjnWLBggd2dtu7du4djx46Zfe56Ug6O3Uy3ur9XyihIzt1wzvHLvhvIV2stF2IcdUL8sZuC5NzO0atpuJmSYzNIbt373ShIjjiNguRIWaIgOVLedCLH1F8vOBgk156C5IhTNFoR4xwIkvNWUJBcaWGMQSKRQKlUwtfXF1WqVEFYWBgCAwPh4+MDmUwGQRDK5H3XB935+voiODgYoaGh8Pf3h1KpLLM6kPIlOBkqwsFtHj8RQgjxLGk5RdCZuZZTN9TfTEsBQoiznA1mtBUoRQhxPwqZBF4WBr78B0dGbr5dy5OWvEqEEFL5+Pn5Gf6fn2/fDlMvLy/P4rJcwdvbG9nZ2QCKR0o9evSoS5fvbhz9/B4u/+/wN8D48ywoKADn3O4L6v8OpzO3bfj5+RkCAyvatkcIIYQQQgghhBBCCCHEtri4OPj7+5d3NYgH+N///gdRFA0BI506dcJff/1V4o4AgiDgsccew86dOwEU36+4ffs26tSp44pqE0JIhaTR6lCgUlstwxkgk0rKqEZW6sE5dOpCh+YRpFIw5njgj1SuRFS759Gw7XMAgNvVbuOni3tNgmYYA5rXpiA5d5SVrwYXRZgLEASKg+SWv9W5QgfJ2YMxhl69euH555/H8uXLLZYTRRFnz55FgwYNTJ47duwY9u3bZ7XBN2MMH374IaZNmwZBcC6EizGGKlWq4O2338aoUaMglZZOE8/bqXng3Pxnqg+S2xPbm4Lk3IxOx5GVb/n3kILknMc5R1ZWFsaOHYuUlBR89NFHTu8H9HJycrB+/XqHOpr88MMP6Nixo8k+OzW70OpyKEjOfSVlFlgOEWQcdf8Okovw0CA5zjlEUYRWq0VhYSG0Wq2hzShjDHK5HAqFAlKp1DCtsrifWWAS7qWv/8NBcp0ah1Wq10UqDs45vtl4gYLk3JB+36hWq1FUVASdTmfYN8pkMnh5eZX5fpFzjvd/i7M7SK59o1BsnExBcsR1RM7xyfIL+GLjCbuD5BaO6YAXOlKQHHGcTuR484cTWGRHkBxjgJdcij8mdkFPCpIjxO1JGEPxSb5j33UuAlqdawY/IIQQ4h4S0833bY+q4figpoQQS7hd1xDM8ZJTDBQhnkZgDAqZ9fYFHEBmbpFdy6O9CCHEIwUFBRn+n5qa6lBgWHJyssVluUJwcLAhTC49Pd2ly3ZHqampDpVPSUkx/D8gIMDkc3/48xRFEWlpaXaP9Kzflswt6+Fp+jC5f29LtpT2tkcIIYQQQgghhBBCCCGEkIpBp9Nh69atYIyBcw6pVIqFCxe6rBNI69atDWFyAHD58mUKkyOEuDVR5LY7twOQ22iUVVa0GvsafunJFN5gJQis0f++9GlVC43CA3D5Xhb0nZEY42gWUZWC5NxUjSo+kEklUGtNO5IpZAKWuUGQnB5jDC+99BJWrFhhMVCJc46bN2+atCPinGP27Nk2g+RiYmLw8ccflzhASr+80gyxbhMVgpWHbptZMUdUeCB2x/aiIDk3JJMKiAzxQUaeyuQ5CpJzDVEU8emnn6JTp07o1KlTiZa1bds25OTk2F2ec47NmzcjKyvLpF1ZvTB/yCSC2f09Bcm5t2a1q4AxmAbvMI46LgySu3XrFjZu3OjUvIIgQC6XIyAgAHXr1kVERASqVKliCCpyNZ1Oh6SkJBw4cAA7duzA6dOnkZSUhNzcXGi1WkOwvyAIkMlkCAgIQHh4OJo2bYqOHTuiTZs2iIiIgEKhsLt+nHOsX78eCQkJFssEBQVh+PDhdh9HcM5x8OBBnD592uzzWw+fA+LOAxCAGq2B8JaG5yhIjrhCTqEG0/88Y/NcGyi7ILmcnBwsWbKk1JY/dOhQVKtWzeLzGRkZ+O2332wup3379mjZsqXd3z+VSoVFixZBq9WafV4qlWLkyJFQKBR2Lc8cURSRkpKC3bt3Y9u2bTh58iRSUlKQn59vCJMTBAFSqRQBAQGIiIhAu3bt0Lt3b0RHR8PPz69U9ydJmQWYv+2yXUFy7aIoSI64Fuccm07cwWfr7Q+S+2l0ewqSI07hnGPRzqtYuP+izSA5gILkCPE0goSBM2Y5NN4CEUCBWlcqdSKEEFI53UrJNXuO3bD6333unQzAIoT8g3NAq3PuuxTsT21zCPE0jDFIJLbvEao09p3bUZgcIcQjRUVFYc+ePQCA/Px8XLt2DVFRUXbNe+rUKaPHjRo1cmndGjdujJs3bwIAbt++jczMTAoNs+LMmTN2lxVFEefPnzc8bty4sUmZf28Hp0+fxlNPPWXX8u3ZNqKionD7dnFj3GvXrqGgoADe3t4uWz4hhBBCCCGEEEIIIYQQQiq/48ePIycnB4wxMMbQvXt31K1b12XLr1GjhtHj+/fvu2zZhBBSEYmiPSP+MgR6V4wGmfmZjg1MJlP4gbGSB//4KGXYHdsbY388jH2XkiEw4OnHamH2y08gyMf5Tumk4oqo5oOXOtfHwl3XDH0CGAMUMgl+Hd8R/d0kSE4vLCzMZpnCwkKTadnZ2dixY4fVMDmZTIbvv/8eMpmsRHUsC4wxvNipAb7deBHxaXlGn70+SC4s0L62LKRyERgwY8Tj6P2/7UahYhQkZ0wmk8HHx8dkulqtNuwjrO0PtFotPvroI+zbt8/pcElRFDFv3jyr6zEnNzcXGzduxIgRI4ymh1fxxitdGuDHnVeN9/dSCVa83ZmC5NwUYwzDO9TD1+sv4mZqjtFnHxnijz3TXBMkBwAXLlzAW2+9VaJl6LdBmUyGiIgI9O/fH//5z39Qt27dEge1cs5RVFSEDRs24KuvvsLp06eh0WhsfsdUKhXy8vJw7949xMXFYfHixWCMISwsDH369MHIkSPRsmVLyGQym9+hBQsWYMuWLRaff+SRRzBs2DC7X8+ff/6JESNGQKUyDQjVY0wAIp4ED/m7rSkD/JQyrH2PguRIycWn5iKnUGO1DGPFQXK7yiBIDigOc3v77bchiqbhqSXFGEPHjh2thsmlpKTgv//9r819S4MGDXDu3DkolfZdg1CpVHj33XdRUFBg9nmlUolhw4Y5FSan0+lw5MgRTJ8+Hfv27YNKpbJaf7VajYKCAiQlJeH48eP4+uuvERAQgGHDhuGdd95BZGRkqexbziVkQqXVQR98bw5jQNuoUGz8kILkiGuJHIhddcruILkfX2+PEZ3q0+8scYpGK+Kz1WdtD4zCioPBV1GQHCEeReLkfSDOOXR2hEATQgjxDJzzvweXM8bAERFsem+EEOI8Z4/BZBI6xyPE0zDGILMjTE6tMT/ozL9RmBwhxCO1bdsWP/zwg+HxunXrMGnSJLvmXbNmjcmyzHm4cahOZ//oDV27dsWGDRsAFDcKW79+PV566SW75/c0ly5dwu3bt1GnTh2bZffs2YO8vDzD4yeeeMKkzL8/z3Xr1tkdJmfPttG2bVts3boVQPF2sXHjRgwaNMiu5a9du9bwfy8vL7Rs2dJyYUIIIYQQQgghhBBCCClVDNY67bin4tcbHR0NiUSCcePGYdy4ceVcJ+Ku9APT6HXq1Mmly//3QEa5ubkuXT4hhFQ0Iud2dcBTKCpGCJRGle9QeYlM7rJDs9BAL/z5bgyK1DowxqCQCdQZ0Y0JjOG7UW1Qu5offtp5FXmFGjSqGYCZLz6OJxpUc7vPvqioyGYZiURiMu3cuXNG7U3MadeuHRo2bOh03cpagLcMB/7XBxMXH8fei0lgDOjarAa+fvkJGuncjTHG0OXR6tjy4VOY9OtxxKfmIchXjjd7N8bopx6xq3FyWahVqxa6devmVHiUK9pTjRkzBp9//rnJdI1GgytXruCjjz7Crl27rAatxMXFIT09HSEhIU7V4d69ezhy5IjD83HOMX/+fAwbNsxofyYwhm9HPol6Yf6Yv+0ycgo0qF/dHzNfjEa7RqFut78n//D3lmP///rgvz8fxd6LSRAY0L15Tcx6MbpMgp0cof9OqdVq3LhxA7NmzcK3336L5557Dl9//TWqVXPu2ESn02HTpk2YMGECbt265XBI47/ryDnH/fv38dNPP2HRokVo3Lgxjh49ajaEsjRwzrFq1Sq89NJLVoPkpFIpJk/9BNmh7fDn8XsoUOnQsm4VfPXKE2gWUYW+96TEpJLic0VL3yl9kNzuMgqSq0xu3LiBX375Ba+99lq5fRc550hISMCYMWOwfft2pwL49J99VlYW5s+fj8WLF2P8+PGYMmWK3QOt20suEwBuPUiu3d9Bcv4UJEdcTKXR4VbqP0HslsikDD+NpiA5UjK5RRokZ5kPD32Yl1yKVRO7oBcFyRHiURQyAQyA42e1DDqd6wOXCSGEVF7X7mebTGOCgBpVKUyOEFcROYdG69wxWEW5Z0sIKTsCYwj08wKSs6yWS8+xfd0IoDA5QoiH6tmzJxQKhaEhxfz58/Hmm2/Cy8v6zerTp09j9+7dhsfR0dGoUaOG2bJ+fn6G/2dmZtpdt4EDB2LSpElQq9UAgM8//xyDBw+2e/QxT8M5x3fffYevvvrKZtlvv/3W6PGAAQNMyjzxxBMIDQ1FSkoKAGDZsmWYOnUqQkNDrS77/v37+P333w2Pw8PD8fjjj5uU69evH6ZMmWJ4/PXXX+P555+3eQNn/fr1uH79uuHx008/DamUfsYJIYQQQgghhBBCCCGkrMXFxcHf37+8q0HcXFpaGoDi+yCMMYv3o5ylUCgAwHB/wp5gFUIIqcwKVRq7RvyVOBFaUxp0OrVD5SUSGVwZ9MsYg5eC7kd7CrlUgg8GNMO7/ZpCxznkEgGC4H6dUDnnOHjwoNUyjDGToBr9fNaCZxhjGDFihFPBV+WFMYbwIB8sf7sz1H83YpdLKTzSEzDG0PnRMBz94hlotCIkEgapwCrUZ//8889j1qxZ5bb+4OBgeHl5mX1PnnjiCWzcuBF9+/bFzp07Le4b1Go1bt265VSYHOccK1asgEajMfu8vl6W1n3ixAncvXsXtWvXNpoul0ow8ZlH8VafJtBxDplEgMQN9/fEVFigF1a8Xfn295xzqNVqrFixAnv37sXGjRvRokULh+qem5uLMWPGYMWKFU4FJdkiiiJu3LgBtVpdJmFynHP8/vvvePnllw1tjM2RSqX45ptvMGbMGDDG8OXLInQih1ImccvjPFI+6ob6ITzQC3czTDsu6YPkdlGQnFmcc3zyyScYNmwYfH19y2X9GzZswMsvv4ysrKwShWw+vMzCwkLMmDED27Ztw9q1axEREeGy35vmtavC30uG7ELT4yPGgLYUJEdKkURg8JJJkGWljEzK8OPrFCRHSk4mFSCTSKDWas0+zxiglEkoSI4QDxXg7eyxjgitHffICCGEeAadyJGQZjqIllIqQZAPnVcT4io6kSNfbf7czhZpJbmPQwhxIQYoZbbb6qVmWh8IU6/ytF4ihBAXqlatGoYMGWJ4HB8fj0mTJlmdJy8vDyNHjjRqUPLWW29ZLB8ZGWn4f35+Pi5fvmxX3WrWrImRI0caHl+9ehUvv/yyxcZh5nDOsWvXLrvLV3bff/+9zZFYf//9d2zYsMHwuGnTpmjXrp1JOblcjtGjRxse5+Tk4PXXX4dOp7O4bK1Wi1GjRhmNQj127FjIZKaj1jdr1gydOnUyPD569Chmz55tte5JSUl44403jKZZ2/YIIYQQQgghhBBCCCGEEFK5/TvcTR/+5ioZGRkA/gkfCAgIcOnyCSGkolFpdBBhvaMMA6swgSqixrEwOW+/4FKqCfEUjDHIpIJbB4zk5uZizpw5NoMSmjZtajLt5MmTVudhjKFt27Ylql95YYxBIZNAIZNQg3QPwljxb55SLoFMUvE6I0gkEgDF9XTmr7QpFApMmzbNZrn79+87tXytVouff/7Z4v7Ky8sLAwYMsPhatVotfvvtN7PzP7y/ryjHPaRsVOb9PeccSUlJ6NatG65cuWL3fGlpaejcuTOWL19eKkFyZY1zjuXLl9sMkpPJZJgzZw7Gjh0LQRAMn723Quq2x3mkfChlEsx7vS0UMgEP71YY/gmSC6EgOYvu37+PuXPnuiTIzRH6UMpBgwYhMzPT5evnnOPs2bNo27Ytrl+/7rLlV/GVY9bLT5gcv+iD5DZNpiA5UnrkUgG9WtWCpUMofZDci50pSI6UnK9ShvaNwsw+xxjgJZdi1cQYCpIjxENJS3AdT62p/OfFhBBCXKNIo0Natumgo1V85fCS04BzhLiKKHK7Br00Ryml7yIhnoYBkP7dTsIalcZy5s3DKEyOEOKxPvnkE6OOMd9//z1Gjx5tFAimd/PmTfTo0QOnT582TGvXrp1RIN2/tWnTxujx66+/jlOnTlkNJdP73//+h/r16xser1y5Eh07dsShQ4esznf37l18++23aNasGQYOHGhzPe5AoVBAo9GgT58+WLNmjcnznHMsWLAAL730ktH0b775xuIyJ0yYYDQy67p16/Dcc88hNTXVpGxKSgr69++PLVu2GKbVr1/fatjbrFmzjILmJk2ahGnTppkNDDx16hRiYmJw584dw7TBgwebDcIjhBBCCCGEEEIIIYQQQoh7qFatmtHjzMxMly4/MTHR6HHVqlVdunxCCKloRG5fJ5kKEarCRRTlZjg0i0TpXUqVIaTy45wjOTkZAwYMMDkG+jcfHx80adLEZP74+Hir8ykUCoSGhtpVl7S0NKSkpDj0V1hYaHPZhJCyVbt2bQiC9SbYjgwe+7ALFy7g+vXrFp/v0qUL5syZA19fX7PPc87x888/O71+QioizjkyMzPx/PPP2/W7mJmZia5du+L06dN2BxkxxiCRSOD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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#create 2 plots: supplements and main text\n", - "fontsize = 28\n", - "width = 50\n", - "height = 15\n", - "# select colormap\n", - "cmap = None#plt.cm.get_cmap('magma')\n", - "\n", - "# gridspec inside gridspec\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs0 = gridspec.GridSpec(1, 2, figure=fig, width_ratios = [3,4])\n", - "gs_pam = gs0[0]\n", - "gs_figure = gs0[1]\n", - "\n", - "#adjust labels for better readibility\n", - "x_csc_label_pam = adjust_heatmap_labels(x_csc_nonzero_pam)\n", - "x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam)\n", - "\n", - "fig_pam = make_heatmap_subfigure(results = results_pam, csc_matrix=csc_nonzero_pam_t, esc_matrix =esc_top5_pam, \n", - " ylabels = True, xlabels = True, x_csc=x_csc_label_pam, x_esc=x_esc_label_pam, \n", - " yaxis = glc_uptake_rates, fig = fig, grdspc=gs_pam, \n", - " annotate = 'A', phenotype_data = pt_data, fontsize = fontsize, cmap = cmap)\n", - "fig_pam.subplots_adjust(left=0.3)\n", - "#set common x axis title\n", - "# fig_pam.xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - "\n", - "#add image\n", - "\n", - "ax_fig = fig.add_subplot(gs_figure)\n", - "ax_fig.imshow(sensitivities_mapped)\n", - "ax_fig.annotate('B', xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.30), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize*1.5, weight = 'bold')\n", - "ax_fig.axis('off')\n", - "ax_fig.set_xticks([])\n", - "ax_fig.set_yticks([])\n", - "\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/Figure2_sensitivities_pam.png', dpi =275,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "1a2a55bd-67a7-4bda-9dd0-2716e5d0deb6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_74560/2464939843.py:20: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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z3T0wszHDiU/qwszYdbEYu2wsnHqJw3nRinapBLO3zoa9j32Tc+82qBtCP218hU9DymolpCZSPLL7EXiP1d7PVCKRYMSzI2AuN8f2R7cL9+9ZvAfPnH9G7/PoQ62s+1D87q/vxsjnR+rsIzMT72fqOsAVc7bP0Wt8n3E+eOLYE/gh8AeUpJdAVaPCqe9OIeQj436QWl1SjYNvHhTaUlMp5v01D16jvXT2H/jIQNi42+DnqT8LP7OI/4vAoEcHNXmxQT2lQgm3wW54/NDjOisqyExlmPrJVGTFZCFxXyKAuioMhYmFTV7oQURE1FINw9ChQ4e2ekw7Ozv4+/vj+vXrAIDy8nJcv34dffv2BQDMmTMHixcvFlabr1mzRu+wfc2aNcJtKysrPPTQQzr77dmzB6+//rrQ7tOnD7777jtMmTJFq29tbS1WrVqFV155BVVVVSgvL8fs2bMRGxsLM7Pm/1//4YcfoFQq4e3tjbVr1+osCa9QKGBmZoaffvoJAHDo0CFRv/3792PixInNnktfqampeOqpp0RB+z/+8Q989tlnsLW1FfW9du0a5s+fj5MnTwIAsrOz8eijj+Lw4cNa+9jLZDJMnDgRO3fuBFAXnDdm//79wgUXMplMmEtTYbvmeNbW1hg1apS+3zIRERER3aaWbYxIRETUgajVapSUlBhkHz6izk6tVqOqqKpu5fMzu7Hx7o3CKm5IgHt/uLfRkPH4x8eFMuMO/g6Y9u00vVYGTXp3Euy86lZUq1VqnF11VqtPWfatkulWLlbNBu23a8TzI3QG7ZoGzRuE3vfeWt2fcyEHqcdTDT4Xr9FejQbthmDpaImgxUFC+9rua212Ln1Fr4kWbVkQ/HJwo0F7Pb9Jfhjx3AihXV1c3exe75qmr5ne6NYF9Ua9JP6AOT0qXe/xiYiI9JGXlydqe3k1/f+fvjw9PUXt/Px84badnR1mzZoltHfv3i16vDF5eXnYvXu30J41axbs7Oy0+lVVVWHhwoXC71lDhw7FyZMndQbtAGBiYoJnn30Wv/32m/AeMj4+XlSuvilKpRKOjo44cuRIo3uv6xPaG9K///1vYfU5ADz33HNYtWqVVtAOAL169cK+ffsQGBgo3Hf8+HH88ssvOsfWXG0eExPT6HOnGZxrhutNBfSaj40bN67df25EREREdyKG7URE1GmVlpZCLpejtLRl+zsTdXYph1PwjuQd0de70nfxscPHWDt+Lc7+cFZYLWznZYe5O+Zi8ILBOseqqajB5W2XhfawRcO0yp43xsTcBH1n9BXayQeTtfporlIuzylv8X7s+gp6Maj5ToAopAYg7K1uSMOebtkepbfDdeCtsum5l3NFQbcxXNl5RbgtkUm0fs6NGf3KaEikty7s0BynKV5jvOA22K3Zft7jvAGN60Zy43L1Gp+IiEhfhYWForZcLjfIuPb29qJ2QUGBqK1Z/l2hUGDjxo3Njrlx40bU1NToHEPTunXrkJWVBaBuRfUvv/yi1/d1zz33YPbs2UJ7xYoVzR5T76OPPoKPj4/e/dtSTk4OtmzZIrR9fX3x3//+t8ljbG1t8dNPP4kuWP3qq6909tUM29VqNfbv36+zn+b9zz//vHABRmZmJi5duqTVv6SkBKdPn9Z5HiIiIiJqOwzbiYiIiLoo34m+ePbSs+hzf59G+6SdSLu1Ah6Az/iWfcipWTY+OzZbq9JE95HdbzXUwJYZW5B/rfmVVy3h0t8FDn76lQb3m+wnugDgxskbBp0LUPdzv12lGaWI/CISv835Dd/3/x6fdvsUH1h9gHdN3hV9/TLt1koptUrdZhcx6EOlVCHjdIbQ7j6iO2zdtVd96SL3lsN9mLvQTj+ZDrWq+Wol+r5OzW3NYeloKbSriqr0Oo6IiEhfDS/8tba2Nsi4DccpKSkRtSdNmiTaE3zt2rXNjqnZx9fXt9Gy6+vXrxduh4WFoU+fxt9LNjR37lzh9rlz57TmrYuNjQ0effRRvc/R1vbs2YPq6mqh/cwzz8DS0rKJI+oMGzYMkydPFtpnz57FjRva7zX79u2L7t1vvUfWtVI9ISEBSUl12zE5OTlhyJAhovBc1zGHDx9GbW2t0G6sEgERERERGRb3bCciIiLqhCQycZn3+pXsmpIPJWPNuDV4LOIxWDlb6Rwn81ymqL16zOqWTUTjtKpaFapLqmEhtxDu8wzyhGewJ9Ij68p33zh1A9/2+RbeY7zhf5c/vMd6o/uI7jC1Mm3ZeTW4BTa/wrmeRCqB60BXYT65lw270tnUyhRyr5avaKssqMS+pfsQszpGr7BZ6/jCyhYfYyilN0pFK+vdhuj/fACA+1B3IaxXlCpQcqOk2Z+hbXf9wnwAMLM2Q2V+3c+npqymmd5EREQtY2NjI2qXl5cbZNyG4zQsXy6RSLBgwQIsX74cABAdHY3z589j0KBBOseLiYlBTEyM0F6wYIHObYMqKipEq6PHjx/fonn36tVLuK1SqXD+/HmMHTu2yWNGjhwJKyvd71WNITIyUtSePn263sfOnDlTtCL9xIkTotX+9aZMmSJc1KArONe8b8qUKZBIJAgJCREumNi3bx8WL17c6DHOzs6isvZERERE1HYYthMRERF1Mj4TfLDg0ALRfdWl1ShOLUb8jnic+voUynPqPqDNjs3Gz6E/48kTT8LEQvutX0VehaitK7RviepicdgOAA9ueRAbQjcgL/7mnqZqIPVYKlKP1e2XLjWRwmO4B/xC/ND/of7oNrBbi85p3a1lK8hsut36ULy6uBpqtVqvPer1YWFv0XynBsqyy7Bu0jrkxeU137kRtVW1zXdqIw2Dfhs3m0Z66tawf1VhVbNhe3N7tYtoPLUNKy8QERG1lqOjo6ituc93azQcp+F5AODxxx/HO++8I/z/tmbNGnzxxRc6x9Nc1V4f1Oty8eJFUan5119/HW+88UYLZ3+LPnvJ9+3bt9k+7enKlVvb2lhbW4suIGjO0KFDRe34+Hid/TTD9qSkJCQmJqJHjx7C45rBef2Kds2V7UeOHEFtbS1MTEx0HjN58mSDvb8lIiIioqaxjDwRERFRF2Buaw7X/q4Y/8Z4PHPhGbj0cxEey4rOQsT/aa+YAQxfVlvXqmy5lxz/PPtPTP5wMuw87bQeV9WqkB6VjqPvH8UPg37A+inrkX0+W+9zmlq3bFW8Zn+1So2aCsOtdpaatvzt9R9P/CEK2q1drTH61dF4eNfDeC7uOSwtWoo3qt7A2+q3ha/HDz4uHsSIGbKiVLxffEufD82y/kDdhSNERESdhZOTk6idnp5ukHEbjtPwPEBdKfhJkyYJ7YZ7sterqakR7ek+efLkRvdHz8sTX/ynUqmgVCpb9KVJn4sPGu5Pb2yFhYXCbVdXV0il+r+/c3MTV/jRHEtTw/3UNYNylUqFgwcPavXt1q0bBgwYAKBu+4KoqCihT2ZmJi5fvtzo+ERERETUdriynYiIiKiLsXa1xtydc/FD4A9CkHzqm1MYvHCwVsn1huXbn4t7Ds59nQ0+J1MrU4xbNg5jl47FjdM3kHwoGemR6Ug7nqa1uj7pQBL+F/Q/zNk+Bz3v6tns2DXlLQvLNftLpJJWlbBvrRunbuDaX9eEts94Hzy862GY25k3eZxm2XZjM7MVh+UtfT4afi/mtk1/70RERB3JoEGDsHXrVqF97tw5PPzww60as7S0FNevXxfaVlZWja6uXrhwIQ4cOAAAyM3NxZ9//okHHnhA1Gf37t2iEH3hwoWNnruoqOj2J66DSqVqto+pqfHei+lSWloq3La2bmEFpQbbCmiOpcnDwwMBAQGIi4sDUBe2//Of/wRQtyVAfUUAf39/+Pn5CceFhITg4sWLAOpKydeX6G9Yip5hOxEREVH74cr2Diw3NxdmZmaQSCTCV2t/YSMiIqI7g2NPR4x7c5zQVqvU2P9/+7X6NdzLvWHwbWgSqQSeQZ4Yu3Qs5u6Yi3/l/AtPn38ak96bBLn3rdLhtVW1+P3R31FV3PzK+/qS+foqyy4TbpvLzY1aYvPKrltlSiEBHlj3QLNBOyD+HozN0sFS1C7LatncGva3cGh5KX4iIiJjabgfecP9vm/HyZMnRVufBAUFicqFa5o5cybs7G5VDtIsF6/rPjs7O8ycObPRczfcO33FihVQq9W3/dVYufqOzNbWVrhdXt7C95ll4vc1mmM1NGXKFOH2gQMHhAsTdJWQ19XW7Kd528/PTxTQExEREVHbYtjegf3yyy9a5b927NhhsP2/Orrly5cLX4cOHTL2dEQ68tyIiIjqjXppFGw9bn3Ad33PdaRHiUuSapabB4CMsxntMrd6EokE3QZ2w/g3x+P5q8/DP8xfeKwyvxJxv8c1O0ZWTJbe51Or1Mi5kCO0G37/7S3/yq19TJ37OsPe116v4zLOtO/z1BTb7raiUvBZ0fo/HwCQeS5TuG1mawa77tpbDRAREXVUI0eOhIXFrQvFTpw4geTk5FaNqVnyHQDGjx/faF8rKyvMmTNHaP/111/Izc0V2jk5Ofjrr7+E9ty5c2FpKb5QTpOzs7jCUcOy8ncCBwcH4XZOTo7owofmZGWJ3wdpjtWQZnCen5+P6OhoAHUr1nX1AYAJEyYIlQBOnTqFkpISAMD+/fsbPYaIiIiI2hbD9g5M19XIVVVV+PXXX9t/MkbwzjvvCF8dLdDuyHMjIiKqZ2ppijFLx4juO/zuYVHbd5IvJNJbK7uv7LgCYzExN0HoZ6Gi+7Jjm9+7PfdyLgqTdO+H2VDSwSRR2fLuQd1bNkkDqyq6tXK/4QrxxihrlC16njT3kVcrDb+5u1QmRfeRt36ON07fQGmm7pKpDRWnFSPz7K2w3TPIU/R6JCIi6uisrKxEVQjVajVWrVp12+MVFhbit99+E9oymazJsu+AuCx8TU0NNmzYILQ3bNiA2tpanX116du3r6jqz9mzZ/Wee1fRp08f4XZ5eTmuXr2q97Hnzp0Ttfv27dto34kTJ0ImkwntiIgIVFVV4fjx4wAAqVSKyZMni46xsbFBUFAQAKC2thaHDh1CfHw8bty4IfRh2E5ERETUvhi2d1Dnz59HTEyMzsd0hfBEREREugz75zDYuN/aO/L639dFq6ItHSxF+6InH0pG0sGkdp2jJoce4tU/SoWy+YPUdXvS6+PkVydF7YCZAXrPrS1o7k9elFKk1zHn/neuRaXaNc9RWVip93Et0Wf6rQ+l1Uq11s+5MVFfREGtunUBQN8ZjX8gTURE1FEtXrxY1P7iiy+QkJBwW2MtW7ZMVIp8xowZ8PHxafKY4OBgUair+bmR5u2AgACMGjWqybGcnZ0RGBgotA8cOCCsnr5TjB49WtTeuXOn3sdu3769ybE0yeVyDB8+XGhHRETg2LFjqKqquxhz6NChcHR01DquYSl5zRLyEolEK6AnIiIiorbFsL2Dahio15eIAupKkl27dq2dZ0RERESdkYmFCUa/Kv6Qr+Hq9glvTxC1tz28DfnX8tEShYmFKEzUXl2eFZulX2B+U8qRFFFb37Lqp749hbTItCb7XNx8EVd33VqZ5DrQFd5jvPWeW1tw6X+rjH3pjVJc+6vp93hZMVmIeC2iyT4Naf4Ma8prkBuX23jn2zR4wWCY2d4qJR/5eSTSTjT9fCQfThZdJGEuN0fg44FNHEFERNQxBQYG4qGHHhLaVVVVWLBgASorW3aR259//okff/xRaJubm+Ott97S61jNFevnz5/HuXPncPbsWVy4cEFnn6Y88sgjwu2SkhJ89tlneh3XVdx1110wN791seKKFSv0ei6jo6Nx4MABoT1ixAh07950FSXN4Pz48eP4888/dT7W2DENw/bAwECtrQCIiIiIqG0xbO+AamtrRftzWVlZ4fXXXxf1Wb9+fXtPi4iIiDqp4U8Ph7WrtdC+uuuqaJ/z7iO7Y9TLt1Y5lWeX48cRP+L0itOorapFY2qrahG/Mx5bH9qKb3p/g+zz2iXfo76Iwpc+X+LAvw8gK7bpvbxTjqRg50KNlUMSoO8Dza90lpnLoKpR4Zd7fkHcdu093tVqNc6sPIMdj+8Q3X/Xl3c1O3Zba/j97Xh8B1KOpujse3HzRaybvA6KMgVMrU119tHFM9hT1N69aDcyz2VCpVS1fMKNMLczx6T3Jgnt+ufj0tZLOvtf3HwRm+7bBFXtrTmE/CcEZtZmOvsTERF1dCtWrBAFq8eOHcOsWbNEq9SbsnfvXjz44INQqW793/jhhx9i4MCBeh0/f/58UUnyNWvWYM2aNUJbJpNh/vz5eo317LPPwsXl1gWBH3zwgdaK7eYUFxfjzJkzLTqmo3BxccHcuXOFdnJyMl577bUmjykrK8OTTz4pev4aVjzQZcqUKcLtyspK0RYEjYXtQUFBsLW1BQDExcUhPDxc53hERERE1D5MjD0B0vb3338jJydHaD/wwAN4+umn8f7770OprFsZtn79erz77ruifbSIiIiIdDG1NMXoV0dj36v7hPuOvHcED227tQJr6idTUZRYhPgd8QCA6uJq/PXsX9j/f/vhPc4bjr0cYSG3QE1FDSryK5B7MRfZ57ObDOPrlWWV4ej7R3H0/aOwcbOB+1B3OPg7wMLeQnj8xskbWmF90ItBcOrt1Oz4I54dgSt/XEFhQiF+nfkrXPq7wHeiLywdLVGWXYbE8EQUJReJjhm1ZBT8Jvs1O3Zbcx/qjr4z+iJ+e93PvSKvAmvHr4X3WG90D+oOUytTlGWVITEiEUVJRQAAUytTTPlwCvYs3qPXObqP6A63wW7CBRapR1OxatgqSKQSmFiYABpvJ18ve72RUZoX9GIQkg8m48rOuv3kq4qq8NtDv+Fgn4Pwm+wHK2crVORVIOlAEvKviCsn9J/TH8OfHq5rWCIiok7B0dERv/zyC8LCwoQy4H///Tf69++PL774Avfffz9MTLQ/hktPT8cHH3yAlStXQq2+tbXK9OnTsWTJEr3P7+7ujrvuuktYGb1p0ybReHfffTfc3Nz0Gsva2ho//vgjZsyYAbVaDaVSiVmzZmHJkiVYunQpXF1ddR6nVqsRGRmJLVu2YO3atVi4cKGoTHpn8u6772LHjh0oLi4GAHz77beoqanBp59+ChsbG1HfhIQEzJ8/H9HR0cJ9Y8aMEQX2jRk9ejSsrKxQUVEBAMKflpaWGDt2rM5jTExMMGHCBOzevRsARKvuuV87ERERUftj2N4BrVu3TtSeP38+3NzcMHXqVOzZU/ehampqKg4ePNiqfZgKCgpw4sQJZGZmIj8/H1KpFE5OTggICMCQIUNgaWnZ4jEVCgUiIyORlJSE3NxcSCQSuLq6YtCgQQgMDDTKxQFKpRKXL19GfHw8MjIyUFZWBisrKzg6OmLgwIEIDAwUXf1tDEqlEqdPn8a1a9eQk5MDpVIJFxcX9O3bFyNHjjT6/IiIqPMb/sxwHP/kOCpy6z7Ai9seh5yLOXAdUPdhqVQmxUPbHsLhdw/jyPtHoFbWfThbXVKNa3/qt32NzLz5/6/KssqaLZUOAEP/ORShn4XqdV5zO3PM+X0ONk7biNIbpci9lIvcS42XSh+2aJjeY7eH6aunozChUHSxQeqxVKQeS9Xqa2Zrhoe2PQSZacveG8zYMAMbwjag9EapcJ9apUZNRc3tT7wBiUSCh357CH889Qdi18UK9+dfydcK1zUN/edQ3PP9PQabBxERkbGMHz8eO3fuxOzZs4V9zlNTUzFr1iw4OTlh0qRJ8PT0hI2NDbKysnD58mVERkaKQnEAmDVrFjZu3Njiz1AWLlwohO35+flaj7XE9OnT8d///hevvvoq1Go11Go1Pv/8c3z77bcYOXIkAgMD4eTkhJqaGhQVFeHKlSuIjo5GYaH2tkKdkbe3N/73v/9h7ty5wsKXlStXYtOmTbj77rvh7+8PhUKB8+fP48CBA6itvXUBardu3bBhwwa9PssxNzfH2LFjRavTAWDs2LGiUvYNhYSECGF7PTMzM4wfP74l3yYRERERGQDD9g6moKAAu3btEtr1ITsAPPbYY0LYDtSF8i0N29VqNbZt24ZPP/0Up0+fFpW30mRhYYGJEyfiH//4B2bOnNnsuNeuXcPy5cuxc+dOlJeX6+zTrVs3LFmyBC+88AKsrKx09lmwYIHWxQYA8M477+Cdd97Reczjjz+utcd9UVERduzYgd9//x2HDx8WfsnVRS6X46mnnsKrr76Kbt26NdrPUHPTlJGRgffffx+bN29u9BdSe3t7LFq0CEuXLoWDg0OjYxERETXFzNoMwa8EY///7a+7Q123uv3BLQ8KfSRSCSYun4hB8wfh+CfHcXnrZVQVVjU+qAToNqgbet7dE4HzA+HSz0Wry7g3xsGxlyOu/30dGWcyoKxufP92iUyCHlN6YPRro9FjSo8WfX/dBnXDouhF2PvSXlzedlnneRx7OSLkPyEImBnQorHbmoW9BZ448QQOvHEAZ1edRW2ldrUAUytT9H2gLya9NwkOPRyQfCi5Redw7e+KZy8+i5h1MUjYk4CcSzmozK9ETWUNoG7+eH1JTaR4YO0DGDhvIA4vP4y0yDSd40ukEniN9sKk9ybBd6Kv4SZARERkZKGhoThz5gyeeOIJHDt2TLg/Pz8fv/32W5PHWllZ4f3338dLL710W4sV7rvvPjg5OWkF7c7OzrjvvvtaPN4rr7yCnj17YsGCBSgqKgJQt8ji2LFjou+tMU2FxZ3Bgw8+iN9++w3z5s0TVpyXlJRgy5YtjR7Tu3dv/P333/D19dX7PFOmTNEK25tboa7r8VGjRjX6eRsRERERtR2JuuHls2RU3333HZ5//nmhvWTJEnz++ecA6spCubm5CcGxtbU1srKytMpXNSYrKwuzZs3CiRMn9J6PXC4XfqFqzEcffYS33npLdBVvU/r27Ys///wTPXpof4jeWKDdFF2B9ksvvYSvvvqqReN069YN27Ztw5gxY3Q+bqi51Vu3bh2eeeYZUbmvpri7u2PXrl0YNmxYi+ZA1JWVlJRALpejuLgYdnZ2xp4OUZejVqmRFZuF3Mu5qMyvRHVJNUytTGHhYAHHno5w7e8KS0f9K+HUVtci52IOCq4XoCyzDIoyBaSmUljILeDYyxHuQ9z1Hu8dya0L3Sa8PQETl08U2lVFVUg5koKS9BJUl1TD2tUabkPc4D7EXe+5Gkt1STVSjqag4HoBasprYO1qDdvutvAZ5wMzm863n3l5TjlSjqagLKsMVUVVsLC3gK27LbzHecPaxdrY0yMiImpTu3fvxooVK7Bv3z7U1DReTcbLywvz58/Hiy++2OQiAH0sXrwYX3/9tdZ9X3755W2PWVxcjG+++QY//fQTkpOTm+zr4+ODqVOn4uGHH8akSZMavWhA8/63334by5cvb9GcDh06hEmTJgntgwcPYuLEiU0ek5ycDD+/W9sIrVmzBgsWLGj2XDdu3MDy5cuxefNmlJWV6ezTvXt3vPjii3jxxRdhYWGh1/dQ79y5c1qf9Zw9exZDhw5t8rju3bsjIyNDaL/77rv497//3aJzExEREVHrMWzvYEaOHInTp08L7XPnzmHIkCFC+8knn8Tq1auFtr6/GCQmJmL8+PG4ceOG6P6ePXti3LhxcHNzg0QiQU5ODmJiYhAdHQ2lUtls2P7MM8/ghx9+EN03YMAABAUFoVu3blCr1UhKSkJERATy8vKEPm5ubjh37hzc3cUfej/55JNCoF1fpguo+yVMKpXqnMPjjz+On376SXSfZtju6OiIwMBA9O7dGw4ODrCwsEBJSQmuXbumtepdLpcjOjpa9MuXoecGAP/5z3+wbNky0X3+/v4YM2YMPDw8IJPJkJaWhgMHDiA9PV3oY21tjaioKAwYMEDn+YjuNAzbie5cTYXtRERERB1JeXk5zpw5g6tXryI/Px+1tbWwtraGp6cnBg8ejF69ehl7inpLTk7GmTNnkJubi8LCQpiYmEAul8PX1xcBAQHw9vY29hTbjEKhwIkTJ5CQkIDc3FyYmprC1dUVgYGBGDRokLGnR0RERERGwrC9A7l8+TL69+8vtPv374+LFy+K+hw+fFh0pe7EiRNx8ODBJsetrKzE6NGjERMTI9w3YMAAfPPNN41e9Zubm4tff/0Va9aswZkzZ3T2WblyJZ5++mmhHRQUhG+//RbDhw/XOYdPPvkE7777rlC6ftKkSThw4ECj827NVc4fffQRioqKMGfOHAwePLjRMLy6uhrfffcdli1bBoVCAQC466678Pfffzc5fmvmtmfPHkybNk3Yk61Pnz747rvvMGXKFK2+tbW1WLVqFV555RVUVdWV8e3bty9iY2NhZtb5VrcRGRrDdqI7F8N2IiIiIiIiIiIiMjbdCSQZRcMS5Y899phWn/Hjx4v2fTp8+HCzJbw+++wzUdA+evRoHD9+vMnyWi4uLnjuuedEq+w1ZWVlYcmSJUL77rvvxpEjR3QG7QBgaWmJt99+G99++61w38GDB5sM21tj2bJl+PjjjzF06NBGg3agbv+wl19+GRs2bBDu27t3L+Lj49tkXlVVVVi4cKEQtA8dOhQnT57UGbQDgImJCZ599ln89ttvQsAfHx8vmi8RERERERERERERERERtT+G7R2EUqkUBahSqRTz5s3T6ieRSPDoo48KbbVajfXr1zc6bkVFhWjvcrlcji1btui9ArSxvbW++uorYa9xe3t7rF+/Xq+V1s888wxGjhwptFesWKHXPNra7NmzhaoCarUaf/75Z5ucZ926dcjKygIAyGQy/PLLL5DL5c0ed88992D27NlCu6P83IiIiIiIiIiIiIiIiIjuVAzbO4h9+/YhIyNDaE+aNAndu3fX2bfhivemwvZ9+/aJ9kr/xz/+AU9Pz1bOVnzORx55BM7OznofO3fuXOH24cOHWz0XQxk4cKBw++TJk21yDs2fW1hYGPr06aP3sZo/t3Pnzon2miciIiIiIiIiIiIiIiKi9sWwvYNYu3atqD1//vxG+/bq1QujRo0S2gkJCTh27JjOvocOHRK1H3744dueY73r16+LLgwYP358i47v1auXcDs3Nxfp6emtnlNTTpw4gVdffRVTp06Fn58fHB0dYWZmBhMTE9HXli1bhGPaYk4VFRWisvyt+bmpVCqcP3/eYHMjIiIiIiIiIiIiIiIiopYxMfYECCguLsbOnTuFtpWVFWbNmtXkMY899hiioqKE9tq1azF27FitfhcvXhRuW1paIjAwsNXzPXfunKj9yCOP6Cx535j6/crr5efnG2S1fUMnTpzAM888c1uhdGFhocHnc/HiRdTU1Ajt119/HW+88cZtj5efn2+IaRERERERERERERERERHRbWDY3gFs3rwZVVVVQnvGjBmwsbFp8pi5c+fipZdegkKhAABs3boV33zzDSwtLUX9NANZV1dXyGSyVs9Xsyw9ULfKujWKi4tbdbwu27Ztw9y5c1FbW3tbx2s+H4bSGX5uREREncXb6reNPQUiIiIiIiIiIiK6w7GMfAewbt06UbupEvL1HBwccO+99wrtkpISbN++XatfaWmpcLu5AF9fRUVFBhmnXmtD54ZSUlIwf/58UdAeHByML774AseOHUNaWhpKS0tRW1sLtVotfD3++ONC/4ar7w2ho//ciIiIiIiIiIiIiIiIiEh/XNluZFevXkVkZKTovnvuuUevYxsGwmvXrsUjjzwius/Ozk64XVZWdpuzFLOyshK1//77b9x1110GGdsQPv74Y1RWVgrtL7/8EosXL272OEP9fBrT8Oe2YsUKPP300216TiIiIiIiIiIiIiIiIiJqG1zZbmQNV7UDgFKp1Our4crm/fv348aNG6L7nJychNvZ2dm3XVZdk7Ozs6jdsDy6se3atUu4PXHiRL2CdqDu59OWOvrPjYiIiIiIiIiIiIiIiIj0x7DdiFQqFX7++ec2HW/gwIHC7aqqKpw/f77V5+nXr5+offbs2VaPaSgVFRVIT08X2nfffbdexymVSkRHR7fVtAAAffv2hUQiEdod6edGRNQSarUauZdzcX3vdWNPhYiIupjs89lIOZICZY3S2FOhdlZTU4OjR48iJibG2FMhIiIiapWSkhJs27bN4NuKEhFRx8Sw3YgOHDiAtLQ0oT1lyhTRHuL6fKWkpIgC3IYr5SdOnChq//LLL62e9+DBg0Ur5nft2tUm+4ebmNza5UCp1O/DtoZvYBwcHPQ67q+//kJ5eXmbzs3Z2RmBgYFC+8CBAygpKdH7nERExlSRX4FLv17Czid34kvvL/F9/+9x+rvTxp4WERF1MWkn0rB2wlp84vgJNk/fjFPfnULB9QKtLbSoa0hISMCKFSvwwAMPwMnJCePHj8fRo0eNPS0iIiKiVikpKcGDDz4IJycnjB49Gu+88w4iIyMNUnWWiIg6Hu7ZbkQNg/HHHnusxWN4e3tjwoQJOHToEAAgPj4eJ0+eRFBQEAAgJCQErq6uyMnJAQD8+OOPeOmll+Dp6Xnb85ZKpZgzZw6+//57AHUfkKxbtw4LFy687TF1sbW1RWFhIQAIf+pzjKbk5ORmj1GpVPjwww/bfG4A8MgjjwgrNUpKSvDZZ5/hnXfeadG5iYjag7JGifSodCSEJyBhbwIyzmQAzDmIiKidKMoUuPLHFVz54woAwN7PHv5h/vAP9YffZD9YyC2MPEO6HSUlJThw4ADCw8Oxd+9eJCYmGntKRERERG1GpVIhMjISkZGRWL58Oezt7TFlyhSEhoYiLCwMPj4+xp4iEREZAFe2G0lpaSl+//13oW1tbY1Zs2bd1lgNQ3rNEN/S0lK0Z3lJSQnmzJmD0tJSvcZubAXJsmXLYGZmJrSff/55REZGtmTayM7OxsWLFxt93NfXV7h9+rR+qydtbW3h5eUltDds2IDKysomj3nrrbcQFRWl1/itmRsAPPvss3BxcRHaH3zwAbZv396icxcXF+PMmTMtOoaISB8FCQU4/f1pbH5gMz5x+gRrx6/F0fePIuM0g3YiIjKuoqQinP3hLH6d+Ss+cfoEq8euxuF3DyP9ZDpUSsNX2SLDUCqVOHXqFN577z2MGzcOjo6OmDFjBlasWMGgnYiIiO44RUVF2LZtGxYtWgRfX1/06dMHL7zwAnbv3o2ysjJjT4+IiG4Tw3Yj2bp1KyoqKoT2rFmzYG1tfVtjPfjgg7CyshLamzdvRnV1tdB++eWXMWTIEKF94sQJjBkzBocPH250zIKCAqxYsQIjRozQ+binpyc+/fRToV1RUYFJkybhww8/bLI0em1tLfbt24cnn3wSPj4+iIiIaLRvcHCwcPvUqVN45513kJmZ2Wj/ejNmzBBup6amYtasWSgoKNDqV1xcjGeeeQYffPABALTo53+7c7O2tsaPP/4olP5XKpWYNWsWXnnlFaH6gC5qtRonTpzA4sWL4e3tjQ0bNug9VyKixlQVVyF+Rzx2P7MbX/t/jW96foO/nvsLV3ZegaJUYezpERER6aRWqpF2PA2H3j6En0b9hP+6/BdbZ2/Fuf+dQ3FqsbGnd8dLS0vDTz/9hIceegguLi4ICgrCW2+9hWPHjum9BRcRERHRneDq1av49ttvcd9998HR0RETJ07ERx99hLNnz7bJtq1ERNQ2JGpufmcUDfeii4iIwJQpU257vEcffRQbN24U2r/++itmz54ttBMTEzF+/HjcuHFDdFyvXr0wbtw4uLm5QSKRIDc3F7GxsTh37hxqamogl8u19kHX9OKLL+Kbb74R3WdlZYXRo0ejX79+cHBwQFVVFQoLC3H58mXExMSIrtL74osv8NJLL+kc+9y5cxgxYoTWGwtTU1PRqvpHH30UP/zwg9BOT09Hv379RKv3bW1tMW3aNPTq1QsKhQLXrl1DeHi4sE/7hAkT4O3tjZ9//hkA4OPj02QJ+tudW73PPvsMr776qqhygJmZGUaOHInAwEA4OTmhpqYGRUVFuHLlCqKjo0Xl6hcvXowvv/yy0fkR3SlKSkogl8tRXFwMOzs7Y0+nw1MpVcg8m4nre68jMTwRaZFpUCtv/21A7/t64+E/HjbgDImI6E535ocz+POZP1s1hnNfZ/QI7QH/UH/4TvSFmbVZ8wfRbSsvL8eRI0ewd+9ehIeHIy4urlXjff3113jhhRcMNDsiIiKi9peeni6qvno7nJ2dMXXqVISFhWHq1Knw8PAw0OyIiMjQuGe7ESQmJuLYsWNC28vLC5MmTWrVmI899pgobF+3bp0obO/RowdOnTqFBx54QFT2/Nq1a7h27dptn/frr79Gv379sGTJElRVVQGoW+UeERHR5Kr1eubm5o0+NnToUPz3v//Fq6++Kgq1a2pqUFNTI7Trz1vP09MTmzdvxqxZs4THSktLsWXLFp3nCQ4Oxvbt27FkyZJm59vaudV75ZVX0LNnTyxYsEC4mEGhUODYsWOi10Zjmvq5ERFpKk4rFvZdT4xIRFWh7n+XiIiIuoq8+Dzkxefh1NenIDOTwWuMl7Dfu1ugGyRSibGn2KmpVCqcP39e2Hf92LFjUChYEYeIiIjIkPLy8rBp0yZs2rQJADBgwACEhYUhNDQU48aNg6WlpZFnSERE9Ri2G8H69etFK5rnzZsHqbR1Ff1DQkLQvXt3YeX63r17kZWVBTc3N6GPh4cHoqKi8Msvv+Czzz5DTExMo+NZWloiJCQEixYtavbcTz/9NB544AF8/vnn+Pnnn5GVldVk/969e+Ouu+7CvHnzMHLkyCb7vvzyywgJCcGaNWtw4sQJJCQkoLS0tNkPc6ZNm4YTJ07gpZdewpEjR3T28fPzw6JFi7BkyRLRanR93e7c6k2fPh3Jycn45ptv8NNPPzW5kh6oW20/depUPPzww62+OIOIui5FuQIph1PqAvbwBOTF5Rl7SkREREajVCiRfDAZyQeTsf//9sPa1Ro9ptatevcP9YeNm42xp9gpZGdnIzw8HOHh4di3bx+ys7ONPSUiIiKiO8rFixdx8eJFfPbZZ7CwsMD48eMRGhqKsLAw9O/fX9i2lIiI2h/LyN/BMjMzceLECWRnZ6OwsBDm5uZwdnZG3759MWTIkNtePR0XF4fY2Fjk5eWhqKgIFhYWsLe3R48ePdCvXz/RBQDtISEhAcePH0dWVhakUinc3d3Ru3fvRvejN5bk5GScOXMGubm5KCwshImJCeRyOXx9fREQEABvb29jT5Gow2EZeUCtUiP7fLawej31WCqUivbZD5Vl5ImIyNAMUUa+JboN6iasevce6w0TC16PDtRV6Dp+/Liwej02Nrbdzs0y8kRERNTZGaKMfEt4eHggNDQUoaGhCAkJgYuLS7udm4iIGLYTEVEndqeG7WVZZUjYl4DE8EQk7EtAeXa5UebBsJ2IiAytvcN2TSaWJvCd4IseoT3QM6wnnAOc75gVQmq1GvHx8cK+64cOHUJlZaVR5sKwnYiIiDq79g7bNUkkEgwdOlRY9R4cHHxbVV2JiEh/vGyfiIiog6utqkXq8VQk7K0rDZ8dy9KtREREhlZbWYvre67j+p7rCEc4bLvb1pWbD/NHj5AesHKyMvYUDSo/Px/79+8XAvb09HRjT4mIiIiIWkmtVuPs2bM4e/YsPvroI1hbW2PSpEnCfu+9evW6Yy4oJSJqLwzbiYiIOhi1Wo28uDxh3/XkQ8moraw19rSIiIjuKKU3ShGzJgYxa2IACeAxzEMoOe8Z7AmZqczYU2yRmpoaREVFCXuvnz59Gix0R0RERNS1lZeXY/fu3di9ezcAwNfXVyg5P2XKFNjb2xt3gkREXQDDdiIiog6gIr8CSfuTcH3vdSSGJ6IkvcTYUyIiIqJ6aiDjTAYyzmTg6AdHYWZjBr/JfkLJeQd/hw65QighIUHYd/3AgQMoLS019pSIiIiIyIiSk5OxatUqrFq1ClKpFEFBQcKq9xEjRsDEhJEREVFL8V9OIiIiI1DWKJEelV63en1vAjLOZABcXEZERNQpKMoUuPLHFVz54woAwN7PXlj17jfZDxZyC6PMq6SkBAcOHBAC9sTERKPMg4iIiIg6PpVKhcjISERGRmL58uWwt7fHlClThP3efXx8jD1FIqJOgWE7ERFROylIKBD2XU86kARFqcLYUyIiIiIDKEoqwtkfzuLsD2chkUngOcpT2O/dY7gHpDJpm5xXqVTi7Nmzwr7rkZGRUCqVbXIuIiIiIuraioqKsG3bNmzbtg0A0Lt3byF4nzhxImxsbIw8QyKijolhOxERURupLqlG0oEkYfV6YWKhsadEREREbUytVCPteBrSjqfh0NuHYOFggR4hPerC91B/yL3lrRo/LS1N2Hc9IiICBQUFBpo5EREREdEtV69exdWrV/Htt9/C1NQUY8aMEcL3wYMHQyptmwtKiYg6G4btRETUJm7cuIFTp07hxo0bqKioQPfu3dGnTx8MHz7c2FNrMyqlCplnM4V919Mi06BWsjY8ERHRnayqsAqXt17G5a2XAQDOfZ2Fvd59JvjAzNqsyePLy8tx5MgRYfV6XFxce0ybiIiIiEhQU1ODQ4cO4dChQ3j99dfh7OyMqVOnIiwsDFOnToWHh4exp0hEZDQM24mI7iBlZWU4d+4cTp06hVOnTuH06dNITk4WHvfx8RG1b8epU6fw73//GxEREVCpVFqP9+zZEy+++CKef/55SCSSVp2rIyhOKxZWridGJKKqsMrYUyIiIqIOLC8+D3nxeTj19SnIzGTwGuMl7PfuFugGNdQ4f/68sO/6sWPHoFBw6xkiIiIi6jjy8vKwadMmbNq0CQAwYMAAhIWFITQ0FOPGjYOlpaWRZ0hE1H4karWaS+6IiLq4zz//HGvWrMHly5d1BuD1Whu2f/rpp1i2bBlqa2ub7Tt16lRs2bIFDg4Ot32+kpISyOVyFBcXw87O7rbHuR1VRVX4afRPyIvLa9fzEhERUdd0HddxUHYQN5Q3jD0VIiIium2mABwBOANwuvnlePP+IgB5Db54wT51TR4eHnjiiSfw3nvvGXsqRERtjivbiYjuAEeOHMHFixfb9Bw//vgjXn31VdF9gwcPxtixY2FtbY24uDj89ddfQhC/b98+zJ49G3v27IGJSef776i2upZBOxERERlMKUoZtBMREXUKVrgVpNeH6o43/5S3cKwy3ArecyEO4lnZhjqvjIwMxMbGGnsaRETtovOlG0REZBA2NjYYOnQozp49i/Ly8laNdf78eTz33HNC29zcHKtXr8Yjjzwi6peYmIjp06cLwf/+/fvx9ttv44MPPmjV+YmIiIiIiIiIDEOCutDcqZEvQ5bHtrn55avjsRJor4Sv/6ox4ByIiIioNVhGnojoDjB37lwkJSVhxIgRGDFiBIYPH46AgABIpVL4+voiJSUFwO2Xkb/vvvuwe/duob1+/XrMnz9fZ9+8vDz0798fOTk5AAArKyskJCTAzc2txec1Zhl5tVqNvLg8XN97HYnhiUg+nIzayubL53clHsM9cPe3dxt7GkRE1IVc3HIRJ784aexptC8J4DHMA56TPeE62hUJlQk4duIYjhw5gvPnz+NO+5X9hRdewLx584w9DSIi6uKqqpTIyKjCjRt1X+nplcLtjIwq1NZ27P9/XVzM4OVlqfFlAS8vS3Tvbglzc6mxp3fHy8rKwgMPPGDsabQ7Dw8PTJgwARMmTMCUKVPg5uYGGxsbY0+LiKjNcWU7EdEdYPPmzW02dkxMjChoHz9+fKNBOwA4Ozvjo48+wpNPPgkAqKiowKeffopPP/20zebYFiQSCVz6ucClnwuClwSjtqoWqcdSkRCegIS9Ccg+n23sKbY5G3cbeAZ5GnsaRETUhWRFZxl7Cu3C1sMW/mH+8A/1R4+QHrBythIeG4ZheGjuQwCA/Px8REREIDw8HHv37sWNG12/zHyvXr0QFBRk7GkQEVEnp1arUVBQhYSEogZfxUhIKEJGRpmxp9gqubkK5OYqcO5cseh+iQTw9LRFr14ON7/shds9eshhbs44oD2kp6cbewrtwtraGpMmTUJoaCjCwsLQq1cvSCQSY0+LiKjd8X9XIiJqla1bt4ramuXkG/PII4/glVdeQVFREQDgt99+63Rhe0MmFiboEdIDPUJ6YOonU1GaWYrEiEQk7E1AQngCKnIrjD1FIiIiMhITCxP4TPCBf6g//MP84dLPRa8PIp2cnDBnzhzMmTMHarUacXFxQvB++PBhVFZWtsPsiYiIOiaVSo309NJGA/Xi4mpjTxEA4OFhAxsbUyQnl0ChULbpudRqIC2tFGlppThwIFX0mFQqgbe3ZhB/K4z385PD1FTWpnOjrmHo0KEICwtDaGgogoODYW5ubuwpEREZ3W2H7QsWLMC6desaffyLL77ASy+9dLvDkxEolUrY2dmhoqIuEHrzzTfx3nvvGXlWdKdp+Dp844038P7777d63JiYGAwZMqTRx2+3fDoBf/zxh3DbzMwM999/f7PHWFhY4N5778WGDRsAACkpKYiJicHgwYPbaprtztbdFoHzAxE4PxBqlRpZsVnCqvfUY6lQ1aiMPUUiIiJqQ64DXYXV6z7jfGBi0bpr3SUSCfr164d+/frhpZdeQlVVFY4dOyaE7+fPnzfQzImIiDqOqqpaJCUV6wzUk5KK2zy81oepqRS+vnL4+8vh728v+vLzk8PKyhQAoFSqkJZWimvXCm9+FQm3ExOLUVvbtp8TqFRqJCeXIDm5BPv2pYgek8kk8PWVi1bC13/5+NjBxISl6e9U7u7uCA0NRWhoKKZOnQoXFxdjT4mIqMPhynYSXLlyRQg4gbqr1DqLvXv34q677gIAODo6IicnBzIZr8bsSPR9jhq+DgMDA9ttjtRyRUVFuHjxotAeMmQILCws9Do2ODhYCNsB4MiRI10qbNckkUrgPsQd7kPcMXbpWCjKFUg5nCLs954Xn2fsKRIREVErWblYwX+qP3qE9oD/VH/Yeti26fksLCwQEhKCkJAQfPLJJ8jMzERERAT27t2L8PBw5Obmtun5iYiIDKWgoBKJiboD9Rs3SqHuANun29mZaQXpPXrUheteXraQyZoPo2WyulDe11eOqVN9RY/V1qqQklKiEcTfCuSTk4uhVLbtD0GpVAs/9z17kkWPmZhI0aOHHL16OaBnT3vRqnhvbzu9vnfqPMzNzTF+/Hhh9fqAAQNYGp6IqBkGC9sbhmZSKf+T7Wyio6NF7aZWAXc0mitrp02bxqC9A9L3OWr4OjRU2C6RSLTOqVKpoO4Iv7F1YnFxcaJ2S/7daHhBT8OxujIzazP0mtYLvab1AgAUpRQhcV8iEsITkBiRiKrCKiPPkIiIiJojNZXCe4y3sHrdbbAbJFLjfRDp7u6O+fPnY/78+VCpVIiNjUV4eDjCw8Nx7NgxKBQKo82NiIgoO7sccXH5Qol3za+ioo5R7t3d3VorUK/7ksPJybJNA0cTE6lwvrvu8hM9VlOjRFJSsdZq+GvXCpGaWgqVqm0/26qtVeHq1UJcvVqo9ZiZmUwI4huuivf0tIXUiO+NSH8DBgwQVq+PHz8elpaWxp4SEVGnYpCwneWXuwbNkNPBwQG+vr7Gm0wL7dq1S7itTwlran/6Pkear0MrKyv07NnTIOcPDAxEbW2t6L7mtsOg5sXHx4va3t7eeh/bsG/Dse4k9j72GPrUUAx9aihUShUyzmQIe72nR6VD3cZXsBMREZF+nPo4Cfuu+07whZmNmbGnpJNUKsWQIUMwZMgQLF26FOXl5Th8+LCw6v1Oft9FRERtT6FQIiYmB5GRGYiKykRUVAaSk0uMPS2YmEjh62unM0zv0cNeKPfe0ZiaytC7tyN693bUeqy6uhaJicW4fr1Ia1V8WlrbVwVQKJSIjy9AfHyB1mMWFibw97+1Ir7+592zpz28vFia3picnJwwdepUhIWFYerUqejevbuxp0RE1KmxjDwJNEPOzlTKOTo6GmlpaQDq9osOCwsz8oyooZY8R5qvw4EDB7JKRgd348YNUdvT01PvY93c3CCTyaBU1u2vlp6ebtC5dVZSmRSeQZ7wDPLEhLcmoKq4CkkHkoT93ouSiow9RSIiojuGhb0FeoT0qCsNH+oPex97Y0/ptlhbW2PatGmYNm0aACA1NVVY9R4REYHCQu2VakRERPpKTy+9GaxnIDIyE+fOZaO62jh7qdvamolKvGsG6l0x4DU3N0FAgBMCApy0HqusrEFiYrHOPeJv3Chr87lVVdXi0qV8XLqUr/WYiYkUfn639rnv2dNBCOL9/OSwsGBsYUgmJiYYM2YMQkNDERYWhiFDhvAzVyIiA+L/WiSIiYkRbnemEvKaK6YnTJgAOzs7I86GdGnJc6T5OuR+7R1faWmpqG1rq//epFKpFNbW1igpqbu6vays7X/R64ws5BYImBGAgBkBUKvVKEwoFIL3pANJUJSxJCwREZGhSGQSeAZ5CqXhPYZ7QNrFPpQH6ioMPfXUU3jqqaegVCpx5swZhIeHY+/evYiKihIuhiQiImqoqqoWZ89m3wzW61aut0dwq8nNzVoI0BuuUnd2btty752JpaUp+vd3Rv/+zlqPlZcrkJBQrLU//LVrhcjKKm/zudXWqoTzNiSRAN272wqr4Rv+aWdn3ubz6wp69eol7Ls+ceLEFn1mR0RELcOwnQAAKSkpKCi4Ve6nM4XtmnuBs4R8x6Tvc9TwdTho0KA2nRe1Xnm5+BcwCwuLFh1vYWHRorC9uroa1dW39nKrP/ZOIZFI4NjTEY49HTHi2RFQ1iiRHpleF76HJyDjTAbAivNEREQtYu9rL4TrfpP9YGHfsvcznZ1MJkNQUBCCgoLw73//G8XFxThw4IAQviclJRl7ikREZCRqtRrJycWIisoUgvWYmBzU1Kja9LwmJlL4+NjpDNR79JDD2rpjbuPSmVhbm2HQIBcMGuSi9VhpqQIJCQ3L0te1c3Iq2nxuanVdtYT09FIcOpSm9bizs6VWWfr6P11crO7Yiy3kcjmmTJki7L3u5+dn7CkREd0xGLYTAHHpbqDzhO03btzAuXPnhPZ9991nxNmQLi15jhq+DrmyveOrrKwUtc3NW3Z1sWb/hmPp8tFHH+Gdd95p0Tm6MpmpDD7jfeAz3geT35+MirwKJO5PFPZ7L71R2vwgREREdxgzGzP4TvIVAnbHno537IeyusjlcsyYMQMzZswAAFy/fl0I3g8cOMBqREREXVh5uQJnzmSL9lrPzm6bcNXa2lRU4l0zUPf27nrl3jsTW1szDB7sisGDXbUeKy6uxvXr4pL09WF8fn7zn+sYQl5eJfLyKhEVlan1mI2NaYMA3kF4jXl62kIm6zqvK6lUipEjRwqr10eOHAkTE8Y9RETG0KH/9T1//jwuXryI9PR0mJiYoF+/fpg8eTLMzBq/erG6uhrHjx/HpUuXUFJSAnt7ewwfPhwjR4402AcoHWVeFRUVOHnyJK5cuYLCwkKYmZnBzc0NY8aMga+vb4vG0gw5LS0t0bdv39uaU3vbtWsX1Oq6ZZyDBg2Cj49Pi45PTExEVFQUMjIyoFKp4Ofnh8mTJ8PJSXufI7o9LXmONF+HEolEa2V7UlISoqKikJaWBpVKBQ8PD4wbN45XahpRw5XsCkXLSpprrlLXZ1X8smXL8PLLLwvtkpISeHl5teicXZmVsxUGzBmAAXMGQK1WIy8uD9f3XkdieCKSDyejtrLW2FMkIiJqfxLAY5gHeoT2QM+wnvAc5QmZmczYs+o0evbsiZ49e+LZZ59FTU0NoqKisHfvXoSHh+PMmTPCe30iIupc1Go1rl8vEu21fuFCLpRKw/67LpVKMGCAM0aNckdQkDv69nWEv789XF3v3BXInZlcbo5hw9wwbJib1mOFhVU6V8MnJBShoKCqXeZXVlaD2NhcxMbmaj1mZiaDn59c56p4Pz85zDrB+0Nvb2+EhYUhLCwMkydPhoODg7GnREREMHLYvnjxYnz99dcAAFdXV2RnZ0OtVuOnn37Cl19+iUuXLmkd061bN3z33XeYNWuW6P6CggJ8+OGH+PHHH3WWFQ4MDMTGjRvRv3//TjuvetHR0fj444+xc+dOVFXpfqMyatQofPTRR5g4caLeY9YbOHAgZLKO/+YCuP0S8r/++is+/PBDxMbGaj1mZmaGp59+Gv/9739hZmaG7777Ds8//7zweH5+PhwdHVs38TtIS54jzdehr6+vsLf7rl278P777+PUqVNax0gkEsycORMrV67kRRJGYGNjI2rrszpdk+a/YQ3H0sXc3LzFq+fvVBKJBC79XODSzwXBS4JRW1WL1GOpQviefT7b2FMkIiJqM7YetvAP9Yd/mD/8pvjB2sXa2FPqEkxNTTFu3DiMGzcO77//PvLy8rB//34hfL9x44axp0hERI0oKanGqVNZor3W2yIAdXa2RHCwB0aNcseoUR4YMcINtrYs+34ncHCwwMiR7hg50l3rscLCKiQkFOH69SIkJBSJbmdktE/VHIVCiStXCnDlSoHWY1KpBF5etjqDeH9/e9jYGOc1bGVlhUmTJgmr13v37s2LVIiIOiCjhu1nz54Vbg8bNgyZmZl45JFHcOjQoUaPyc7OxuzZs7FlyxbMnj0bABAREYF58+YhJyen0eNiY2Mxfvx4nDt3rtnVzx11XgqFAq+99hq++eYbqFRN740UFRWFyZMn48MPP8T//d//NdkXEIecnaWEfHl5OQ4cOCC09SkhX1hYiNmzZ2P//v2N9lEoFPj6669x/vx57N69WxTI+/j4MGhvgZY+R5qvw8DAQOTn52PRokXYtm1bo8eo1Wps27YN169fx4kTJ2BlZdX6iZPeGgbkLSkrqlKpUFFxqxydPmE73T4TCxP0COmBHiE9gP8CpZmlSNyXKOz3XpHb9vuuERERtRUTCxP4TPARAnaXfi78ILIdODs7Y86cOZgzZw7UajXi4uKE4P3w4cMtvhCTiIgMQ6VSIz4+X7TX+qVLeTB0MRKZTILBg12FYD042AM9esj5fzBpcXCwwPDhbhg+XHtFfEVFDRITi5CQUIzr1wtFQXxKSonBqy3oolKpkZJSgpSUEuzfn6r1eLduVloBfF2JejmcnCwN+pofOnQoQkNDERYWhuDgYC46ISLqBIwWtqtUKsTExAhtHx8fTJkyBXFxcZBIJBg5ciSCgoJgbW2NK1euYPfu3UJ5YrVajRdeeAHTp0/Hjh078Mgjj0CpVMLCwgKTJ09G//79oVQqER0djYMHDwrnKCgowBtvvIENGzZ0unmVlZXhnnvuwZEjR0T3Dxs2DIMHD4aHhweKiopw9OhRYf5qtRrLli2Di4sLnnzyyUbHzsvLQ3p6utDuLGF7eHi4UILa3d0dI0aMaLJ/Xl4exo0bh/j4eOE+mUyGcePGITAwEHZ2dkhOTsaff/6JgoICHDp0CMuXL8f58+eF/sOGDWubb6aLaslz1PB16O3tjalTpwoBfEBAAMaNGwdXV1fk5OQI2zLUi42NxXfffYdXX321jb4b0qV79+6idlpamt7HZmVlobb2VllzT09Pg82LmmfrbovAxwIR+Fgg1Co1smKz6oL3vQlIPZYKVU3TF3UREREZm+tAV2Hfde+x3jC1NDX2lO5oEokE/fr1Q79+/bBkyRJUVVXh2LFjwn7vmr9XERGRYRUUVOLkycyb+6xn4uTJTBQXVzd/YAu5uVkjONgDwcF14fqwYd1gZcX/f6l1rKxMMWCACwYMcNF6rKZGiZSUElEAX/9nYmIxqqraZ7u87OwKZGdX4MSJDK3H5HJzYV/4+j3i7exqAMgBlABo+mIBd3d3hIaGIjQ0FCEhIXB1dW2T74GIiNqO0cL2K1euoLy8XGivXr0aCoUCo0ePxvfff4/AwEBR/8uXL2PChAnIy8sDULeS/D//+Q/ef/99KJVKPP744/j444/RrVs30XE7duzAzJkzhX3ktm/fjpqaGpia6n4j2BHnVVNTg/vvv18UtI8ePRrffvutzmB827ZteOyxx4QVoy+99BKmT58OZ2dnnd+z5mpioPOE7Zrlye+9994mryBUKpV4+OGHRUH7gw8+iE8//VSrokBJSQmeeOIJbNu2DV999RWkUqnw2NChQw34HXR9LXmOGr4OV61ahaqqKgwcOBDffPMNJkyYIHpcpVLhq6++Eu3fvXXrVobt7axv376idmqq9tW/jWnYt+FY1H4kUgnch7jDfYg7xi4dC0W5AimHU4SS83nxecaeIhEREaxcrOA/1R89QnvAf6o/bD1sjT0laoKFhQVCQkIQEhKCTz75BJmZmYiIiBBWvufmau+lSkREzVMqVbh4Me9msF6317qustitZWoqxdCh3TRKwrvD29uOq9apXZmaytCzpwN69nRAWJj4MZVKjYyMMp3l6a9fL0RJiaJd5lhcXI1z57Jx7lzD7freBFADIBdAzs2vbJiaFmH8+L64++66gH3AgAH8e0VE1MkZLWzXLNUO1JXunjt3LtavX68zcO7Xrx/ef/99PP3008J9b7/9NgDg888/x5IlS3Se54EHHsD8+fOxfv16AEBFRQWSkpLQu3fvTjOvpUuXilbCL1y4ECtXrmz0goFZs2YhJycHzz77LIC6VfE//PAD3nzzTZ39NUNOmUyGgQMH6uzXkahUKvz5559Cu7ny5G+99RYiIiKE9tKlS/Gf//xHZ187Ozts3LgRly5dEoXzAFe2t0RLn6OGYXtVVRXuvfde/Prrr7C0tNTqL5VKsWTJEuzatUv4+9Hw+aK2FxAQIGo3fB6bcu7cuSbHIuMxszZDr2m90GtaLwBAUUqRUHI+MSIRVYWG39ePiIioIampFN5jvIXV626D3SCR8oPIzsrd3R3z58/H/PnzoVKpEBsbi/DwcISHh+PYsWNCxTgiIhLLza3QCNYzcPp0FsrKagx+Hi8vWyFYDw72wODBrrCwMOoOpERNkkol8PS0haenLSZM8BI9plarkZ9fKSpNrxnKZ2e311Z6pgA8bn7VqakBDh6UICVFjsOHryMgoAABAU43vxxhZ8ey8UREnU2HCduDgoIaDbTrjRkzRuu+F154odFAu97o0aOFUBsASktLO828zp07h6+//lpoT5o0CatWrYKJSdNP3ZNPPonXX38dRUVFAIC9e/fqFbb37dtXZ7DZ0URFRQkrIaysrBASEtJo38TERHzyySdCe8qUKfjwww+bHN/c3BwvvPACnnvuOdH9XNmuv5Y8R4B2SDt06FBs3boVFhYWTR43evRoIWyvqmIA2N4cHBzQv39/oaR/dHQ0qqqqmn3eAODEiROi9vjx49tkjtR69j72GPrUUAx9aihUShUyzmQgYW8CVEqWmiciIsOycbPByBdGwj/MH74TfGFmY2bsKVEbkEqlGDJkCIYMGYKlS5eivLwchw8fxt69e+Hu7m7s6RERGU1NjRLnz+cK+6xHRWUiIaHI4OexsDDB8OHdhGA9KMgd3buzYgx1HRKJBM7OVnB2tkJQkPZ7i9JSxc194rXL06emlkDdxtvEq1Tqmyvwi7BrV4LoMQ8PGwQEOArhe30Q362bFVfAExF1UEYL2zVXNEokEqxatarJQBsA7O3tRW0PDw989NFHzZ7Lzs5O1La2tu4081q2bBmUSiWAusBy48aNzQbtAGBmZoYRI0Zg3759AICrV6822lfze+6MJeRDQkKavEDgnXfeEfaFNjc3x9q1a0Wl4RvT8CIKT09P7pnTAi15jgDtv3s//fSTXoGt5goYR0fH25gptdb9998vhO0KhQI7d+7EnDlzmjymqqpKVPnA29u70/z7c6eTyqTwDPKEZ5CnsadCRERdUN8H+qLvA9xa5k5jbW2NadOmYdq0acaeChFRu8rMLBMF62fOZKGy0vB7UPfoIceoUbf2Wg8MdIGpqczg5yHqLGxtzRAY6IrAQO3Pequra5GcXKKzPH1SUjEUCmWbzi0jowwZGWXYv1+8/aK9vXmDAL7uTx8fO8hkzX/WTUREbccoYbtarRatYg0LC8OgQYOaPS49PV3UfvbZZ5sMzuulpKQItyUSCby9vTvFvJKSkoSwHACef/75Fl3l7+bmJtwuLCzU2aesrAzXr18X2p1l5bZmkNtUefLk5GRs3LhRaC9cuBCenvoFRA2D9Zb8bE6dOoUdO3bg0KFDyMzMRHZ2NqRSKVxdXTF8+HDcc889ePDBB3W+Tt577z289dZbWvfLZDLY2dnB0dERAwcOxOjRozFv3jx4eHho9a23efNmPPzwwwCAiIgITJkypdG+//rXv/DZZ58BqNtffdeuXY32jYuLQ//+/aFWq/H888/jm2++0eqj73MEaL8Ow8LCMHjw4CaP0ZxLPT8/P72OIcOaPXu26AKj77//vtmw/ZdffhEqbwDAgw8+2FbTIyIiIiIiIjK66upaREfniErCp6Y2Xn3zdllbm2LkSDeMGnVrr3VX1+Y/pySiOubmJujTxxF9+mgv6lEqVUhPL0VCQvHNAL5QFMq3xRYP9YqKqhEZWfdvhyYLCxP06eOAgAAn9O3rKITwvXs7wNycW0EQEbUHo/xre/XqVVHJ9JkzZ+p1XGxsrKit73EXL14Ubvv6+sLKyqpTzGvDhg1Qa9SseeKJJ/Qat179am4AsLXVXQoqNjYWKtWtMsCdYWXp9evXhYBVIpE0GeT+8ssvQmUAoK68v74aliTXZ7/2y5cv47nnnsOhQ4d0Pp6UlISkpCRs3boVS5Yswbvvvovnn39e1KfhPtb1lEolCgsLUVhYiISEBOzYsQPLli3Dc889hw8//FBncC+Xy4XbJSUljc67rKwM//vf//TqCwBff/011Go1pFIpFi9erPV4S54jQPt1OHv27Cb7a9L8e8Q9v41jyJAhmDZtGv766y8AwJEjR/Dzzz9j/vz5Ovvn5eVh2bJlQtvS0hL/+te/2mWuRERERERERG1NrVYjLa0UUVF1q9YjIzNw7lxOm6yI7dPHUQjVg4M90L+/M0xMuMqVqC3IZFL4+Mjh4yPH5MnihXNqtRq5uRWisvRXrhQgLq4AV64UoLq6bVbEV1XVIjY2F7GxuaL7pVIJevSQa5Wj577wRESGZ5SwvWGYOHHiRL2O09xP3dXVVe9gTd8y6R1tXpqBbZ8+fdCnTx+9xq2nuZrd2dlZZ5+G+2Tru5rYmDRXTI8cORLdunVrtO/ff/8t3Pbx8UG/fv30Pk/DagDNrWzftGkTHn/8cdTU1EAqlWLmzJl4+OGHMWTIENjb26OgoACXLl3Ctm3b8Msvv6CwsBBr167VCtvrn5NRo0aJKhvU1taipKQE169fx6FDh7B69WrcuHEDX3/9NU6dOoWIiAitwF1zi4OmAvTVq1ejuLhYaGvebqiwsBDr168HULcCvmfPnlp9WvIcAdqvw0mTJjXZv15BQQGSk5OFdme4WKSr+uCDDxAeHi5c5POPf/wDJiYmQmWFeklJSbj//vuRk5Mj3Ld48WLuzUlERERERESdVmVlDc6ezRaVhM/IKDP4eezszBAUVBeqjxrljpEj3eHk1PS2fUTUPiQSCVxdreHqao3Ro7uLHlMqVUhOLkFcXP7NrwLhz+Li6jaZD/eFJyJqP0YJ2zXDablcrjOsa+644cOH63VMRUUFrly5IrSbWp3ckealVCoRFRUltEeMGKHXuJo0y9T36tVLZx/NkNPPz09r//mOSLO8eVMrplUqFc6cOSO09b14op5meXKg6dfO+vXrsXDhQqhUKvj5+WHLli1az5mDgwP8/f1x//3344033sDChQsxcOBAUZ+CggLheRsyZAhsbGxEj9vb28Pb2xuTJ0/Ga6+9hkcffRQ7d+5EVFQUXn75ZaxcuVLUX3Nle2MBukqlwtdffw2gbl/69PT0JsP2H3/8ERUVFQCAJUuW6Oyj73NUT/N16OjoqHc5+IYXyOhTfeBOlZKSAn9/f52PaVZ/SElJgYmJ7v8a9u/fjwkTJuh8bPDgwfjmm2/wzDPPAACqq6vxyCOP4JNPPsG4ceNgZWWFuLg4/PXXX6KqGxMnTsS77757u98WERERERERUbtSq9VISirWCNYzEBOTi9paVfMHt4BEAvTr53QzWK8L1wMCnCCVMggj6mxkMin8/e3h72+Pe++99fmcWq1GdnaFzhC+LS7YqdfSfeF9feX8t4eIqAlGD9uHDh2q19VSCoVCVC5a31AtJiZGVJ66qdXJHWleubm5QqAJNB6WN6a4uBjx8fFCe/To0Tr7aYacnWFVcGFhIY4dOya077///kb7pqWliUrBe3l5tehcmhc7uLm5NbryNjo6Gk899RRUKhV8fX1x7NixJvdRB4DevXvj4MGDuHr1qtZY9ZqrMmBjY4PNmzejT58+SE1NxU8//YQ333xT9H3qs7J9165dSEhIgFQqxdKlS/HCCy80GrYrlUp89913wvx0XcDQkueonub33VwFAU2af2elUmmnqMxgLGq1WhSqN6WxfprbWujy9NNPo7i4GG+++aYQqMfExCAmJkZn/8mTJ2Pr1q0wNTXVa15ERERERERE7a2sTIHTp7OEcvBRURnIza00+HkcHS1uloP3QHCwB0aMcINczlLPRF2ZRCKBm5s13NysMWmSuCx9cXE14uMLtIL4xMRiqFRNf0Z3u/TZF14ziO/Vi/vCExEBRgjb1Wq1KFjTN5y+ePEiFApFi49ruPK1sSCvo80rN1e8x0pLV5zv2bNHFIzpWo1aU1ODS5cuCW19wvbU1FRERERg//79uHTpErKzs5Gfnw8rKysEBARg5syZeOGFF2BhYdHsWJcvX8bq1atx6NAhJCcno7S0FG5ubvD398c999yDhx56SCsg11wV6+vrq7UyXFNBQYGo7eLi0uyc6tXW1mLbtm1Cu7HXTXV1NebNm4eamhqYmJjg999/bzZor2dmZoYBAwaI7tN8XegTHFtYWOCf//wn3nzzTSiVSvz1119YtGiR8Lg+YfsXX3wBoC4Ur/8+G+v7+++/IzW17orHxla1t+Q5ArRfhy0J2zV/Xn369IGVlZXex1LbWLp0KSZOnIg333wTBw4cEF1UVK9Hjx548cUX8eKLL7I0FREREREREXUYarUaV68WIioqA5GRdavWL1zIM3iwJZVKMGiQi7DP+qhR7ujVy4G/IxORQC43R1CQO4KCxAvAqqpqce1aodZKeGPsCy+TSdCjh73WSvi+fbkvPBHdWdo9bE9ISBCtmtU3WNM3NG/quO7du8PV1bVTzKthQKUrsGrKqlWrhNs9evTQubL90qVLogsF9AnbJ0yYINoju15xcTGioqIQFRWFjRs34vDhw6IS5prKy8vxwgsvYO3atVorZVNTU5GamoqDBw+isrISb775puhxzb3A9SlPrknze23O77//jszMTKHd2EUUa9euFcrNP//8862uDlD/upDJZM2G1PVGjRol3L5w4YLoMSsrK5iamqKmpkbnavWYmBgcPnwYAPDyyy/Dzs4OQN3K5vLycq094L/66isAdSv9586dq3M+LX2OGr4OW1IKvmE1Cmqcr69vsyvTDSUoKAj79u3DjRs3cPLkSdy4cQOVlZXw8PBA7969MXLkyHaZBxEREREREVFTiourcepUpmiv9cLCquYPbCFXVytRsD58uBtsbMwMfh4i6vosLEwwcKALBg4ULywzxr7wSqUa164V4tq1Qvzxh3hf+O7dbYQAfv78fhgxQnfVWCKirqDdw3bNcAzQP1jTPM7FxUXvkuCaoba+JeQ7wrwcHR1Fbc3gtznh4eE4cOCA0F6wYIHOK2M1V/IDzYft+fn5cHNzw8KFCzFy5Eh4eHjA3d0dNTU1SE5Oxs8//4wffvgBsbGx+Pjjj/Hhhx9qjVFeXo7Jkyfj1KlTkEgkeOihh/D4448jMDAQlpaWSElJQXh4OFasWKG153lNTQ327NkjtJsrT+7g4CBqX7lypcn+mnN89dVXRffpeo5UKhU+/fRTAIC5uTmWLl2q1/hNqX9OevfuDUtLS72O0Vyxn5eXp/W4XC5HXl6eztXq9avaR4wYgXHjxiEtLU14rLi4WBS2nz17FsePHwcAPPfcczAz0/6lsKXPEaD9OtQ3NC8uLkZiYmKLj6P20717d8ycOdPY0yAiIiIiIiKCSqXG5ct5QqgeGZmBuLh8GPq6dBMTKYYMcdUoCe8OX185V60TUZtqal/4rKxyjfA9/2Z5+rbdF/7GjTLcuFGGiIgUjB7dnWE7EXVp7R62a4bMdnZ2eu9Frhlq6xuEV1dX4/Lly3od19Hm5enpCUdHR6EUuuYe2E0pLCzEM888I7S7deuGxYsX6+yrGXK6uro2W/7cyckJkZGROh/z8PDA6NGjUVFRgfXr1+Ovv/7SCtvVajVmz56NU6dOwdTUFFu3bsX06dNFfRwdHTFkyBAsXrxYaxXu4cOHhcDYzs5OZ2l8Td7e3rC1tUVpaSkAYPfu3VAoFDpD4noqlQpPPPGEUCq9nq7n6NSpU7h+/ToAYNq0aXBzc2tyPs0pKyvDtWvXAOhXQr5edfWtKxN1BfSNhe1ZWVnYvHkzgFsl4etXtgN1Ybbma+LLL78EUFe6/umnn9Y5l5Y+R4D4dSiXy+Hv799E71vOnTsneo20ZEU8EREREREREXVt+fmVOHkyUygJf+pUJkpK9K96qC8PDxsEB9/aa33oUFdYWpoa/DxERLdDIpHA3d0G7u42mDzZOPvCBwQ4Nt+JiKgTM+rK9iFDhuh1VWdtba2oPLa+odqFCxdQU1MjtPVd2d4R5iWRSDB16lRs2bIFAHDixAmcPn1aa7W3ptLSUtx9992i1b6ffvqpKEDVpBlytrb8eb36wLnhfukAsG7dOvz9998AgBUrVmgF7Zp07fmuWZ78rrvugqlp07+4SKVSTJw4Ebt27QJQFy4vW7YMn332mc7+ZWVlePrpp/Hrr7+K7m+sYkF4eLhwu6nvRV+xsbHCdgEtCds1f9bdunXTerx+3/aGZeS///57KBQKeHl5Yfbs2QAAW1tbSCQSqNVqUf/MzEzh5zJ//nw4OzvrnEtLnyNA+3Wo75Xemn9nJRKJwV7DRERERERERNS51NaqcPFi3s1y8HUl4a9eLTT4eczMZBg2rJuoJLyXl+7P3YiIOrr22BdeIgF693ZoviMRUSdm1JXt+obTly5dQlXVrf2S9D2uJfupd8R5LV68WAjbAWD27Nn4448/MGjQIFE/tVqN8PBwPP3006L91J977jk8+uijOsdWq9WIjY0V2voGlRcuXMDPP/+MEydO4OrVqyguLta5F3rDMLa2thavv/46AGDcuHF48skn9TqfpvrQHNCvPDlQt4+65nGff/45UlJS8PLLL2PIkCGQSqVIS0vDH3/8gW+++QbJyckwNTWFra2tEGI39vxoPo/Dhw9v8ffT1HgtCY7rV9cD0LkqvD5s11zZXlVVhR9++AEA8OKLL8LEpO6fAqlUCmtra5SVlYnC9hUrVgjP80svvdToXFr6HDV8Hd7ufu3+/v6NXlRCRERERERERF1Ldnb5zXLwdcH66dNZKC+vaf7AFvLxsRNC9VGjPDB4sAvMzdv941QionZlyH3h/fzkrPZBRF1eu747TEpKQmHhratKb2df9JYcpxleurq6onv37p1qXsHBwfjnP/+JVatWAQBSUlIwZMgQTJo0CUOGDIG5uTkyMjJw6NAhJCUliY596qmn8PXXXzc6h+vXrwvl1YHmw93Kyko8++yzWLdunVZ5d1169+4tah8+fFjYd/5f//pXs8c3dP78eeFCAhMTE0ybNk2v40JDQ/HII4/gl19+Ee7btm0btm3bprO/paUlfvvtN9HFAI1VE8jKyhJu+/j46DWfpmi+Llqysr1+H3Wg7vttSC6XAxCH7Rs3bkRubi5sbW3xj3/8Q9Tfzs4OZWVlQv/q6mohmA8LC0O/fv10zuN2nqOGr8OW7Luu+fPifu1EREREREREXZNCoURsbI6wz3pUVCaSkoqbP7CFLC1NMGKEmxCsjxrlDnd3G4Ofh4ios7qdfeEDApyMOGMiovbRrmG7IcJpZ2dneHt7N9Fb93H6lpDvSPMCgK+//hp5eXn4/fffAdTtKb5//37s379fZ38rKyt89NFHePHFF5scV7N0N9B02K5SqXDvvffiwIEDAOpWLD/44IMYNGgQ3N3dYWlpCYlEgszMTCFkbzhefcl1c3NzTJ06tcm56aK5YnrMmDFwcNC/9Mzq1atRU1ODrVu3Ntlv8uTJ+Oabb2BpaSkK0idOnKizv2Z4bW1trfd8GlP/nHh4eMDFxaWZ3nUqKiqwZ88eAEC/fv10hv66ysjX77/+xBNPCGF8PTs7O2RkZAj9f/nlF+Tm5gK4tbe7LrfzHDV8Her7d6+kpETY3x5g2E5ERERERETUVdy4USoK1s+ezUZVVa3Bz9Ozp70QqgcHe2DgQGeYmsoMfh4ioq6uqX3ha2paVnaeiKgzMlrYbmNjo7X6WZ/j9A3VGu6nrm/Y3pHmBdSF07/99htWrVqFDz74AGlpaTr72dvbY968eXjttdf0Cv01Q05bW1v07Nmz0b7r168XgvYNGzZg3rx5OvudPn1auN0wNE1NTQUAdO/eHZaWls3OryHNvcD1LSFfz9zcHL/++iv+/vtvrF69GidOnEBeXh5sbW3h6emJ8ePH46GHHsLYsWMB1O1lXs/MzAyjR4/WOW59iA0ApaWlrSpjXl1djcuXLwNo2ar21atXC1UZFi9erLNPfZhev4I8IiICFy9ehEwm03lMff/6sP2rr74CUBfmh4WFNTqX23mONF+HLfm7Fx0dLaqwwLCdiIiIiIiIqPOpqqpFdHSOsNd6ZGQm0tNLmz+whWxsTDFypLsQrAcFucPFxcrg5yEiIjFexEREd4J2Dds/+ugjfPTRRy0+LjIyssXHmJiYoLKyUq++HXVe9SQSCRYtWoR//vOfOHfuHKKjo5GXlwcAcHFxQf/+/TF8+HBh3219tOR7rt83fsSIEY0G7QCwdu1a4XbD8DM/Px8AYGra8v1ZsrKyREF+S8P2enfffTfuvvvuZvutXLlSuD19+vRGLw4ICAgQXgPR0dGYMGHCbc0LAC5evIiamrq9xfQN25OTk/Hmm28CADw9PbFgwQKd/eovClAqlSgvL8cXX3wBAHjggQfg5+en1b/+ooHi4mIcOnRI2FO9qb3ab/c5ut2/exMmTNBrOwMiIiIiIiIi6hjUajVSUkqEvdYjIzMQHZ2DmhqVwc/Vt6+jaK/1/v2dIJNJDX4eIiIiIqJ2DdupdSQSCYYNG6Z3qW1Dqd8P3tPTs9E+f/31F/bt2wcA8PPzg6Ojo+jx+tXSiYmJqKmpaVHovmvXLiFY7du3b5Or8Fvr559/xvnz54X2okWLGu17zz33YPXq1QCA//3vf3qH7TU1NSgtLRX9jFq6X3tmZiYeeOABFBcXQyqVYs2aNTAzM9PZV3MF/qlTp/D3338DAF555RWd/TXD9vpV7c7Ozpg/f36j82nP54iIiIiIiIiIOr7SUgVOn85CVFQGTp7MRFRUJnJyKgx+Hrnc/GaoXhesBwW5w8HBwuDnISIiIiLSxSBhe0pKitaq6s8//7zZfcOpc6jfj/zcuXNQKBRaoe6ZM2dEK96HDx+uNcaoUaPw22+/oaamBp9//jmWLl3a6PkqKipgZXWrlFdrSsi3RExMDF544QWhPXbsWEyePLnR/tOnT0dAQADi4uKwYcMGhISE4PHHH2/yHPv27cPLL7+Mbdu23XbY/tdff+GZZ54RSvN/9NFHCAkJabS/5p7s7733HtRqNUaNGoXg4GCd/evD9piYGBw7dgwA8PTTT8PCovFfVNvrOWqN2NhYrQtVVCrDXz1PREREREREdKdRKlWIi8tHVFSmEKxfupQHQxelk0iAAQOcRXut9+njCKlUYtgTERERERHpyWAr25VKpajNEKvrCA0Nxblz55CSkoI5c+bgrbfego+PD9LS0rBp0yZ89dVX8PPzQ1FREQDt/doBYMGCBXj//fdRVFSEN998E4WFhZg7dy58fHygUChw/fp1HDx4EJs2bcKqVaswZswY4dhx48YJYz788MN6zzsvLw+XL1/G+PHjm+27fft2PPXUU8I+5SYmJlixYgUkksZ/WZPJZNi8eTPGjBmDsrIyLFiwALt27cKCBQsQGBgIW1tbFBUVITk5GYcPH8bWrVtx6dIlyOVy9OrVSzRW/d7lVlZWcHNzQ1lZGYC6v1cFBQXIyMjAkSNHsH37dqFcu7m5OVauXNlswK+5sv3gwYMAgJdffrnR/vVh+5EjRwDU7Vv/3HPPNXmO232O2pNardb6d4qIiIiIiIiIWi47uxwnT94K1k+fzkJpqcLg53FyshRC9VGj3DFihBvs7MwNfh4iIiIiotvFMvLUrNdeew3btm3DtWvXsGPHDuzYsUP0eFhYGF555RWEhoYC0B22Ozk5Ydu2bZg5cyaKi4vx8ccf4+OPP9bqJ5VKtVZ2v/baa7c178jISNx///0YN24cHn74YYwaNQr+/v6wtbVFVVUV0tLScPz4caxZswZHjx4VjpNIJFi9ejUGDBjQ7DkGDRqE48ePY86cOYiPj8e2bduwbdu2RvtLJBLMnDlTFOIrlUqhdH1FRYUQdjfGxMQEc+fOxeuvv46AgIBm56i5sh0AfH19MXPmzEb7Nzz/3Llz4ebm1uQ5bvc5IiIiIiIiIqKOrbq6FjExuYiKyhBWriclFRv8PDKZBIMGuQjBenCwB/z97ZtcCEFEREREZGy3HbavXbsWa9euNeBUqKNycHBAZGQk3nrrLezYsQO5ublwcXHBiBEjsGDBAjzwwAPC3uUAMHToUJ3jTJ48GZcuXcK3336LvXv3IiEhAZWVlXB0dIS7uzvGjx+P+++/Xyhb31r1q8WPHj0qCtObYmFhgW+//bbJ/ckbGjRoEC5evIjff/8d27dvx6lTp5CdnY3KykrY2NjAy8sL/fr1w/jx43HPPffA19dXdHx8fDwqKyu1xpVIJLCzs4ODgwOcnJwwdOhQjB49GlOnTkX37t31np/mynYAePHFFyGTyRrt3zCcX7Jkid7n6sgGDx4s7CtPRERERERERNrUajWSk4sRFZUpBOvR0TlQKAxfKa5bNytRsD5sWDdYW5s1fyARERERUQciUTN9oi7qkUcewaZNm/TuHxoais8//xz9+/dvw1kRkSGVlJRALpejuLi42aoQREREREREJFZSUo3Tp7M09lrPQG6u9oKA1jIzk2HoUFeMGuWBoCB3BAW5wddXzlXrRERERNTpMWynLqu2thbh4eGIiIjA2bNnkZSUhPz8fFRVVcHGxgZOTk7o06cPxowZgxkzZjBkJ+qEGLYTERERERHpR6lU4fLlfFGwfvlyPtrik8EePeRCsD5qlDsCA11gbs7dLImIiIio62HYTkREnRbDdiIiIiIiIt2ys8tvhup1wfrp01koK6sx+Hlsbc1urlavC9aDgtzh4mJl8PMQEREREXVEvKSUiIiIiIiIiIioE6uqqkV0dI6wYv3kyUwkJ5cY/DxSqQQDBjgLwfqoUe7o29cJUinLwRMRGZpSoUTc73G4sPECynPLYe1ijYHzBiJgZgBkZjJjT4+IiG5i2E5ERERERERERNRJqNVqJCUVCyvWT57MRHR0DmpqVAY/V7duVhg1ykMI1ocPd4ONjZnBz0NERGLFacXYeNdG5F7OFd1/dfdVuPRzwbw98yD3khtpdkREpIll5ImIqNNiGXkiIiIiIurqiourcfp0lhCsR0VlIi+v0uDnMTeXYejQbkIp+FGj3OHtbQeJhKvWiYjak1KhxMohK7WCdk0u/VywKHoRV7gTEXUAXNlORERERERERETUASiVKly6lC8K1uPi8tEWS2X8/e1FwXpgoCvMGNoQERld3O9xTQbtAJB7ORdx2+MwYM6AdpoVERE1hmE7ERERERERERGREWRllYuC9dOns1BeXmPw89jZmSEo6FawPnKkO1xcrAx+HiIiar0LGy/o3Y9hOxGR8TFsJyIiIiIiIiIiagdJSUWIiEjFgQOpiIzMQEpKicHPIZVKMHCgsxCsBwW5o29fJ0ilLAdPRNQZlOeW69cvR79+RETUthi2ExERERERERERtYG8vAocPJiGiIgURESkIDGx2ODncHOzxqhRt4L14cPdYGNjZvDzEBFR+7B2sdavn6t+/YiIqG0xbCciIiIiIiIiIjKAiooaHDt2A/v3pyAiIhXR0dkG3W/d3FyGYcO6aey17gEvL1tIJFy1TkTUVQycNxBXd1/Vqx8RERkfw3YiIiIiIiIiIqLboFSqcPZstrBy/fjxDCgUSoON37OnPUaN8kBQkBtGjfLAoEEuMDOTGWx8ouYoFUrE/R6HCxsvoDy3HNYu1hg4byACZgZAxtciUZsImBkAl34uyL2c22gfl34uCJgR0I6zIiKixkjUakNeX0tERNR+SkpKIJfLUVxcDDs7O2NPh4iIiIiIuji1Wo1r1wpvhuupOHgwFUVF1QYZWy43R1CQuxCsjxzpBmdnK4OMTXQ7itOKsfGujToDP5d+Lpi3Zx7kXnIjzIyo6+PfPyKizoNhOxERdVoM24mIiIiIqK1lZ5dj//5UYfV6WlqpQcbt398JY8d6CiXh+/RxhFTKcvDUMSgVSqwcsrLZlbWLohdxhTtRG1EqlIjbfrOyRE45rF1vVpaYwcoSREQdCcvIExERERERERER3VRWpsCRI+lCuH7hQp5BxvX0tEVIiDdCQnwwZYoP3NysDTIuUVuI+z2uyaAdAHIv5yJuexwGzBnQTrMiurPIzGQYMGcA/44REXVwDNuJiIiIiIiIiOiOVVOjxOnTWUJp+MjIDNTWqlo9rlxujsmTvRES4o0pU3zQu7cDJBKuXKfO4cLGC3r3YxBIREREdzKG7UREREREREREdMdQq9WIi8tHRERdafhDh9JQWqpo9bhmZjKMGeOBkBAfhIT4YOjQbjAxkRpgxkTtrzy3XL9+Ofr1IyIiIuqqGLYTEREREREREVGXduNGKSIiUoS91zMzDRMQDhniKoTrY8d2h5WVqUHGJTI2axf9tjmwduV2CERERHRnY9hORERERERERERdSnFxNQ4dShP2XY+PLzDIuL6+dpg61RchId6YNMkbLi5WBhmXqKMZOG8gru6+qlc/IiIiojsZw3YiIiIiIiIiIurUqqtrERWVKYTrp09nQalUt3pcR0cLTJniLaxe79HDvvWTJeoEAmYGwKWfC3Iv5zbax6WfCwJmBLTjrIiIiIg6HobtRERERERERETUqahUaly4kCuE60eOpKOiorbV41pYmGD8eE+EhNQF7IGBrpBKJQaYMVHnIjOTYd6eedh410adgbtLPxfM2zMPMjOZEWZHRERE1HFI1Gp16y/zJSIiMoKSkhLI5XIUFxfDzs7O2NMhIiIiIqI2lJJSjIiI1Jt7r6cgN7ey1WNKpRIMH95NWLkeHOwBCwuuTSGqp1QoEbc9Dhc2XkB5TjmsXa0xcN5ABMwIYNBOREREBIbtRETUiTFsJyIiIiLqugoKKnHw4K19169fLzLIuL17Owjh+sSJXnBwsDDIuEREREREdOfhpbpERERERERERGR0VVW1OH78hhCunz2bDUMsEXF1tboZrntjyhQfeHvzQl0iIiIiIjIMhu1ERERERERERNTulEoVoqNzhHD9+PEMVFW1ft91a2tTTJjgKaxeHzDAGRIJ910nIiIiIiLDY9hORERERERERETtIjW1BOHhyQgPT8b+/akoKKhq9ZgymQRBQe5CuB4U5A4z7iVNRERERETtgGE7ERERERERERG1ibIyBQ4dSrsZsKfgypUCg4zbv7+TEK6PH+8JOztzg4xL1FEoFUrE/R6HCxsvoDy3HNYu1hg4byACZgZAxotJiIiIiDoMhu1ERERERERERGQQKpUa0dHZ2Lu3bvX6iRMZqKlRtXpcDw8bTJ1aF65PnuwNDw8bA8yWqGMqTivGxrs2Ivdyruj+q7uvwqWfC+btmQe5l9xIsyMiIiIiTRK1Wq029iSIiIhuR0lJCeRyOYqLi2FnZ2fs6RARERER3ZHS00uxb1/dyvV9+1KQn1/Z6jHt7MwwaZI3QkK8ERLigz59HLnvOt0RlAolVg5ZqRW0a3Lp54JF0Yu4wp2IiIioA+DKdiIiIiIiIiIi0lt5uQJHjqQjPDwF4eHJuHw5v9VjmppKMXq0h1AafvhwN5iYSA0wW6LOJe73uCaDdgDIvZyLuO1xGDBnQDvNioiIiIgaw7CdiIiIiIiIiIgapVKpERubI+y7fuzYDSgUylaPO3CgM6ZO9cHUqb4YN647rK3NDDBbos7twsYLevdj2E5ERERkfAzbiYiIiIiIiIhIJCOjTKM0fDJyc1tfGt7FxRKhob4IDfVFSIgP910n0qE8t1y/fjn69SMiIiKitsWwnYiIiIiIiIjoDldZWXOzNHxdwH7xYl6rxzQzk2Hs2O4IDfVBaKgvAgNdIZVy33Wipli7WOvXz1W/fkRERETUthi2ExERERERERHdYdRqNS5cyLsZrifjyJF0VFe3vjR8v35ON1ev+2D8eE+WhidqoYHzBuLq7qt69SMiIiIi42PYTkRERERERER0B8jKKkdERIoQsGdnV7R6TCcnS0yd6oPQ0Lq91z09bQ0wU6I7V8DMALj0c0Hu5dxG+7j0c0HAjIB2nBURERERNYZhOxERERERERFRF1RVVYtjx24I4XpsbOPhnb5MTaUYM6a7sHp9yJBuLA1PZEAyMxnm7ZmHjXdt1Bm4u/Rzwbw98yAzkxlhdkRERETUkEStVquNPQkiIqLbUVJSArlcjuLiYtjZ2Rl7OkRERERERqVWq3HpUh7Cw+tWrx8+nI6qqtpWj9unjyPCwurC9QkTvGBjw9LwRG1NqVAibnscLmy8gPKccli7WmPgvIEImBHAoJ2IiIioA2HYTkREnRbDdiIiIiK60+XklCMiIlVYvZ6ZWd7qMR0cLBAS4o3QUF9MneoDHx+5AWZKRERNUSqUiPv95gUWueWwdrl5gcVMXmBBRETUkbGMPBERERERERFRJ1FdXYvjxzOEcD06OqfVY5qYSBEc7IHQUB+Ehvpi2LBukMmkBpgtERHpozitWOfWAVd3XxW2DpB78cInIiKijogr24mIqNPiynYiIiIi6urUajXi4wsQHp6MvXuTcfhwGioqWl8avlcvByFcnzjRC3Z25gaYLRERtZRSocTKISu1gnZNLv1csCh6EVe4ExERdUBc2U5ERERERERE1IHk5VVg//760vApSE8vbfWYcrm5qDS8n5996ydKREStFvd7XJNBOwDkXs5F3PY4DJgzoJ1mRURERPpi2E5EREREREREZEQKhRKRkbdKw589m43W1iGUySQYNepWafjhw91gYsLS8EREHc2FjRf07sewvfNRlCmw//X9uLTlEhTlCphZm6H/nP6Y8uEUmNmYGXt6RERkAAzbiYiIiIiIiIjakVqtxtWrhUK4fvBgGsrLa1o9bo8ecoSF+SI01BeTJnlDLmdpeCKijq48t1y/fjn69aOOI/1kOtaOXwulQincV1Neg1PfnMLZlWex4MgCeAZ5GnGGRERkCAzbiYiIiIiIiIjaWEFBpUZp+GSkpra+NLydnRkmT64rDR8a6gt/f/vWT5SIiNqVtYu1fv1c9etHHYOiTKEVtGtSKpRYO34tXst/jSvciYg6OYbtREREREREREQGVlOjRFRUphCunz6d1erS8FKpBCNHugnh+siRbjA1lRlmwkR0R1IqlIj7PQ4XNl5AeW45rF2sMXDeQATMDIDMjP++tIeB8wbi6u6revWjzmP/6/sbDdrrKRVK7H9jP+7+6u52mhUREbUFhu1ERERERERERK2kVquRkFCEvXtvlYYvLVW0elwfHzuhNPzkyd5wcLAwwGyJiIDitGJsvGsjci/niu6/uvsqXPq5YN6eeZB7yY00uztHwMwAuPRz0XoeNLn0c0HAjIB2nBW11qUtl/Tux7CdiKhzY9hORERERERERHQbioqqcOBAKsLDUxAenoykpOJWj2ljYyoqDd+zpz0kEokBZktEdItSodQZtNfLvZyLjXdtxKLoRVzh3sZkZjLM2zOv0eej/sIHPg+di6JcvwvuFGWtvzCPiIiMi2E7EREREREREZEeamtVOHWqvjR8Ck6ezIRK1bra8BIJMGLErdLwo0a5szQ8EbW5uN/jmlxJDdQF7nHb4zBgzoB2mtWdS+4lx6LoRYjbfrOkf045rF1vlvSfwZL+nZGZtRlqymua78f92omIOj2G7UREREREREREjUhMLBLC9f37U1BS0voVaF5etjfDdR9MmeIDJydLA8yU7jTca5ta48LGC3r3Y9jePmRmMgyYM4A/7y6i/5z+OPXNKb36ERFR58awnYiIiIiIiIjopuLiahw8mCoE7AkJRa0e08rKBJMmeWPqVB+EhfmiTx9HloanVuFe29Ra5bnl+vXL0a8fEYlN+XAKzq48C6VC2WgfmZkMUz6Y0o6zIiKitsCwnYiIiIiIiIjuWLW1Kpw5kyWE61FRGVAqW1caHgCGDesmrF4PDvaAuTk/giHD4F7bXYOxKxNYu1jr189Vv35EJGZmY4YFRxZg7fi1OgN3mZkMC44sYBl5IqIugL/pEREREREREdEdJTm5+Ga4noz9+1NRVFTd6jG7d7cR9l2fMsUbLi5WBpgpkTbutd35dYTKBAPnDcTV3Vf16kdEt8czyBOv5b+G/W/sx6Utl6AoU8DMxgz95/THlA+mMGgnIuoiJGq1uvWXaxMRERlBSUkJ5HI5iouLYWdnZ+zpEBEREVEHVVqqwKFDaULAfvVqYavHtLQ0wcSJXsLq9YAAJ5aGp3ax6b5NeoWkve/rjYf/eLgdZkQtoVQosXLIyiYvmHDp59LmlQk6yjyIiIiIOjuubCciIiIiIiKiLkWpVOHcuWyEh6cgPDwZJ05koLZW1epxBw92RVhY3er10aM9YGHBj1Wo/XGv7c6to1QmkJnJMG/PvEa3JKhfYc+gnYiIiKhp/K2QiIiIiIiIiDq9tLQSIVyPiEhBQUFVq8d0c7NGaKgPQkN9ERLig27duHcxGR/32u7cLmy8oHe/tt4GQO4lx6LoRYjbfnPv+JxyWLve3Dt+RvvsHU9ERETU2TFsJyIiIiIiIqJOp6xMgcOH04SAPT6+oNVjWliYYPx4TyFgHzDAmaXhqcPhXtudW0erTCAzk2HAnAFtHuwTERERdVUM24mIiIiIiIiow1Op1IiOvlUa/vjxG6ipaX1p+EGDXIRwfezY7rC0NDXAbInaTsDMALj0c2l2r+2AGQHtOCvSFysTEBEREXUtDNuJiIiIiIiIqENKTy/Fvn3JCA9PQURECvLyKls9pqurFUJDfREa6oOQEB+4u9sYYKZE7Yd7bXdurExARERE1LVI1Gq12tiTICIiuh0lJSWQy+UoLi6GnZ2dsadDRERERK1UUVGDI0fSER6ejPDwZFy6lN/qMc3NZRg37lZp+IEDXSCVsjQ8dX5KhZJ7bXdCSoUSK4esbLYywaLoRXweiYiIiDoBhu1ERNRpMWwnIiIi6txUKjXOn88VwvWjR29AoVC2etwBA5yFcH3cOE9YWbE0PBF1HMVpxc1WJpB7yY0wMyIiIiJqKYbtRETUaTFsJyIiIup8MjPLsG9f3b7r+/alICenotVjOjtbYurUunB96lQfdO9ua4CZUmsoFUrE/X5z1XVuOaxdbq66nslV10QAKxMQERERdRUM24mIqNNi2E5ERETU8VVW1uDo0RvC6vULF/JaPaapqRRjx3a/ufe6LwYPdmVp+A6Eq3aJiIiIiOhOwbCdiIg6LYbtRERERB2PWq3GhQt5Qrh+5Eg6qqtbXxo+IMBRCNcnTPCEtbWZAWZLhsb9qO8crF5ARERERASYGHsCRERERERERNS5ZWeXi0rDZ2WVt3pMR0cLUWl4Ly9eXNkZxP0e12TQDgC5l3MRtz0OA+YMaKdZdT4dPchurHrB1d1XWb2AiIiIiO4oDNuJiIiIiIiIqEWqqmpx7Nit0vCxsU2Hq/owMZFizBgPYfX6kCGukMmkBpgttacLGy/o3Y9hu24dPchWKpSNbhMA1F1MsfGujaxeQERERER3BIbtRERERERERNQktVqNy5fzhXD98OF0VFbWtnrcPn0cERrqc7M0vBdsbVkavrMrz9WvqkF5TuurH3RFnSHIZvUCIiIiIqJbGLYTERERERERkZbc3ApERKTcDNhTkJFR1uoxHRwsEBLiLZSG9/FhmemuxtrFWr9+rvr1u9N0hiCb1QuIiIiIiG5h2E5EREREREREqK6uxYkTGUK4fu5cdqvHlMkkCA6uKw0fFuaLYcO6sTR8Fzdw3kBc3X1Vr36krTME2axeQERERER0C8N2IiIiIiIiojuQWq1GfHyBUBr+0KE0VFS0vjR8z572wr7rkyZ5wc7O3ACzpc4iYGYAXPq5NLk626WfCwJmBLTjrDqPzhBks3oBEREREdH/s3fn8VFVd//AvzMTQLYgIIJsKu6oRdAo2qqo1apVXOuK1rYqtbGttXZRW23tU21r1dbK76laFWlRH1fctVbBBZfGBVxQsVYWEWQPIHuY3x+YkSEsmWSSmZD3+/W6L+bcuffc70wmAfK555wvCNsBAACgmZg9e3E8/fSUzOj1jz9eWO8+O3RoFYcc8sXU8H36bF7/QmmyUi1TcfoTp6933fEufbvE6U+cXrD1xotdUwiyzV4AAABfELYDAADAJmr58qp46aVPMqPXX3vt00in69dnKpWIffbZKjN6vaysW5SUmBqeL3To1SGGvjE03n3g3Xhr5Fvx2czPou2WbWP303ePXY7bRdC+AU0hyDZ7AQAAfCGRTtf3v9kAUBgLFiyIDh06RGVlZZSWlha6HACAgkun0zFx4rxMuD569NT47LMV9e532207xNe+9sXU8JtvvlkeqgXWVrW8Km7sf+NGg+yhbwwt6E0LlVMrNzp7QYdeHQpQGQAANC5hOwANZu7cuTF27Nj4+OOPY8GCBbHVVltFnz59Yr/99otksv6jn4TtAAARc+cuWWNq+EkxZUr9p4Zv375lZmr4ww7bJrbbbvP6FwrUSlMJsquWV5m9AACAZk/YDkDeffDBB/Hzn/88HnnkkVi+fHmN57t37x7nnntuXHzxxdGyZcs6X0fYDgA0RytWVMUrr0zPrLteUTEjVq2q33/tk8lE7L13t0y4vvfe3aJFC2EZFIogGwAAmgZhOwB5NXLkyBg6dGh89tlnGz12wIABcf/998fWW29dp2sJ2wGA5iCdTseHH87PhOvPPDMlFi6seUNjrrbeujQzNfzBB/eOjh2LZ2r4quVV8e79nweNsz6Ltl0+DxqPFzQCAABQPITtAOTNE088EUcddVRUVVVl9u2www5x8MEHR6dOneLDDz+Mhx9+OJYsWZJ5frfddosXX3wx2rdvn/P1hO0AwKZq/vyl8cwzU+Kf/5wc//znpPjoo8p699muXYs46KDecdhhW8dhh20TO+zQMRKJRB6qza9CTqEt5AcAACAXwnYA8mLGjBmx8847R2Xl6l8EJxKJ+OMf/xgXXHBB1vrss2bNim984xvx7LPPZvaddtppMXLkyJyvKWwHADYFVVWrYvr0z+LDD+fH6NGrA/ZXXple76nhE4mIvfaqnhp+6xg4sHu0LPLAuGp5VdzY/8Z1Bu3VuvTtEkPfGJr38LuprJMNAABA8RC2A5AX3//+9+OGG27ItK+44or45S9/uc5jly5dGgMGDIh33303IlYH82+88Ub069cvp2sK2wGAYpdOp2Pu3KUxderCmDJlQdaf1Y+nTVsUVVX5+a95r17tM+H6IYdsHZ07t85Lv43l7bvejvtOvW+jx51w1wmx28m75e26hQz5AQAAaLqE7QDU28yZM6N3796xbNmyiIjYbrvt4t13340WLVqs95xnnnkmDjnkkEz7hBNOiHvvvTen6wrbAYBCW7x4RVZwnv3nwpg6dUEsXryywa7fpk3J51PDrw7Yd9qpU1FODV9bdx59Z0x8ZOJGj9vx6B3j1IdOzdt1CxXyAwAA0LSVFLoAAJq+Bx98MBO0R0Sce+65GwzaIyIOPvjg2GmnneL999+PiIjHHnssFi9eHG3atGnQWgGApqkQa2mvXLkqpk9f9Hlovu4wfc6cJQ1y7Q3Zc8+umXB93327R6tWm85/7T+b9VntjptZu+Nq662Rb9X6OGE7AAAA1Tad/5EDUDAPPfRQVvvEE0+s1Xnf+MY34n/+538iImLJkiXxz3/+M4499th8lwcAzUIhwujGsr61tCc+MrHOa2mn0+mYM2fJeoP0qVMXxrRpi+q9bno+9OjR7vNwfZs45JDe0aVLbjcnNqXPRtsubWt33Ja1O662ChXyAwAA0LQJ2wGot+effz7zuGvXrtGnT59anbfvvvtmtZ977jlhOwDUQUOE0cWiannVOl9btVkTZsXIw0fWWEv7s8+WrzGV+7rD9CVLGm569/po3bokBg3qlRm9vssunes8NXxT+2zsfvrutZpGfvfTd8/rdQsV8gMAANC0CdsBqJfp06dHZWVlpt2/f/9anztgwICs9rvvvpu3uqi7xhj9VqzXqFpeFe/c/U68dO1LMf+j+RERsXmfzWPfH+0bu560a9GN/sunur5fxfh1bGp9NWSfDaGx6yzW96W6rjf//mbMnjg7li9YHq1KW0XnHTvHl874UqPWV9cwuql49/53a7y2qohYGImo/HxbMGF+PHf0fbG4VYtMmD537tLCFFxL7dq1iN69S6NXr/Zr/Nk++vTZPMrKusVmm234v+u1+d5oip+NXY7fJbr07bLemiMiuvTtErsct0ter1uokD9f1vd52PGoHWPiIxOL7u93AACATUUinU4Xfk48AJqs0aNHx8EHH5xpn3vuuXHjjTfW6tx0Oh2bbbZZLF++PCIittlmm/joo49qfe0FCxZEhw4dorKyMkpLS3MrvIlq6L+2M6Pf3p1d47kuu2yRl9FvxXqNyqmVMeKQETH3P/PW2Wen7TvGmU+fWVSj//Klru9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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 28\n", - "width = 20\n", - "height = 15\n", - "\n", - "#supplemental figure (GECKO)\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs = gridspec.GridSpec(1, 1, figure=fig)\n", - "#adjust labels for better readibility\n", - "x_csc_label_gecko = adjust_heatmap_labels(x_csc_nonzero_gecko)\n", - "x_esc_label_gecko = adjust_heatmap_labels(x_esc_top5_gecko)\n", - "\n", - "fig_gecko = make_heatmap_subfigure(results = results_gecko, csc_matrix=csc_nonzero_gecko_t, esc_matrix =esc_top5_gecko,\n", - " cbar =True, ylabels = True, xlabels = True,x_csc=x_csc_label_gecko,\n", - " x_esc=x_esc_label_gecko, yaxis = glc_uptake_rates, fig = fig, grdspc = gs[0],\n", - " phenotype_data = pt_data, fontsize = fontsize, cmap=cmap\n", - " )\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/SuppFigure2_sensitivities_gecko.png', dpi =300,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecfd9d55", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAModelpy", - "language": "python", - "name": "pamodelpy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/.ipynb_checkpoints/Figure3_sensitivities_protein-overproduction-checkpoint.ipynb b/Figures/.ipynb_checkpoints/Figure3_sensitivities_protein-overproduction-checkpoint.ipynb deleted file mode 100644 index d7df101..0000000 --- a/Figures/.ipynb_checkpoints/Figure3_sensitivities_protein-overproduction-checkpoint.ipynb +++ /dev/null @@ -1,1622 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6d2a8cae-9e89-4faa-9b23-2c97b6186037", - "metadata": {}, - "source": [ - "# Code to generate figure 3 in the publication\n", - "Analysis of sensitive enzymes and reactions in the model simulations of protein overexpression" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3e02abee-b41a-4257-ae81-9f22b71d17b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading PAModelpy modules version 0.0.3.3\n" - ] - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "import matplotlib.colors as mcolors\n", - "\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "from optlang.symbolics import Zero\n", - "\n", - "from PAModelpy.configuration import Config\n", - "from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector, CustomSector\n", - "from PAModelpy.Enzyme import Enzyme\n", - "\n", - "if os.path.split(os.getcwd())[1] == 'Figures':\n", - " os.chdir(os.path.split(os.getcwd())[0])\n", - " \n", - "from Scripts.pam_generation import set_up_ecoli_pam\n", - "\n", - "DATA_DIR = 'Data'\n", - "eGFP_MW = 2.8*1e4 #g/mol\n", - "eGFP_RANGE = np.arange(0,0.15,0.01)\n", - "GLC_CONC = 9.81 #mmol_glc/gcdw/h\n", - "eGFP_BEINICK_DATA_PATH = os.path.join(DATA_DIR, 'eGFP_expression_Bienick2014.xls')\n", - "eGFP_SEQ_PATH = os.path.join(DATA_DIR, 'eGFP_protein_sequence.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eb3e6732-331c-4e03-8b71-cae05360439a", - "metadata": {}, - "outputs": [], - "source": [ - "BIOMASS_RXNID = 'BIOMASS_Ec_iML1515_core_75p37M'" - ] - }, - { - "cell_type": "markdown", - "id": "47bbc17e-1d0f-4375-922a-4f630333f787", - "metadata": {}, - "source": [ - "## sensitivities of protein overexpression in the PAM of E.coli" - ] - }, - { - "cell_type": "markdown", - "id": "fd5ff0a1-bc1e-42fe-bc4c-7d93571a5c27", - "metadata": {}, - "source": [ - "### 1. Usefull functions" - ] - }, - { - "cell_type": "markdown", - "id": "75ddd447-76d1-49d1-b93f-6af5e4357b0f", - "metadata": {}, - "source": [ - "#### 1.1 Sensitivity analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "69b86edc-c37a-420f-9c88-2fa9fd186b59", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_sensitivities(pamodel):\n", - " #initialize objects for storing information\n", - " results_df = pd.DataFrame(columns = ['eGFP', 'mu', 'mu_normalized'])\n", - " Ccsc = [] #capacity sensitivity coefficients\n", - " Cesc = [] #variable sensitivity coefficients\n", - " x_axis_esc = []\n", - " x_axis_csc = []\n", - " y_axis = []\n", - " fluxes = []\n", - " \n", - " # #set glucose uptake rate\n", - " # pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " # lower_bound = -100, upper_bound = 0)\n", - " \n", - " #set glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -GLC_CONC, upper_bound = -GLC_CONC)\n", - " \n", - " \n", - " for conc in eGFP_RANGE:\n", - " print('Running simulations with the following eGFP concentration: ', conc, 'mmol/g_cdw/h')\n", - " with pamodel:\n", - " #change eGFP concentration\n", - " pamodel.constraints['eGFP_min'].ub = -conc*1e3\n", - " sol_pam = pamodel.optimize()\n", - " #check if simulation is optimal\n", - " if pamodel.solver.status == 'optimal': \n", - " y_axis += [conc]\n", - " \n", - " # save data\n", - " fluxes.append(sol_pam.fluxes) # flux distributions\n", - " \n", - " # calculate normalized growth rate\n", - " mu = pamodel.reactions.get_by_id(BIOMASS_RXNID).flux\n", - " if conc == 0:\n", - " mu_normalized = 1\n", - " mu_wt = mu\n", - " else:\n", - " mu_normalized = mu/mu_wt\n", - " results_df.loc[len(results_df)] = [conc,mu, mu_normalized]\n", - " \n", - " #save sensitivities\n", - " Ccsc_new = list()\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if csc == 'EC_min_f':\n", - " Ccsc_new += [-coef for coef in capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()]\n", - " else:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - " \n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of variable sensitivity coefficients: \\t \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6), '\\n')\n", - "\n", - " return {'Ccsc':Ccsc, 'Cesc':Cesc, 'y_axis':y_axis, 'fluxes':fluxes, 'capacity coefficients':capacity_coeff, \n", - " 'enzyme coefficients':enzyme_coeff, 'results': results_df}\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f0b9ed5c-3082-4af5-8e58-5f56e30e5f79", - "metadata": {}, - "outputs": [], - "source": [ - "def find_nonzero_sensitivities(Cv, x_axis):\n", - " indices = []\n", - " for row in Cv:\n", - " for index, coeff in enumerate(row):\n", - " if abs(coeff)>0.09 and index not in indices:\n", - " indices.append(index)\n", - " \n", - " coeff_nonzero = []\n", - " for row in Cv:\n", - " coeff_nonzero.append([coeff for i, coeff in enumerate(row) if i in indices])\n", - " x_coeff_nonzero = [coeff for i, coeff in enumerate(x_axis) if i in indices]\n", - "\n", - " return coeff_nonzero, x_coeff_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ec341893-b52f-4b88-a488-2581b93ee22b", - "metadata": {}, - "outputs": [], - "source": [ - "def find_top5_sensitivities(Cv, x_axis, yaxis):\n", - " #top 5 enzymes per simulation\n", - " Cv_df = pd.DataFrame(Cv, columns = x_axis, index =yaxis)\n", - " largest = list()\n", - " for i, row in Cv_df.iterrows():\n", - " top5 = abs(row).nlargest() \n", - " if top5.iloc[0]:\n", - " largest += [index for index, value in top5.items() if abs(value)>0.05]\n", - " print([index for index, value in top5.items() if abs(value)>0.05])\n", - " \n", - " #remove duplicates\n", - " largest_list = list(set(largest))\n", - "\n", - " #extract non duplicate top5 enzymes\n", - " top5_df = Cv_df[largest_list].T.drop_duplicates().sort_index()\n", - " largest_list = top5_df.index.values\n", - "\n", - " top5_matrix = [list(row) for i, row in top5_df.iterrows()]\n", - " return top5_matrix, largest_list\n" - ] - }, - { - "cell_type": "markdown", - "id": "b8633232-45b6-4af2-8099-2fb4841ef988", - "metadata": {}, - "source": [ - "#### 1.2 Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e47c6d59-277f-41bc-89a6-647ecc476120", - "metadata": {}, - "outputs": [], - "source": [ - "def parse_x_axis_heatmap(capacity_coeff, enzyme_coeff):\n", - " x_axis_csc = []\n", - " \n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if csc == 'flux_ub' or csc == 'flux_lb':\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[capacity_coeff['constraint'] == csc].rxn_id.to_list()]\n", - " else:\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == csc].enzyme_id.to_list()]\n", - " \n", - " x_axis_esc = enzyme_coeff.enzyme_id.to_list()\n", - " return x_axis_csc, x_axis_esc" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cf73a888-2270-4ea9-9cc8-4c9e0f5eceaa", - "metadata": {}, - "outputs": [], - "source": [ - "def make_heatmap_subfigure(results, csc_matrix, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc, ylabels = True,\n", - " xlabels=False, cbar =True, title = None, fontsize = 16, vmin = -1.5, vmax = 1.5,\n", - " annotate = None, phenotype_data = None,cmap = None):\n", - " # fig = plt.figure()\n", - " if cmap is None:\n", - " # Create separate colormaps for positive and negative values and a color for zero\n", - " colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128))\n", - " colors_pos = plt.cm.OrRd(np.linspace(0.1,1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128))\n", - "\n", - " colors_zero = np.array([[1,1,1, 1]]) # gray for zero\n", - "\n", - " # Combine them into a single colormap\n", - " colors = np.vstack((colors_neg, colors_zero, colors_pos))\n", - " combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap')\n", - "\n", - " # Create a norm that handles the zero color properly\n", - " bounds = np.linspace(vmin, vmax, len(colors))\n", - " norm = mcolors.BoundaryNorm(bounds, combined_cmap.N)\n", - "\n", - " if cbar:\n", - " gs = gridspec.GridSpecFromSubplotSpec(2, 2, width_ratios=[len(yaxis), 1], \n", - " height_ratios=[len(x_csc), len(x_esc)], hspace =0, subplot_spec=grdspc)\n", - " else:\n", - " gs = gridspec.GridSpecFromSubplotSpec(2, 1, width_ratios=[len(yaxis)], \n", - " height_ratios=[len(x_csc), len(x_esc)], hspace =0, subplot_spec=grdspc)\n", - " \n", - " esc_ax = fig.add_subplot(gs[1,0]) #ESC heatmap\n", - " csc_ax = fig.add_subplot(gs[0,0],sharex = esc_ax) #CSC heatmap\n", - " if cbar:\n", - " cbar_ax = fig.add_subplot(gs[0:,1]) #colorbar\n", - "\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " csc_ax.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.1), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize+5, weight = 'bold')\n", - "\n", - " #CAC heatmap\n", - " im_csc = csc_ax.imshow(csc_matrix, aspect=\"auto\", vmin = vmin, vmax =vmax,cmap = combined_cmap)\n", - " if title is not None: csc_ax.set_title(title, fontsize = fontsize*1.5)\n", - " csc_ax.set_yticks(np.arange(len(x_csc)), labels=x_csc, fontsize =fontsize)\n", - " csc_ax.xaxis.set_visible(False)\n", - " if ylabels:\n", - " csc_ax.set_ylabel('CSC', fontsize = fontsize*1.5)\n", - "\n", - " #Make line between CSC and ESC data more clear\n", - " axis = 'bottom'\n", - " csc_ax.spines[axis].set_linewidth(10)\n", - " csc_ax.spines[axis].set_color(\"black\")\n", - " csc_ax.spines[axis].set_zorder(0)\n", - " \n", - " #ESC heatmap\n", - " im_esc = esc_ax.imshow(esc_matrix, aspect=\"auto\", vmin = vmin, vmax =vmax,cmap = combined_cmap)\n", - " esc_ax.set_yticks(np.arange(len(x_esc)), labels=x_esc, fontsize =fontsize)\n", - " esc_ax.set_xticks(np.arange(len(yaxis)),labels = yaxis, fontsize =fontsize, rotation=45, ha='right')\n", - " if ylabels:\n", - " esc_ax.set_ylabel('ESC', fontsize = fontsize*1.25)\n", - " if xlabels:\n", - " esc_ax.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - " \n", - " #colorbar\n", - " if cbar:\n", - " cbar_ax.xaxis.set_visible(False)\n", - " make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm,\n", - " vmin = vmin, vmax=vmax, fontsize=fontsize*1.25)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f1fc7967-5869-4911-8ed4-2cee897adbd7", - "metadata": {}, - "outputs": [], - "source": [ - "def make_scaled_colorbar(ax, fig, cmap, norm,vmin, vmax,\n", - " fontsize=16, cbarlabel='Sensitivity Coefficient'):\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " \n", - " cbar = fig.colorbar(sm, ax=ax, cax=ax, shrink=1, fraction=1)\n", - " \n", - " # Adjust the tick intervals\n", - " tick_locations = np.linspace(vmin, vmax, num=5) # Adjust num to the desired number of ticks\n", - " cbar.set_ticks(tick_locations)\n", - " cbar.set_ticklabels([f\"{tick:.1f}\" for tick in tick_locations]) # Optional: customize tick labels\n", - "\n", - " # Setting the fontsize of the colorbar\n", - " cbar.set_label(cbarlabel, fontsize=fontsize)\n", - " cbar.ax.tick_params(labelsize=fontsize)\n", - " cbar.ax.yaxis.get_offset_text().set(size=fontsize)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ba775506-b0e9-4fc3-a403-7c68515059e7", - "metadata": {}, - "outputs": [], - "source": [ - "#adjust labels for better readibility\n", - "def adjust_heatmap_labels(labels):\n", - " new_labels = labels.copy()\n", - "\n", - " for i, label in enumerate(labels):\n", - " if 'EX_glc__D_e' in label or label[:-3] == 'EX_glc__D_e':\n", - " if label[-1] == 'B': new_labels[i] = 'EX_glc_'+label[-2:]\n", - " else: new_labels[i] = 'EX_glc_lb'\n", - " if label == 'TotalProteinConstraint_proteome':\n", - " new_labels[i] = 'Protein pool'\n", - " if label == 'eGFP_enzyme_min':\n", - " new_labels[i] = 'eGFP_min'\n", - " if label[0].isdigit(): #all enzyme ids start with a digit\n", - " if label == '2.7.3.9':\n", - " new_labels[i] = 'Glucose\\ntransport'\n", - " else:\n", - " rxn_ids = pamodel.get_reactions_with_enzyme_id(label)\n", - " rxn_name = pamodel.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0]\n", - " new_labels[i] = '\\n'.join([part for part in rxn_name.split(' ')])\n", - " return new_labels" - ] - }, - { - "cell_type": "markdown", - "id": "3fcf2695-9f1e-47f6-a20e-b39b9f00bad5", - "metadata": {}, - "source": [ - "## 1.3 Determining aminoacid content of eGFP" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5335c49f-82fd-44ca-add8-7f95aaf4fbe7", - "metadata": {}, - "outputs": [], - "source": [ - "def check_freq(x, total):\n", - " return {c: x.count(c)/total for c in set(x)}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3eedb11a-a294-4c85-ab17-16cf36683efa", - "metadata": {}, - "outputs": [], - "source": [ - "#aminoacid lookup table\n", - "aa_lookup ={'V':'VAL', 'I':'ILE', 'L':'LEU', 'E':'GLU', 'Q':'GLN', \\\n", - "'D':'ASP', 'N':'ASN', 'H':'HIS', 'W':'TRP', 'F':'PHE', 'Y':'TYR', \\\n", - "'R':'ARG', 'K':'LYS', 'S':'SER', 'T':'THR', 'M':'MET', 'A':'ALA', \\\n", - "'G':'GLY', 'P':'PRO', 'C':'CYS'}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "743d4dbe-7d07-48ba-ba91-25aa86598269", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MRKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFGYGVQCFARYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYKLEHHHHHH\n" - ] - } - ], - "source": [ - "#read amino acid sequence\n", - "with open(eGFP_SEQ_PATH) as f:\n", - " lines = f.readlines()\n", - "#need to remove document start ('\\ufeff') and end ('\\n') to only yield the amino acid sequence\n", - "aa_seq = lines[0].strip().replace('\\ufeff', '')\n", - "\n", - "print(aa_seq)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "84f51474-b4ef-45a4-85c2-9cdae265fb5f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'trp__L_c': 0.0040650406504065045,\n", - " 'asp__L_c': 0.07317073170731707,\n", - " 'val__L_c': 0.06910569105691057,\n", - " 'pro__L_c': 0.04065040650406504,\n", - " 'met__L_c': 0.024390243902439025,\n", - " 'phe__L_c': 0.052845528455284556,\n", - " 'leu__L_c': 0.08130081300813008,\n", - " 'ile__L_c': 0.04878048780487805,\n", - " 'glu__L_c': 0.06910569105691057,\n", - " 'arg__L_c': 0.028455284552845527,\n", - " 'ser__L_c': 0.032520325203252036,\n", - " 'lys__L_c': 0.08130081300813008,\n", - " 'asn__L_c': 0.052845528455284556,\n", - " 'ala__L_c': 0.036585365853658534,\n", - " 'cys__L_c': 0.008130081300813009,\n", - " 'gly_c': 0.09349593495934959,\n", - " 'gln__L_c': 0.032520325203252036,\n", - " 'tyr__L_c': 0.044715447154471545,\n", - " 'thr__L_c': 0.06097560975609756,\n", - " 'his__L_c': 0.06504065040650407}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#determine amino acid composition\n", - "aa_freq = check_freq(aa_seq, len(aa_seq))\n", - "\n", - "#match to model identifiers\n", - "aa_biggid_freq = dict()\n", - "for aa, freq in aa_freq.items():\n", - " threeletter = aa_lookup[aa].lower()\n", - " if threeletter != 'gly':\n", - " bigg_id = f'{threeletter}__L_c'\n", - " else: \n", - " bigg_id = f'{threeletter}_c'\n", - " aa_biggid_freq[bigg_id] = freq\n", - " \n", - "aa_biggid_freq" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "313f2b25-b355-4ad3-8205-f5b5c7f991be", - "metadata": {}, - "outputs": [], - "source": [ - "def add_aminoacid_sequence(model, seq, protein):\n", - " \"\"\"\n", - " model: COBRA model\n", - " seq: dict with {aminoacid_id: freq} key, value pairs\n", - " protein: enzyme variable\n", - " \"\"\"\n", - " for aa, freq in seq.items():\n", - " model.constraints[aa].set_linear_coefficients({\n", - " protein.forward_variable: -freq/protein.molmass,\n", - " protein.reverse_variable: -freq/protein.molmass\n", - " }) \n", - " return model" - ] - }, - { - "cell_type": "markdown", - "id": "c259d078-bc25-4abc-9725-54b159fcbe79", - "metadata": {}, - "source": [ - "### 2 Run PAM simulations with default protein content\n", - "#### 2.1 Build the model and add the eGFP protein" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "edf9d83b-43b2-469d-a031-2a487f22efd7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "Read LP format model from file /tmp/tmpli4k8wpd.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.258 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/PAModel.py:222: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f'Molar mass for {enz.id} is invalid: {molmass}')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: \n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel = set_up_ecoli_pam()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bd4a7d2e-577c-4f26-8093-3745d491f685", - "metadata": {}, - "outputs": [], - "source": [ - "#add eGFP enzyme and aminoacid consumption\n", - "eGFP_enzyme = Enzyme('eGFP', {}, molmass = 1e6) #molmass of 1e6 to get a direct relation between enzyme concentration and total protein content\n", - "pamodel.add_enzymes([eGFP_enzyme])\n", - "pamodel = add_aminoacid_sequence(pamodel, aa_biggid_freq, pamodel.enzyme_variables.get_by_id('eGFP'))\n", - "\n", - "#turn off Pyruvate Formate Lyase (PFL) reaction (inhibited by oxygen)\n", - "pamodel.change_reaction_bounds('PFL',0,0)" - ] - }, - { - "cell_type": "markdown", - "id": "2b696f7d-d722-49ba-b5ff-4ac0e846aa79", - "metadata": {}, - "source": [ - "#### 2.3 Run simulations for different eGFP concentrations" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6b9e3329-f635-4e9f-be49-bfe044aeab30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running simulations with the following eGFP concentration: 0.0 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.735457 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.01 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.725774 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.02 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.715353 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.03 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.704107 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.04 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.728808 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.05 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.766706 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.06 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.754825 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.07 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.741665 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.08 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.959542 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.09 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000995\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.014114 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.1 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.00109\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.016058 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.11 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001203\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.017774 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.12 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001342\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.019888 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.13 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001562\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.051363 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.14 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001856\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.089343 \n", - "\n" - ] - } - ], - "source": [ - "results_pam = calculate_sensitivities(pamodel)\n", - "x_axis_csc_pam,x_axis_esc_pam = parse_x_axis_heatmap(results_pam['capacity coefficients'], results_pam['enzyme coefficients'])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0e45ccd4-69f1-4ca5-96fc-5e9c77523197", - "metadata": {}, - "outputs": [], - "source": [ - "#get nonzero sensitivities\n", - "csc_nonzero_pam, x_csc_nonzero_pam = find_nonzero_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam)\n", - "esc_nonzero_pam, x_esc_nonzero_pam = find_nonzero_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d285624b-a4e2-4189-a7c1-c3abd74b7e05", - "metadata": {}, - "outputs": [], - "source": [ - "csc_nonzero_pam_t = np.transpose(np.array(csc_nonzero_pam))\n", - "esc_nonzero_pam_t = np.transpose(np.array(esc_nonzero_pam))" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "97d5be6c-4f10-46fb-82a2-f0bbad45e0e6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '2.3.1.16', '1.2.4.1', '1.1.1.86']\n", - "['3.6.3.14', '2.3.1.16', '1.1.1.86', '2.7.3.9']\n", - "['3.6.3.14', '2.7.3.9', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '2.7.3.9', '2.3.1.16', '1.1.1.86']\n" - ] - } - ], - "source": [ - "#get top5 nonzero sensitivities\n", - "csc_top5_pam, x_csc_top5_pam = find_top5_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam, yaxis = eGFP_RANGE)\n", - "esc_top5_pam, x_esc_top5_pam = find_top5_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam, yaxis = eGFP_RANGE)" - ] - }, - { - "cell_type": "markdown", - "id": "de650fc9-f708-444b-a278-8f96fee23252", - "metadata": {}, - "source": [ - "### 3. Run PAM simulations with more efficient ATP synthase\n", - "ATP synthase is the enzyme contributing the most to the enzyme burden" - ] - }, - { - "cell_type": "markdown", - "id": "63313890-2a22-49c1-a3dc-96bbc359a378", - "metadata": {}, - "source": [ - "#### 3.1 Build PAModel" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "72498350-f562-4921-ae68-88b78c89917a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "79.0" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#get old kcat value to change\n", - "old_kcat_atp = pamodel.enzymes.get_by_id('3.6.3.14').get_kcat_values('ATPS4rpp')['f']\n", - "old_kcat_atp" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ce56a430-0cc9-4a13-b163-5c1f0c5fafd0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmpivebp_ql.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.258 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/PAModel.py:222: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f'Molar mass for {enz.id} is invalid: {molmass}')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: \n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel_atp = set_up_ecoli_pam()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "f969f4a4-a015-418a-ab13-f3eba1f914d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ATPS4rpp\n", - "{'f': 158.0}\n", - "{'3.6.3.14': {'f': 79.0, 'b': 285.0, 'molmass': 234022.89499999996}}\n" - ] - } - ], - "source": [ - "#make atp synthase enzyme twice as efficient\n", - "new_kcat_atp = old_kcat_atp*2\n", - "active_enzyme = pamodel.sectors.get_by_id('ActiveEnzymeSector')\n", - "for rxn, kcat in {'ATPS4rpp':{'f': new_kcat_atp}}.items():\n", - " print(rxn)\n", - " print(kcat)\n", - " print(active_enzyme.rxn2protein[rxn])\n", - "\n", - "pamodel_atp.change_kcat_value(enzyme_id = '3.6.3.14', kcats = {'ATPS4rpp':{'f': new_kcat_atp}})\n", - "\n", - "#add eGFP protein\n", - "pamodel_atp.add_enzymes([eGFP_enzyme])\n", - "pamodel_atp = add_aminoacid_sequence(pamodel_atp, aa_biggid_freq, pamodel_atp.enzyme_variables.get_by_id('eGFP'))\n", - "\n", - "#turn off Pyruvate Formate Lyase (PFL) reaction (inhibited by oxygen)\n", - "pamodel_atp.change_reaction_bounds('PFL',0,0)" - ] - }, - { - "cell_type": "markdown", - "id": "4b88ec03-baeb-4f0f-aaad-3ccb31373122", - "metadata": {}, - "source": [ - "#### 3.2 Run simulations for different eGFP concentrations" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "b242baad-747a-4fc6-8b8c-0e497ddf6628", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running simulations with the following eGFP concentration: 0.0 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000506\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.780457 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.01 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000526\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.771913 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.02 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000547\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.762675 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.03 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.00057\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.752655 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.04 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000598\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.744873 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.05 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000626\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.733051 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.06 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000656\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.720077 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.07 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001345\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.740334 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.08 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001421\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.725628 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.09 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001507\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.709153 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.1 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.002268\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.976606 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.11 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.002493\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.97432 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.12 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.002836\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.995158 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.13 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.003199\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.994593 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.14 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.003667\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.993862 \n", - "\n" - ] - } - ], - "source": [ - "results_atp = calculate_sensitivities(pamodel_atp)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "60319828-627e-4d99-9e9d-ac9c764dad7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['3.6.3.14', '4.2.1.3']\n", - "['3.6.3.14', '4.2.1.3']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14', '2.3.1.16']\n", - "['3.6.3.14', '2.3.1.16']\n", - "['3.6.3.14', '2.3.1.16']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n", - "['3.6.3.14', '2.3.1.16', '1.1.1.86', '1.2.4.1']\n", - "['3.6.3.14', '2.3.1.16', '1.1.1.86', '2.7.3.9']\n" - ] - } - ], - "source": [ - "#parse results for nice plotting\n", - "x_axis_csc_atp,x_axis_esc_atp = parse_x_axis_heatmap(results_atp['capacity coefficients'], results_atp['enzyme coefficients'])\n", - "\n", - "#find top 5 sensitivities\n", - "csc_top5_atp, x_csc_top5_atp = find_top5_sensitivities(results_atp['Ccsc'], x_axis = x_axis_csc_atp, yaxis = eGFP_RANGE)\n", - "esc_top5_atp, x_esc_top5_atp = find_top5_sensitivities(results_atp['Cesc'], x_axis = x_axis_esc_atp, yaxis = eGFP_RANGE)" - ] - }, - { - "cell_type": "markdown", - "id": "e58822c2-7c17-4c99-b0c5-cd52c6862db9", - "metadata": {}, - "source": [ - "### 3. Run PAM simulations with total protein content of 0.31 g/g_cdw\n", - "#### 3.1 Build PAModel" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "bf9cd6cb-6992-4df6-a10b-47405063969e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmp4_xmbxls.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.31 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/PAModel.py:222: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f'Molar mass for {enz.id} is invalid: {molmass}')\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: \n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel_inc = set_up_ecoli_pam(total_protein =0.31)\n", - "#add eGFP protein\n", - "pamodel_inc.add_enzymes([eGFP_enzyme])\n", - "pamodel_inc = add_aminoacid_sequence(pamodel_inc, aa_biggid_freq, pamodel_inc.enzyme_variables.get_by_id('eGFP'))\n", - "\n", - "#turn off Pyruvate Formate Lyase (PFL) reaction (inhibited by oxygen)\n", - "pamodel_inc.change_reaction_bounds('PFL',0,0)" - ] - }, - { - "cell_type": "markdown", - "id": "a87b7228-cc6b-42d7-81b1-ed069b20b55d", - "metadata": {}, - "source": [ - "#### 3.2 Run simulations for different eGFP concentrations" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "191d8735-d3a4-4fc0-afd7-960dc42bb628", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running simulations with the following eGFP concentration: 0.0 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.113115 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.01 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.673889 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.02 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.753316 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.03 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.745336 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.04 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.746299 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.05 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.737405 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.06 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.727862 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.07 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.717598 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.08 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.706527 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.09 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.694551 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.1 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001568\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.722234 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.11 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001757\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.758754 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.12 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001851\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.745918 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.13 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.874575 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.14 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.013072 \n", - "\n" - ] - } - ], - "source": [ - "results_inc = calculate_sensitivities(pamodel_inc)" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "3f87f6f6-a880-4adb-b1f8-3daf07f8a9f6", - "metadata": {}, - "outputs": [], - "source": [ - "#parse x-axis\n", - "x_axis_csc_inc,x_axis_esc_inc = parse_x_axis_heatmap(results_inc['capacity coefficients'], results_inc['enzyme coefficients'])\n", - "#get nonzero sensitivities\n", - "csc_nonzero_inc, x_csc_nonzero_inc = find_nonzero_sensitivities(results_inc['Ccsc'], x_axis = x_axis_csc_inc)\n", - "esc_nonzero_inc, x_esc_nonzero_inc = find_nonzero_sensitivities(results_inc['Cesc'], x_axis = x_axis_esc_inc)\n", - "csc_nonzero_inc_t = np.transpose(np.array(csc_nonzero_inc))\n", - "esc_nonzero_inc_t = np.transpose(np.array(esc_nonzero_inc))" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "9e342a99-72d7-4180-85c4-79611d46e01f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['EX_glc__D_e_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "[]\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14']\n", - "['3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16']\n", - "['3.6.3.14', '1.2.4.1', '2.3.1.16', '1.1.1.86']\n" - ] - } - ], - "source": [ - "#get top5 nonzero sensitivities\n", - "csc_top5_inc, x_csc_top5_inc = find_top5_sensitivities(results_inc['Ccsc'], x_axis = x_axis_csc_inc, yaxis = eGFP_RANGE)\n", - "esc_top5_inc, x_esc_top5_inc = find_top5_sensitivities(results_inc['Cesc'], x_axis = x_axis_esc_inc, yaxis = eGFP_RANGE)" - ] - }, - { - "cell_type": "markdown", - "id": "999ba86b-fa39-470a-ad63-9c99da737ee7", - "metadata": {}, - "source": [ - "### 4 Create plot" - ] - }, - { - "cell_type": "markdown", - "id": "df74f52e-fdf7-4b82-b797-297bd4a139a7", - "metadata": {}, - "source": [ - "#### 4.1 Load phenotypic data" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "b11ae446-27b5-4e18-b3bb-9553acbb397c", - "metadata": {}, - "outputs": [], - "source": [ - "# load phenotype data from excel file\n", - "pt_data = pd.read_excel(os.path.join(DATA_DIR, 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'), sheet_name='Yields', index_col=None)\n", - "pt_data['EX_glc__D_e'] = -pt_data['EX_glc__D_e']" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "8ce6c775-f714-4534-91ce-9c4cfd4e73c2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_23069/3670735817.py:46: UserWarning: constrained_layout not applied because axes sizes collapsed to zero. Try making figure larger or axes decorations smaller.\n", - " fig.savefig('Figure3_sensitivities_protein-overproduction.png', dpi =200,bbox_inches='tight')\n", - "/usr/lib/python3/dist-packages/IPython/core/pylabtools.py:151: UserWarning: constrained_layout not applied because axes sizes collapsed to zero. Try making figure larger or axes decorations smaller.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#create 1 big plot\n", - "fontsize = 26\n", - "vmin =-1\n", - "vmax=1\n", - "width = 40\n", - "height = 15\n", - "cmap = None\n", - "\n", - "# gridspec inside gridspec\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "gs0 = gridspec.GridSpec(1, 14, figure=fig, wspace = 25)\n", - "gs_pam = gs0[:6]\n", - "gs_inc = gs0[7:]\n", - "\n", - "#adjust labels for better readibility\n", - "x_csc_label_pam = adjust_heatmap_labels(x_csc_top5_pam)\n", - "x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam)\n", - "\n", - "\n", - "fig_pam = make_heatmap_subfigure(results = results_pam, csc_matrix=csc_top5_pam, esc_matrix =esc_top5_pam,cbar =False,title = 'WT PAM',\n", - " x_csc=x_csc_label_pam, x_esc=x_esc_label_pam, yaxis = eGFP_RANGE, fig = fig, grdspc = gs_pam,\n", - " annotate = 'A', vmin = vmin, vmax=vmax, fontsize = fontsize)\n", - "\n", - "# adjust labels for better readibility\n", - "x_csc_label_atp = adjust_heatmap_labels(x_csc_top5_atp)\n", - "x_esc_label_atp = adjust_heatmap_labels(x_esc_top5_atp)\n", - "\n", - "\n", - "fig_inc = make_heatmap_subfigure(results = results_atp, csc_matrix=csc_top5_atp, esc_matrix =esc_top5_atp, ylabels = False,\n", - " title = 'ATP synthase efficient PAM', x_csc=x_csc_label_atp, x_esc=x_esc_label_atp, yaxis = eGFP_RANGE, \n", - " fig = fig, grdspc=gs_inc, annotate = 'B', vmin = vmin, vmax=vmax, fontsize = fontsize)\n", - "\n", - "#set common x axis title\n", - "ax_xlabel = fig.add_subplot(gs0[0, :2])\n", - "ax_xlabel.set_xticks([])\n", - "ax_xlabel.set_yticks([])\n", - "ax_xlabel.set_frame_on(False)\n", - "ax_xlabel.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - "ax_xlabel.xaxis.set_label_coords(6, -.15)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "# fig.savefig('Figure3_sensitivities_protein-overproduction.png', dpi =200,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "7dc9eac9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_23069/2688687879.py:25: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# 2 separate plots: 1 main text, 1 supplements\n", - "fontsize = 26\n", - "vmin =-1\n", - "vmax=1\n", - "width = 20\n", - "height = 13\n", - "cmap = None\n", - "\n", - "\n", - "#supplemental figure (GECKO)\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs = gridspec.GridSpec(1, 1, figure=fig)\n", - "#adjust labels for better readibility\n", - "x_csc_label_atp = adjust_heatmap_labels(x_csc_top5_atp)\n", - "x_esc_label_atp = adjust_heatmap_labels(x_esc_top5_atp)\n", - "\n", - "\n", - "fig_inc = make_heatmap_subfigure(results = results_atp, csc_matrix=csc_top5_atp, esc_matrix =esc_top5_atp, \n", - " ylabels = True, xlabels = True, x_csc=x_csc_label_atp, x_esc=x_esc_label_atp, \n", - " yaxis = eGFP_RANGE, fig = fig, grdspc=gs[0], vmin = vmin, vmax=vmax, \n", - " fontsize = fontsize, cmap = cmap)\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/SuppFigure5_sensitivities_protein-overproduction_atp.png', dpi =200,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "44a1ed1c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_23069/1010484757.py:24: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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oa2VcPos6bwAOHHEckiRJkiRphBZ4GlqSJEmSJEnSIBwRKE2hqg5kCY5yq6qFPqJRkiRJklZTWfibhWhB89snSZIkSZIkLQEmAiVJkiRJkqQlwKnBkiRJkiRJ8yGQZa7GpPFxRKAkSZIkSZK0BJgIlCRJkiRJkpYApwZLkiRJkiTNg4C7Bmus/PZJkiRJkiRJS4AjAiVJkiRJkuZDwnJHBGqM/PZJkiRJkiRJS4CJQEmSJEmSJGkJcGqwJEmSJEnSPEjcLETj5bdPkiRJkiRJWgJMBEqSJEmSJElLgFODJUmSJEmS5kWcGqyx8tsnSZIkSZIkLQEmAiVJkiRJkqQlwKnBkiRJkiRJ8yTJuEPQEuaIQEmSJEmSJGkJMBEoSZIkSZIkLQFODZYkSZIkSZoHCe4arLHy2ydJkiRJkiQtAY4IlCRJkiRJmg8Jyx0RqDEyEShpwbvh9ydx8V1uM+4whnL5uAMYgSvGHcAI3DDuACRJkhaA68YdgKRZMw0tSZIkSZIkLQGOCJQkSZIkSZoHwc1CNF5++yRJkiRJkqQlwESgJEmSJEmStAQ4NViSJEmSJGk+BJYtz7ij0BLmiEBJkiRJkiRpCTARKEmSJEmSJC0BTg2WJEmSJEmaByEsd9dgjZHfPkmSJEmSJGkJcESgJEmSJEnSfAhkmZuFaHwcEShJkiRJkiQtASYCJUmSJEmSpCXAqcGSJEmSJEnzZJmbhWiM/PZJkiRJkiRJS4CJQEmSJEmSJGkJcGqwJEmSJEnSPEji1GCNld8+SZIkSZIkaQkwEShJkiRJkiQtAU4NliRJkiRJmg+BZcuXjzsKLWGOCJQkSZIkSZKWAEcESpIkSZIkzYsQNwvRGPntkyRJkiRJkpYAE4GSJEmSJEnSEuDUYEmSJEmSpHmQQJZl3GFoCXNEoCRJkiRJkrQEmAiUJEmSJEmSlgCnBkuSJEmSJM2TZcuXjzsELWGOCJQkSZIkSZKWABOBkiRJkiRJ0hJgIlCLRpIDk9QMjgO76h7e3rsiyYpp+tkuyVVt+cPm+n0NIslRbTyHjjmOFV2f726TPD+997OXJEmSpCUjIWssX9CHFjYTgVLjRcClwHrAh6Yp+xFgHeAi4MVzG5YkSZIkSdJouFmIFqudgTOnKXNd56Kq/pHkP2mSfA9L8uSq+mxvhST7AXu2L19eVeeNKmBJkiRJ0uKWwLLljsnS+Pjt02J1VVVdMc1xXU+dg4GftNfvS7Jx98MkmwHvbl/+qKo+PsfvQZIkSZIkaWRMBEqtqirguTQjBW8J/E9PkfcBmwDXAAfMa3CSJEmSJElDMhEodamqU4C3ty+fmeRBAEkeCjylvf+mqjp11H0nuV2SQ5P8Pck17cYaH+1sXtK1Ccf+s2x/zSTPSvLtJP9Icm17/kWSNyTZYZTvZ4B40sbz8ySXJLk8ybFJnp0k8xmLJEmSJM2PkOXLF/Shhc01AqWbeivwBGBH4KNJ7gN8uH12MvCuUXeY5MHA12g2IenYhmaE4uOT7DVk+9sBXwfu0PNo8/a4J/BAYLdh+pmhw4An9dy7V3s8PMkTquqGeYxHkiRJkqRFzRGBUo+qupZm6m8BtwNOAFYAq4DnVNX1o+wvyZbAl2iSgOcDzwK2AG5NMwrxSuBzQ7S/CfAjmiTgdTRTnncFNm372Qv4IHDZrN/EzO1HkwQ8FLhrG8u9aZKhAI8B3jSP8UiSJEmStOg5IlCL1bpJ1u9XoKqu6PPsmCSHAM+mGZkH8KGqOnaEMXa8HtiAJkm3Z1Wd1PXss0mOBX4zRPvvALYCVgKPrKrv9zw/GzgiyXz+ebAC+HBVPb/r3oVJHgN8FdgbeHmSg6rq7HmMS5IkSZLmTmDZGo7J0vj47dNi9Tvg8n5Hko2maeOwruvrgNeOOsgkazIxPfbQniQgAFV1GvCBWba/IfD09uVHJ0kCdvczn9NwrwZePUkMBbyEZjTmGsBT5zEmSZIkSZIWNROB0iSSLAfe3XXrZsAz5qCrOwGdkYvf6FPu67Ns/19oYgf41CzbmAtHVdWlkz2oqr8CnYTo/aZqIMlzkxyf5PjL5yJCSZIkSZIWGROBWqy2rapMc1zSp/5LadauA/hje35zki1GHOc2Xdd/6lPuj32e9bNd1/Uw04tH7Q8DPt9mqgJVdXBV7VpVu24wurgkSZIkaQ4FsmxhH1rQ/AlKPZJsCxzYvvws8DDgKpp1/A4acXfrdV1f2afclOsZTqOTI1tZVVfPso250O+9wsT77bvOoyRJkiRJGpybhUg39WFgXeBi4MVVdV6SN9BsuvGYJHtX1Wyn6vbqToitN2Wp2SfEOrNmlydZZzVKBvZ7rzDxfmebAJUkSZKk1U7cLERj5rdP6pLkacBe7ctXVNV57fV7mJha+7/T7Ug8A2d0Xd++T7l+z/r5c9f1nWfZxlzYccDnZ/QtJUmSJEmSBjajRGCSOyW5c9cx1IjCJGv2tLfzMO1Jw0iyCU3CD+AY4GOdZ+2OugcAq4CtgDeNqNvfMjHq7ZF9yu09y/Z/ClzbXj+9X8F5tlu7o/FNtFOzO0nLn85fSJIkSZIkLW4DJwKTPBY4Efh1e/xnmxyZtaq6HnhVV5snJXnIMG1KQ3g3sBlN4uy5VVXdD6vqF8CH2pcvTHK3YTtsfwc+377cP8kde8sk2QZ40Szbv5SJ3YIPSLL7VGWHTezP0DrA2yaJIcB7gQA3AJ+Zx5gkSZIkaW4lZPnyBX1oYRsoEZhkOfBWmr+chyZp96wRxfAs4KSutt8xona1tK2bZP1pjrU7hZPsAezXvnxbVU21S+9/AX8HlgMHt78bw3oDzajAtYAfJnlmklsnuVWSJ9KMTrxwiPZfBfyNZk3Qbyd5R5K7Jtm47WePJO8DvjTk+5iJ04F/S/LxJLu0sdwT+Arw6LbM/1TV2fMYkyRJkiRJi9qgIwIfysQaZauA51XVNaMIoN284IDOS+DObVJGGsbvaDbK6Hd8DqBNCH6krfcHJhmp1lFVlwEvbF/eHXjBsIFW1d+AxwPXALekmZJ8NnBOG+PNgSd3VZnRSNyquhDYHfgTTbLxlcCvaJKLZwM/AP4DmHSq7hz5BM1IyP1pRhpfCPyCiSTgV4HXzmM8kiRJkiQteoMmAp/Wngs4vKqOH2UQVXUccDjNiECYGJklzYfXA9vTfL+fW1XX9StcVV8Bvta+fFOSLYcNoKq+C+xCM433H8B1NKP4DqFJOP6+q/jlN2lg+vb/DNwJeD5wJHABcD1NIvBY4EDgObN+A7PzZOB5wHHAZTQ7KB/XxvHYYZcekCRJkqTV0bLlyxb0oYUtPcugTV4ouYRmVFIBu1XVj0ceSHJ/4Oj25SVVtfGo+5AWqiR3oZmSD7BrVZ0wxnBWOyuSWujDB2ec3V0NXTF9kdWe2WdJkqTpHQycXZVpC+omdrnF+vW9PXYZdxhDufWXfnZCVe067jg0O9OmcpPcniYJCHAR8JM5iuWnTKyDtmGS281RP9JC9Kj2fB1w8jgDkSRJkiRJC9Mgu4R2UtUF/LR3J9VRqapVSX4K7N3eujNw6lz0Ja1ukmxcVRdN8Ww74KXty69X1bXzF5kkSZIkaVSSsGwNd97V+AySCNyk6/ofcxXIJO1vOsd9SSOTZP1ZVLuuaz3CdyfZBPgkcDxwCc3GIQ+j2al4I5o1/d48dLDTSLIug68f2nG9CUpJkiRJklZvgyQCN+q6PneO4ug4b4p+pdXdbJZ4ewPNJh3QbJTzKCamAPe6Hti/qn4zi35m6vfANjOs8wmaHYAlSZIkSf3E5RU1PoMkArtH+WwwV4G01uu67rtzq7TIvAf4O/AgYCtgM5rk399odvl9X7vzryRJkiRJ0qwMkgjsHqW32VwFMkn7589xX9LI1JA7ZlXVScBJIwpnKFW1YtwxSJIkSZKk0RskEdidkLvbXAUySfvnTVlKkiRJkiRpoXGzEI3ZIBsC/Jpmx+AAOyXZai4CadvduX1ZwIlz0Y8kSZIkSZK0FE2bCKyq82mSgR0vnKNYOu0W8JuqckSgJEmSJEmSNCKDTA0G+DbNtN0AL0zy0ar6y6iCSLIdTSKw2lvfHFXbkiRJkiRJq4VAlg8yOVOaG4N++94PXE6TqFsL+E6SW44igLad77TtBrgC+MAo2pYkSZIkSZLUGCgRWFUXAu+hSdQVsD3w0yT3HabzJPcGfty2R9v2e9v+JEmSJEmSJI3IoFODAd4OPBi4L03CbjvgmCSfAg6uqp8P2lCbADwAeBpNMrIzJfhY4K0ziEmSJEmSJGlBCGHZcncN1vgMnAisqmuT7E2TrNueJnm3DHgG8IwkZwDHAScAZwOXAlcC6wEbArcG7g7cE1jRNtsZYRjgz8DeVXXd0O9KkiRJkiRJ0o3MZEQgVXVRkvsDhwEPYmIkX2iSe9sAj5+mmXSaYyIJeDTwFKcES5IkSZIkSXNjRolAgKo6N8mewGuAVwIbMJEQhIlE36TVuXHy8ArgncBbq2rVTGORJEmSJElaMNw1WGM2q29fNd4CbAW8GjiNJrHXLwlIV5nTaRKJW1XVm00CSpIkSZIkSXNrxiMCu1XVZcA7gHck2QK4P3AXYBNgY5rRglcAFwEXACcBx1TVWcP0K0mSJEmStBBl2XRjqKS5M1QisFtV/R34XHtIkiRJkiRJWo04MV2SJEmSJElaAkY2IlCSJEmSJEl9BHBqsMbIEYGSJEmSJEnSEjBvIwKTPADYB7g7sClwHXAu8AvgK1V14nzFIkmSJEmSJC01AyUCkywHdui6dWFVnTtg3S2Az9DsKPzP2+25gAcD/53kC8ALqurCQdqVJEmSJElaWEKWOzVY4zPo1OC9gN92HfcbpFKSbYBf0iQBw40TgNUp1h5PAI5MsumAMUmSJEmSJEka0KCJwIcykbA7HfjKdBWSLGvLbd7e6iT/0nPQdf+OwMEDxiRJkiRJkiRpQIOuEfiA9lzAJ6uq+hVuPQu4Czce+Xch8B7gh8DFwG2AfYEDgOVtmUcn2b2qjhwwNkmSJEmSpNVeAnHXYI3RtInAJGsDd+i6dfiAbb+4uxngbOB+VXVG1/1TgaOTfBH4DrBWe//ZgIlASQPZ9I635Zlffvu4w9CqVeOOYHi1ctwRDG/VDeOOYDRWXT/uCIa3apB/N13NrVoEvxOXXjruCIZWV1897hCGd9VieA9XjTuC4V1y2bgjGInrz7983CEM7aqzFvZ7+OrXjh13CJJmaZARgTu25Qo4p6p+P12FJHcDdmJiym8Bz+9JAv5TVR2d5I3AW9tbeydZVlWL4G+VkiRJkiRJDTcL0TgNskbgbbuufzlgu3v2vD61qr4+TZ0PAte01+tw41GIkiRJkiRJkoYwSCLw1l3Xk47om0RnTcHOaMBppxNX1eXAz7pu7TxgX5IkSZIkSZKmMcjU4PW7ri8asN17MzEtGOCoAev9Hti9vd54wDqSJEmSJEmrPzcL0ZgNMiKw+xt6s2kLJ9tz0yTeoFOKL+66vvmAdSRJkiRJkiRNY5BE4CVd15sPUP4+Pa//UlWDbk+1fMBykiRJkiRJkmZgkKnB53Rd332A8rt1XRfw8xnEs1nX9cLeT12SJEmSJKmXU4M1RoOMCDy+PQe4U5IdpyqYZE3gUdx4fcCjZxDPiq7rc6YqJEmSJEmSJGlmpk0EVtVZwKk0yT2A9yaZKn39LGDTrtcrgW8OEkiSZTSbjHScOkg9SZIkSZIkSdMbZEQgwMFMjPB7CHB4klt3F0jyOOBdTIwGLOCIqjpvwD7uDmzQXl8P/GnAepIkSZIkSau/hCxb2Mfs3na2TPL6JL9Mcl6Sa5KckeQbSZ6eZJCl6wbt68AkNcNjy1H1v7ob9IP+CPDvwDbt68cAj05yCnAZsC3NRiKdBGDHm2YQyzPacwG/rqprZ1BXkiRJkiRJq5kk+wMfYGLwV8fW7fFI4D+SPKmq/jzP4S05AyUCq+rKJE8Hvges095eDtyRG68HWF3nD1fVLwZpP8nawFO66h8zSD1JkiRJkqSFJMuXzmYhSfYDPsZE3uivwNeAi4EdaQaarUMzS/QHSe5bVWePMITPM7H3RT+XjLDP1drAQy+r6qdJHgZ8AbgVNx7517nu/GA/Dbx4BnEcANyiq51vzKCuJEmSJEmSViNJtqGZYdrJFX0IeFFVrewqswL4LrADzSzUjwB7jzCM71bVoSNsb8EbdI1AAKrqx8DtgNcBv2lvpz2upvnhPaqqnlFVNwzSZpJ1gFd3tXU+8NOZxCVJkiRJkqTVyhuBtdvrnwAv7E4CAlTV6cC+NHtFADwqyQPmLcIlaMaLMVbVFcCbgTcnWU6zS/D1wMVVVX0rT97e1Um2uPGtmbcjSZIkSZK0Wgswyw03FpIk69Ik+DreVFWrJitbVScn+RLwpPbWfrhk3JyZ0YjAXlW1sqrOraqLhknete10jkm/GJIkSZIkSVoQ9gLWba8vBn4wTfnDu673SbL4s6VjMlQiUJIkSZIkSepxt67rnw0w6OvHXdcbAytGFMdzkpyU5LIk1yb5R5IfJ3lLku1H1MeCMqOpwUnuxMQijwC/H3QtwCnaWxPYqevWyqr63WzbkyRJkiRJWl2FJbNr8M5d16dOV7iqzk9yKbBhe+uOwGkjiOO+Pa83b49/AV6V5P+Al1TV1SPoa0EYOBGY5LHceKjm56rqqcN0XlXXJ3kV8MSufh5WVUcM064kSZIkSZLG5tZd12cNWOcsJhKBm48ghgtoNin5A3ApsB5wB2BP4OY0s2QPAO6SZPequmoEfa72BkoEtpuCvJWJ0YC/Ap41ohieRTMqcJf29TsAE4GSJEmSJEkL0wZd11cOWKc7EbfBlKWm9wuaZN+PJpuSnOTmwJuAF7W37gV8CNh/iD4XjEHXCHwocPv2ehXwvKq6ZhQBtMMvD+i8BO6cZI9RtC1JkiRJkrQ6SRb2AWya5Piu47mTvM11uq6vG/Cj6c4zrTtlqWlU1Xeq6od9dim+rKr+g2bAW8cz2uXw5lSSrdtj/VnUXa9Tf5gYBk0EPq09F3B4VR0/TKe9quo4mmnHnRGH+42yfUmSJEmSJI3EBVW1a9dx8CRlutfcu9mA7a7ddT0f03TfAJzRXgd4wjz0eTrN2oeTJU+ns39b96/DBDBoIvBhXdcfGqbDPv63PQd45Bz1IUmSJEmSNB4JWbawjwFd3nW93oB1ukcBXj5lqRGpquuAr3bd6t1YZHUUbryJ74xNmwhMcnuaRRQBLqJZaHEu/BS4sL3eMMnt5qgfSZIkSZIkzZ1zuq63GLDOllPUn0vdOxqPYoOS1d4gIwI7m3gU8NOqqrkIpJ27/dOuW3eei34kSZIkSZI0p37XdX37KUu1kmzKxI7BvfXnUk1xvTrqTLG+dphGBtk1eJOu638M09kAutvfdI77kiRJkiRJml/LB12lbUE7oev6PkkyzcCy+3ddX0yzFt582L7r+rx56nO2dmzPFw3TyCDfvo26rs8dprMBdH/oG01VSJIkSZIkSautI5jYMGRjYI9pyu/bdf3VuZqN2i3JmsBjum4dO9d9zlQamyZ5EvBUmlGLJw/T5iCJwO4hhxsM09kAuheQHHR7aUmSJEmSJK0mqupK4Itdt16bZNJNLpLcAXh8161PzGVsXf4bWNH1+vBRNp7k9UlWdh+dR8C7ep9NdgA30AzK+wwTm6l8aZi4BkkEdo/S22yYzgbQ3f75c9yXJEmSJEnS/Alj3/V3nnYNBngdE4PLHgC8P8nyG30cyTY0CcM121vfqqqjJ/3oktOTVHscOEWZVyR5T5IdpvwRJBskeXcbX8dnq+rXA72rmUnPMdX9QQ6AHwCHDBPQIGsEdifk7jZMZwPobn91n5utLu0v4esneXQDzfz+3wFfA/5fVV0xj6HNuyQrmFjP4EFVddT4olmYkhwFPBD4RFXtP95oJEmSJEkzVVWnJ3k+E4mrFwIPT/I1mjzBDsBjmRjp9jfgeUN2ux7wEuAlSf5Is1bh6cDlbT87AQ/mxhuTnAA8d8h+J3MJcEbPvW1opvdeAlw2QBvXA5cCpwDfBL7YbrY7a4MkAn9NE2SAnZJsVVV/G6bTySTZCti5fVnAiaPuQ2OxBs1Iz93a4wVJHlZVp/arNBeS7A98HKCqZvTPGJIkSZIkaWaq6mPtlOD3AesD2wEvnaTor4EnVtVZI+x+h/aYykrgYODlVXXVCPsFoKreD7y/+16SThLvLVX1nlH3OYhpE4FVdX6SXzMxWu+FwCvnIJYXdroEflNVjghcuHYGzmyv1wRuR/OdeRzNL/3Xkty5qm4YU3ySJEmSJI1Fli+tcSlVdUiSI4BnA4+kGRW3Ac3adycBXwAOG1GO4P3AL4F7AfcAtgI2pdmw5DomZiz+hGYG2sgHug1grF+AQUYEAnybJhEY4IVJPlpVfxlVEEm2o0kEdnaF+eao2tZYXNUz/fc4YN8k3wf2pBmK+1iaX/ZFp6pOZ8y/2JIkSZIkrS7ahNvrmXxJsUHbWDFAmYuBb7XHaqeqBtmrY04NGsD7aeZTF7AW8J0ktxxFAG0732nbDXAF8IFRtK3Vzpu7rqfbOlySJEmSpMVlNltErG6HFrSBEoFVdSHwHpofeQHbAz9Nct9hOk9yb+DHbXu0bb+37U+Lz++6rrfsXCQ5sN315/T29S5JPpnkzCTXJTmxu5EkGyZ5XZITklya5Ookf01ySJKd6ZFkRZKiXR+wvVc9x1GTBZxkjySHtbFck+SSJD9P8uIka01RZ0VXu7tN8ryz09GB7esnJDk6ycVJrkry6yQv6t1NaVC9/SdZP8kbkvy+bf+CJN+cLLZJ2to+yYeS/Kmte3mSk5K8NcmmA9S/W5JD2/fc+fyOS/LqJOvP5v1JkiRJkqTZGXRqMMDbaXZWuS9Nwm474JgknwIOrqqfD9pQmwA8AHgaTTKyMyX4WOCtM4hJC8vKrutJ/x0hyeOAz9CMEJ3s+V1oRpBu3vNo2/bYL8lLquqgYQJNcjOanY2e1vNoLeDe7fHMJA+tqn8M0c+HuemuSHehGYV7L+Cps227tTHwC+AOXffWAR5Bs1vTK6vqf6aI7ek0n8GaPY/u1B7PS7J3Vf1kivqvBt7CjX/Wa9Gs03CPtv5eVfWHmb8tSZIkSZIWtiTrAnemyXGsz+AD9j452z4HTgRW1bVJ9qZJ1m1Pk7xbBjwDeEaSM2jWgjsBOJtme+MrabZu3hC4NXB34J7AirbZzgjDAH8G9q6q62b7ZrTa26nr+uxJnt8COJRmW+zX0SSw1gLuCJBkM+C7wK1opqq/DvgqzffsXsDb2rIfSHJWVX2lbfcMmoVInwp8pL23QU/fK3te/x9NEvAG4CCa5ORpNL+YD6VJcN0Z+FKS+1dVb/1BPIPmd+EDNAm3s9rX76BZS/EpSQ6rqmHWNng3ze/efwGfo/nc7tn2sTPwriS/rarvdVdK8kCan8Uymt/NV9OM3r0Z8HCahP3GwLfajV/O6Kn/DCaS+ie0/f+K5nN/PM26EFsD32vrXzrEe5QkSZKkBSGELB/7MnEasyT3oMlp7AXMdDZgAXOfCASoqouS3B84DHgQEyP5QpPA2IbmL/n9dEYHFRNJwKOBpzgleNF7Vdf1UZM8vznwe+D+PZuNdHbxeR1NEnAV8Iiq+nFXmW8m+QlN8vD2wEFJvlFVN1RVAVckubZTuKf9G0nyEJokHcD+VfWZrscXAQe3fR0P3AfYF/j8VO31sS3wip4ReRe1Cfc/0Uyf3o/hFjldQbMFe/fGLN9K8nOaxNw2NNP+e6dUH0STBDwLuF/PLt4fTfILmn8UuDlNUvFJnYftlOl3ty9PBB7QtRX7+cA7kvy2fV9b0yQZu78bkiRJkiQtSkmeA3yI5u/c877q4owSgQBVdW6SPYHXAK+kGeFTXUX6vYlO8q9T7grgncBbq2rVTGPR6i/JGsDtgFcAj2pvnwEcPkWV102WpGvb6STnPtuTBASgqi5J8irgy8AWNJn12STRXtiev9eTBOzu6/dJDgOeBTyF2SUCz6BJwvW2fXWSLwIvpplCO4yf9yQBO31clOQNwMeAOyS5R1X9Ev75LxN3aou+oScJ2Kl/YpKPAi8CHpdko6q6pH28N8327NAkOq+apP63k3y9LfuvSV7dJmwlSZIkSVqUktwJ+DATU4CvAY4E/sjEJr1zasaJQID2L+xvSXIQ8G/Ac4DbDlC1kyQ8jWbq5YedErgonZZMmQ8+B9inqq6d5FkB35vkPjSJqZu311/q0/c3gatp1sG7PzNMBLYbdDywfXn0NBtanNye7z6TPrr8oE8C/NT2fKtZtt3x1T7PvkKTCAS4H/DL9vpfusp8uU/9w2kSgWvQrJn43Z76lwA/nKb+3sBmwA7AjNYKTPJc4LkAW99m2n1LJEmSJGn8Ajg1eCl7CRN7ZXwLeOZ8z46dVSKwo6ouo5kW+I4kW9AkXu4CbEKzftgGNKP+LgIuAE4Cjqmqs4bpVwvOtTRTfr8OHNTnS35+nym723Rd/36qjqrq+iSn0qzft/UsYr01E+sHvpXBNq/ZbBb9APTbZKQzim6dWbbdMWVyrR1BeS5NsrH78+1cn1NVF/Vpu3sX6O7PulP/D9OM8uutP6NEYFUdDBwMsOudtnM0oSRJkiRpdbdbez4T2Hcc+2QMlQjsVlV/p9mM4HOjalML1s40X2qAG6rqmgHrXd3nWffIvCnX92td3p57NwQZxIazqHOzWdSBm25QMheunOb5FTSJwO7Pd/2uZ/1c3nXd/VkPW1+SJEmSpMVoc5rRgF8Z12a5I0sESl2u6rcZxyx1t9dvum7388v7lppcd+LsMVX11Vm0sTpZb5rnkyXtruh5Nl1duPFnPWx9SZIkSVqkAstmukmsFpGrgLWAc8cVgBPTtVCc3nW901SF2k1Fbt++PGMW/ZxNs1gnwF1nUX91s+NUD5JsxMQahN2f1entefMkt+jTdvdOw5PV3zF9FovsU1+SJEmSpMXor+15tkuMDc1EoBaK3wKdjWUe16fcI5hYV+8nPc+u71y0m4LcRDs095j25VPbxOJCts+Az37add39uT22T/192/MNwC8mqb8RsPsA9c8H/tSnnCRJkiRJi8GXaLaM6fd35TllIlALQlWtBD7Zvnxykvv2lklyc5rNawDO4qY7EHdvUnLrPt29tz1vB7y736i2JGsl2Waq56uB+yR5Qu/NJBsDr29f/r6qOjsGU1XH0yReAV6f5CZb8ia5M82O4QBfqqpLuh5/gya5B/DOJDfZ8CTJXkwkIj82zaYikiRJkrQ4BFi+fGEfGsbBNDMRd0ny+HEEYCJQC8mbaObRLwe+k+RFSbZJslmShwM/BnZoy76oqm7oqX8isKq9PjDJVknWTLJG9wjBqvou8PFOO8APkjw6yRZJNkqyIsnDk7yXZkrrWH55B3Q68Mkkr06ybZJN28/qGGBFW+alk9R7Ic1ntRXw0ySPSXLLJFsmeQ5wJM26BpcBr+quWFXXAi9rX94NOCrJQ9q+t03yCuDL7fMzgbeN6s1KkiRJkrS6qqqLgSfSrK3/sSRPnO8YFvq0Ry0hVXV+kocC36HZaef97dFtJfCSqvrKJPXPSXI4zS/ds9qj42gmtvEGOIBmF+Pn0wzZ7Tds99qZvZN59TLgzcBb26NbAa+sqt6Rk1TV0UmeCfw/mjUXv9xbBrgY2LuqTp+k/qeSbAm8BbgnNx2dCU0ScK+qunSSZ5IkSZK0CAWWOyZrqUryjPby/2gG5RyW5NXAt2kGGl09SDtV9cnpS03ORKAWlKo6McmOwIuBRwPbAzejGVr7I+C9VXVynyb2B06hWftue2DdKfq5Hvj3JIcAz6VJEm4BrA1cApwK/JBmWuyJw72rOXURcC+aUXuPA7ah2aXoWOBdVXXUVBWr6pNJfk7zh9OeNO9/Fc3ipt8E3lNVF/Sp/7YkR9CMqnwgTfL2Wpr1AL8MHDQHu0tLkiRJkrS6OpRmUA7tOcCd2mNQxcTSaTMWl+aSFpckK4DT2pcP6pfsWyx2vdN2ddyX3z7uMLRq1fRlVne1ctwRDG9V76oIC9Sq66cvs7pbtQj+H2vVIviduHThDzyvqwcaHLB6u2oxvIerxh3B8C65bNwRjMT1518+7hCGdtVZC/s97P61YznxgsumXEtdU9t1m83qF/+1z7jDGMoaB/y/E6pq13HHsRAlGcVfmqqqZr1YoyMCJUmSJEmS5kOAZU4NXsLeMO4ATARKkiRJkiRJc6yqxp4INA0tSZIkSZIkLQGOCJQkSZIkSZoXgeWzXt5NGpojAiVJkiRJkqQlwBGB0iJTVafTLEErSZIkSZJWU0m2BB4B3AO4JbAe8JGqOryn3Mbt5bVVdeUwfZoIlCRJkiRJmg/uGiwgyfrA+4CnM5GbC1DAtyap8gNgF+BPwE7D9O23T5IkSZIkSZoHSTYFjgOeCaxJkwCcblbf+9syt09y92H6d0SgJEmSJEnSfIibhYgvADu21ycA7wVOBE7uU+crwEdpEocPbevNiolASZIkSZIkaY4leQSwG80U4C8BT6mqG9pnU9arqsuSHA/cpz1mzanBkiRJkiRJ0tx7cnu+BPjXThJwQL+hmR68wzABOCJQkiRJkiRpnsSpwUvZfWhGA369qq6YYd3z2vMthwnAEYGSJEmSJEnS3LtVez51FnWvac/rDBOAiUBJkiRJkiRp7lV7nk0+bpP2fOkwATg1WJIkSZIkaT4ksGzqTSG06J0PbNMeM7Vre/7HMAE4IlCSJEmSJEmae7+i2fDjIem3TXCPJNsC96cZUfizYQIwEShJkiRJkiTNvW+25y2BAwapkGQ5cDDQ2WXma8ME4NRgSZIkSZKk+eKuwUvZZ4DXA1sD70tydVV9YqrCSW5LkwTcnWY04G+r6jvDBOCIQEmSJEmSJGmOVdX1wLOAlcCawMeSnJzk3V3F7pXktUmOAP4APKi9fy2w/7AxOCJQkiRJkiRpPiSwzDFZS1lVHZnk6cAhwLrATu3R2VF43/aAZj1BgCuBp1TVicP277dPkiRJkiRJmidV9XngHkysGZgpDoDvAveuqm+Mom9HBEqSJEmSJEnzqKpOAfZOsjWwB7ALsAlNru5C4BTgiKo6dZT9mgiUJEmSJEmaL24Woi5VdSbw8fnqz6nBkiRJkiRJ0hJgIlCSJEmSJElaApwaLEmSJEmSNB8SpwZrrEwESpIkSZIkSSOS5HXdr6vqjZPdn61Oe7NhIlCSJEmSJEkanQOB6nr9xinuz5aJQElL2Fq3YNntHj/uKIby1ZP+Me4Qhnb1DSvHHcLQFsN7uOTqhf8eAM6/8vpxhzC0i6+6YdwhDO2SK68bdwhD+80fzht3CEP7++nnjjuEoV1/xinjDmF4V68adwQjsP64AxiRxfA+bj3uAIZy7VUnjjuEhW252zUsEWnPvYm/9BacoaESiSYCJUmSJEmSpNF55gzvzxsTgZIkSZIkSfMhgWWOCFzsquoTM7k/n/z2SZIkSZIkSUuAiUBJkiRJkiRpCXBqsCRJkiRJ0nxxarDGyESgJEmSJEmSNMeSLAfeDiwHflFVnx+w3pOAewLXAq+pqlnvHGwiUJIkSZIkSZp7jwBeBhTw4BnUuwB4cVvvaOC7sw3ARKAkSZIkSdJ8CE4NXtoe0Z7PraojZ1Dvh8C5wC2BRzFEItBvnyRJkiRJkjT37kEzqu/HM6nUTgU+hiaVfK9hAjARKEmSJEmSJM29bdvzH2dRt1Nn276lpuHUYEmSJEmSpHkRpwYvbeu15ytnUbdTZ4NhAvDbJ0mSJEmSJM29y9rzxrOo26lz1TABOCJQkiRJkiRpvsQxWUvYP4BbMLt1/jp1zhkmAL99kiRJkiRJ0tz7Kc2GH/dLstOglZLcAfgXmo1GfjZMACYCJUmSJEmSpLn35fa8DPhUkvWnq5BkPeBTTOTwvtyn+LRMBEqSJEmSJM2XLPBDs1ZVRwC/bF/eFTg+yV5TlU/yUOAE4C40owFPrKpvDhODawRKkiRJkiRJ8+NpwC+ADYHbAd9Oci5NgvDctsytgHu0Z2hSsJcCTxm2cxOBkiRJkiRJ0jyoqlPbkX5fBm7T3t4ceOQkxTtjMP8O7FtVfxy2fxOBkiRJkiRJ8yGBZa7SttRV1XFJdgH+E3gucHMmn3h9KXAw8M6qunAUfZsIlCRJkiRJkuZRm9h7ZZJX00wDvgOwCU1C8ELgZOD4qlo5yn5NBEqSJEmSJElj0Cb6jm2POWciUJIkSZIkab7EqcEaH799kiRJkiRJ0hLgiEBJkiRJkqT5smyyPSGk+WEiUJIkSZIkSRqRJEd2vayq2mOS+7P1z/Zmw0SgJEmSJEmSNDq7AUWzA3BNcn+2etubMROBkiRJkiRJ8yFxs5ClY6o54GOdG24iUJIkSZIkSRqdbWd4f96YCJQkSZIkSZJGpKrOmMn9+eR4VC1pSbZO8uokP0hyZpKrklyd5OwkP0zy1iR361N//yQ14HFgT90Dpyh3fZLzkvwoyYuTrD/nH8QIdX8m445FkiRJklY7ycI+NK0kL2qPu4w7ll6OCNSSlORmwNuAFwA3m6TIrdtjd+DVSX4OvKCqfjUP4a0BbEaziOhuwAuSPKyqTp2HviVJkiRJ0nDeR7OpxyuAEzs3k3ysvfxcVR0x/2GZCNQS1I6w+xbwgPbWX4EPA0cBZwPXA5sD9wEeBzykvX4s0C8R+HDgx32eX9fn2c7Ame31msDtgFe2/W8HfC3Jnavqhj5tSJIkSZKk1df+NAnCkwETgdI8+SgTScAPAS+uqut7ypwP/BY4OMl92zrTubqqrphlTFf11D0O2DfJ94E9gZ1oEpFfmGX786aqDgUOHXMYkiRJkrR6Wub02iVgJc1yfGuOO5BerhGoJSXJXsBT2pdfrqp/nyQJeCNV9TPg3sD35jq+Sby563qPMfQvSZIkSZJm5tL2vMVYo5iEiUAtWEnWaTfTOCbJBUmuazf5ODzJA6eo9or2vBL4j0H7qqorq6rftN+58ruu6y2HbaxrQ5L9kyxL8u9Jjk9yWfsZfjfJfXrqPDjJt5Oc026kcmKS5/TpY8rNQpKs6IphtyRrJfnPJCcluTLJJe0mLQ8b9r1KkiRJkjQmpwABnpDk9uMOpptTg7UgJbkD8E1g255Htwb2pZlW+66qemVXnQ1oNt8A+H5VnTUfsQ5pZdf1KMePrwl8G9ir5/5ewIOSPLqqvpvktcAbe8rsQjNletuqes0QMWwAHAPcs+f+7sDuSQ6oqoOHaF+SJEmSVj9xTNYS8A3gfjQbgZ6S5Dzg6q7nr0nyglm2XVW13WwD89unBSfJ5sCPaJKAZwDPodlQY2Pgrkys5/eKJM/vqnofYHl7/dP5iXZoO3Vdnz3Cdl9DkxT9L5qNSTYFHgWcRbOL8keT7EuTBPw4cHdgE2BX4CdtG/+ZZOchYng/sCPwEuC2bQwPp9m8BeC9SW45RPuSJEmSJI3DQUyMCgxwS2Cb9lmAW7SvZ3qsaI9Zc0SgFqL30PwSnQ3cs6rO63p2MfC8JOcArwfelOTQqrqKG/+y/HEO4lqn3ZF4MjdU1TWzaPNVXddHzaL+VFYAj6mqr3bd+2aSq4AfAlsDnwXeU1Uv6ypzUZJHA6cBNwee3hPjTGwF3L+qju26950k+wAnAevSjO780CzblyRJkqTVSwLLHJO12FXV1UnuRzP4Zm+av2OvRbNjcCc5OBZ++7SgJLkV8Pj25ct6koDd3g5cQTNKsDP99RZdzy/r08daSdaf7JgmvG8Dl09xfG6aut39r5FkpyQfoxmlB83Ix8MHbWMAx/QkAQGoqiNpdkwGuIYmmdpb5iLg++3L3mm9M/G5niRgp/3fAie2L+8xRPuSJEmSJI1FVV1SVa+oqh2qap2qWkaTACzg5VW1bJbH8mm67stEoBaaB9CMZC3g530SdmswMerv7u25O+N+k40suhzC1Am9uXJa1wYb1wO/B57ZPjsH2Keqrh1hf0f0i6U9H1tVV0xR5i/tefMhYui3C/Op7flWUxVI8tx2o5Pjzz///KmKSZIkSZKkllODtdDs0J4DnD5gnc3a80Vd9zYcVUBdHlRVR42orWtpkoFfBw6qqgtH1G7HOX2eXT2DMusMEcM/+jy7arr2241EDgbYdddd+yV2JUmSJGn1sWxss0I1fmfSDEy6dFwBmAjUQjObBN5a7fn0rns7TFIOgKp6GvC0zuskrwLeNot+Z2Jnmj8QYPbrCc7EyumLDFRmmP+CzXX7kiRJkiTNuyQXA6uA11ZV97r3D6RJBF40acV5YCJQC82V7fnSqtpohnWPpUk+LafZxnt1clWfabiSJEmSJGnh2JAm4bd2z/3TaBKEr6TZCHXemQjUQvPX9rxhkm2r6rS+pbtU1WVJjgL2AB6cZMuqOmsugpQkSZIkaVJxu4Ylbqwz3/z2aaE5kiZ7DrD/LOq/qz0vB94/ioAkSZIkSZK6dNbV32icQUzGRKAWlHYE3+Hty1cmuW+/8km2SdJZI5Cq+h7w2fblY5N8MMmacxOtJEmSJElags5uz/caaxSTcGqwFqIXA/cHbgMcmeQgmuRgZ9rw5sCuwD7AI4Bb0+zC2/FcYMu2jecDD0vyYeAo4CyaHWs3BHYEntAe0MzvlyRJkiRpdpLm0GL3c2A7YM8khwFHMzFKEGDXJM+YbeNV9cnZ1jURqAWnqs5JshvwFZrddl/eHpNZSc/utFV1RZI9gXfQJAK3Bd45Tbc/B14y+6glSZIkSdIS8SHgqe31E9ujI5Pcm4kCTARqaamqU5PcBXgKsC/NCMBNaNYPPAf4LfB14KtVdfEk9a8DXpLkvcDTgT2B27VtFHAx8CeanYY/X1UnzvFbkiRJkiQtBY4IXPSq6tgk/w68G1hnkiJj+xKYCNSCVVU30GTBZ50Jr6ozgbe0x2zqHwocOsu6BwIHzqbubFXVtH/YVNVuA5Q5kCli7/eZVNXpDPAHXlXtz+w2g5EkSZIkaeyq6iNJvgg8GNgaWBt4Pc3gox8APxtHXCYCJUmSJEmSpBGrqguY2LCUJK9vL79XVe8ZR0wmAiVJkiRJkubLsmXjjkBLmIlAaQFJshaw5gyrraqqq+YiHkmSJEmSNLBntudfjisAE4HSwvJRYL8Z1jkDWDH6UCRJkiRJ0qCq6hPjjsFEoCRJkiRJ0rwIxKnBuqkkGwPrARdX1RVz1Y/fPmkBqar9qyozPFaMO25JkiRJkjQhyc2TvCrJz5NcC5wPnA48d5KyL0zy0iSPHbZfRwRKkiRJkiRJ8yTJQ4FPARt3brXnmqLKPYCnApcn+XZVXTPbvk0ESpIkSZIkzYcAy5aPOwqNUZK9gK8Dy2m+EVcCv6dJ9k3lYOBpwAbAXsDXZtu/U4MlSZIkSZKkOZZkA+DTNAPzbgBeBmxSVfeapupPgfPa6z2HicFEoCRJkiRJ0rzIIjg0hOcCm9BMAX5OVb23qq6brlJVFXAczQ/grsMEYCJQkiRJkiRJmnsPb88nV9UnZ1j3D+35tsMEYCJQkiRJkiRJmnt3oBkN+P1Z1L24PW80TABuFiJJkiRJkjRf3CxkKevsEnxe31KTG8kXxxGBkiRJkiRJ0ty7vD1vMIu6W7Xni4YJwESgJEmSJEmSNPf+3p7vPIu6D6KZVnzqMAE4NViSJEmSJGleBOKYrCXsaOBOwIOTbFZV5w9SKcljgO1pEoFHDROA3z5JkiRJkiRp7h3WntcCPpok01VIsjPw0fblSmCmuw3fiIlASZIkSZIkaY5V1bHA14EAjwaOSHL3ycom2SLJ64CfA5vSjAY8tKpOGyYGpwZLkiRJkiTNhwBx1+Al7pnAL2im+u4OHJfk4q7n/57k34EV7evOqMGTgf8YtnNHBEqSJEmSJEnzoKouBv6FZq2/tMfGNCP+oEkAruh6Rlv2QVV19bD9OyJQkiRJkiRpXrhZiKCqzgN2T/JY4N+B+9KsG9htJc3IwQ9U1RdG1beJQEmSJEmSJGmeVdWXgS8nWQvYCdiEJld3IfCnqrps1H2aCJQkSZIkSZLGpKquBU6cj75MBEpaUJLUFPfnOxRJkiRpyer9//Kq8n/IB7XMzUI0Pk5MlyRJkiRJksYsyTpJ1p7LPkwESpIkSZIkSfMsyd2S/G+S3yS5GrgCuDLJ1UlOTHJQkruOsk+nBkuSJEmSJM0bZ1EvdUluARwCPLr7dtf1WsCd2uP5Sb4KPLuqLh62b0cESpIkSZIkSfMgyWbAL2iSgOk6blK069gHODbJpsP2byJQkiRJkiRJmh+fA7anSfCtal/vA2wNrAOs3V7vA3wWWNnWu11bdihODZYkSZIkSZoPCXHX4CUryUOBBwEFnAc8uqqOm6ToWe3x9STvA74GbA48KMlDq+q7s43BEYGSJEmSJEnS3Htie14F7D1FEvBGquqXNNOIV7W3njRMAI4IlCRJkiRJmheBOCJwCbsfzWjAH7QJvoFU1S+THAE8FLjvMAE4IlCSJEmSJEmae5u354GTgF06dTbvW2oajgiUtKBU1U12U9p1l53quO99fBzhSJJWd6uuH3cEkkatVk5fRnPqng8/gON/88fJdjmV1F9nOOhs/gflhp42ZsVEoCRJkiRJ0nyJkzOXsPOBrYCdZ1H3jl1tzJrfPkmSJEmSJGnuHQ8EeGSSrQatlGRr4JE06wueMEwAJgIlSZIkSZKkuffV9rwO8OUkm0xXoS3z5bYOwFeGCcBEoCRJkiRJ0rxodw1eyIeG8VngT+313YBTkrysHfF3I0m2TvJy4BTgrjSjAf8IHDZMAK4RKEmSJEmSJM2xqlqZ5PHAMcDNgU2BdwLvTHIJcAFNwm8zYKO2WmdjnkuBx1fVqmFicESgJEmSJEmSNA+q6rfAbkyMDEx73ALYHrhde925D/AH4IFV9bth+3dEoCRJkiRJ0nxJpi+jRa2qfpNkF+DpwH7APYC1mEj8AVwL/BL4OPCZqrpuFH2bCJQkSZIkSZLmUZvYOwQ4JMnNgBU0IwEBLgLOGFXyr5uJQEmSJEmSpPkQ3HBDN9Em/P40bcERcI1ASZIkSZIkaQlwRKAkSZIkSZI0B5LcD+gMA/1lVV09g7rrAru2L2+oqp8NG8+iGxGYZJ0kj0nykSQnJLkkyfVJzk/ywyTPT7LOPMSxf5JKUnPQ9m6dtpOsGHX7o9IV4/7jjkWSJEmSpPFLMzV4IR8aWJJHAccAPwJeOZMkIEBVXQW8sq3/4yR7DhvToksEAucCXwYOAO4GbEgz8nFTYHfgg8CvktxubBFKM5Dk9DaheuC4Y+lnocQpSZIkSdI8eSPNypCX0uwOPBvPBC5r23nTsAEtxkTgBjRbLH8WeBKwHbAxcBfgw0ABOwJHJFl/TDFKkiRJkiRpkUpyZ2AXmjzUQVV14WzaqarzgYPal/dMstMwcS3GROAHgW2q6ilV9fmq+mtVXVxVv6mq5wOvbsutAJ4/tiglSZIkSdLSs2zZwj40qMe25wL+d8i2DmrbAdh3mIYW3U+wql5QVef2KfJuoJOFfdg8hCRJkiRJkqSl5Z7t+dftqL5Za+v/qn15r2HaWnSJwOlU1Q3Aqe3L2wzTVpLHJTmq3ZDk8iS/TvKyJGvOoI09khyW5Mwk17Rt/TzJi5OsNWAbN0/yliR/THJ1kguTfCPJvScpe++uTTweOE27/9KvbJLbJvl4kr+3sZ+R5P+SbDdAzDdaT67dXOXoJBe091/cU/5uSQ5t63U+p+OSvLrfFO/uftJ4Vvv5dn5mxyZ5dpL0aeNOSf47yTHtpjPXJ7k4yS/a+xv1qXtg2//p7esd2s/srCTXJvlb+5ltOUndQ9vNZrZpb72+6+fROXabpN5sP6sNkjw+yafa79JVbf0zk3x+sr6GjHOjJK9tY7uo/TzOSPLJJHeZKk5JkiRJkhaAO9CM4vvliNr7Jc06gTsP08gao4llwblVe75stg0k+RDwbz2379IejwIOm6b+zYBDgKf1PFoLuHd7PDPJQ6vqH32a2gL4PrB91721gUcCeyXZp6q+3XlQVccm+T3NF3I/4Og+bXcWsjyNZpeb7vh3B74BrNt1e2vg2cDjk+zVp92epvIF4PF9CrwaeAvNF75jLeAe7fG8JHtV1R+m6eswmnUju92rPR6e5Altori7712AEydpayOa7P49gWcleUhVnTpJue629gS+AnQn47ak+cwekeQ+VXXGNO+hryE/q08C+0xyf6v2eEKSt1bVfw0TYxvn/Wk29dm059HWwNOBpyZ5cVUddJPKkiRJkrRgxZ13l45btOfzRtRep52Nh2lkyY0ITHJXYNv25bGzbOPfmUgC/hjYjSahsTPwXuABTKxFOJX/o0kC3tDW2RXYhGZU1QHABcCdgS8lff+U+BSwHvAsmmTNLWmSXRcAawL/l5uOUPx4e358kvWmeI/rMJGc+0RVVdez29AkcdYFLmrj3ZJmhOUzgKuBz/V/+//0rLafj9Ds8rwpcFfgqLavZwBvpUlsnQA8tH2P2wGvavvaGvhekg379LMfzedyaNv+pjTJ1q+1zx/D5LvvFM335OXAv7T9bgrcCXgRcCbNepOf6zeqkCZx+AXgFJop6bek+Vm/BlgF3Br4n546B9BsfnNm+/pt7evu48edwiP4rC4EPtZ+FnehSZhvQ7Pb9mfbMq9JsveQce4MfI/mc/wt8OQ2rk2A+wBfovmz6QNJHjFJnJIkSZIkre7Wbs/Xjai9TjsDzR6dylIcEfiu9lzAwTOt3CbI3ty+PBbYs6o6P4wLgZcmubCrzGRtPIQmYQawf1V9puvxRcDBSX4CHE+TGNkX+PwUzW0C3K2q/tJ17/NJrqQZsXcb4MHAt7uef5ImWbM+zeKVn5qk3X2ADWk+p0/0PHtd++wG4CFVdULXs08l+QUTc9enswXwxqp6fde9CwHSTI1+d3vvROABVXVV+/p84B1Jfgt8iyaR9GqahNdkVgAfbjeM+Wc/SR4DfBXYG3h5koOq6uxOgao6ieZn0OtC4OQkhwO/p0li7gH8YIr+N6T5eT6gqq7puv+2JJsALwMenWTDqrq07fta4Np22i3AdVV1xWSNj+KzqqpnTxH7mcCP2unNrwZeCXy9q97AcbYOBtYBTgLuXVVXdz07Ftg3yaE0ydt3Jfl2dyJakiRJkqQF4CKawTm9M+Fma5P2fPEwjSypEYFJXkGTrIEmKfTbWTSzN83oLoD/7EoCdnsH8Pc+bbywPX+vJwn4T1X1eyamFz+lT1sf6EkCdnyb5ksHzZTQ7rbPo0kIwcT0316d+0dV1emdm0nW6IrnUz1JwE77f6LZvXkQF9JMZZ3M3kz8wryiK7HV3de3mUhK/WufUXlXM8kozTbB9BKahOcawFMHjLtT/xwmkn979CsLvKonCdjRScSuSTMSbzZG+VlN5dPt+d5TjSSdTpK7A/dtXx7QkwTs9t/teSdm/5lIkiRJ0mooC/zQgDobhOw0ovbu0J4vGKaRJZMIbNese1v78rc0Uz1no5PEuIiu6Y7d2nXmvjlFHMuBzsYbRydZf6oDOLktd/c+8XxvihhWAZ0E4a0mKfKx9vygJFv3xHgbYM/25aE99e5EM9UTmpF0U/lKn2fdjpwimQrNVFyAS4Af9mnj8Pa8GbDDFGWO6oy061VVf6UZnQZwv97nSZYleXKSr7YbZ1zdvREGE1Oob98nxmuZej3G7rUFJ/tZDWIkn1WaDWDemeSXaTZEWdn1Pn/XFlsO3HaWce7eni8Dft/nu38JE39o9vv+S5IkSZK0Ovo1Teb0XzLgZrBTSbI2cH+aQUy/HqatJTE1uB2FdDhNAuNvwCN6RyK1Cbp1pmji+nb6IzRTTAH+OM10xak2rrg1E4m0t7bHdDbr86zfRiKdUWGTva9vA+cAm9NsztA9Ku9pNJ/VFTTrtXVb0XXdb3OO6Tbu6Ditz7POLrR/mOaz/l3X9dZT9D1dPH8AdunqE2h20qVJ6j5gmvrQTP+dyvm9G5F0VNVVXYPzpvoOTmfozyrJE2nWjxwkhn7vtZ9O8vHmwKSJ2Un0+/5LkiRJ0sKRQJbMmKyl7kc0+ZZ1adbW/8AQbT23bafadmdt0X/7ktwe+A5N8u18mjXt/jZJ0fsDl09xfLSrXGdK5JXTdD3VGmmzSaDcrM+zlQPUv8nY3TYp1ZmS2js9uLN+4eFV1fs+u6eE9vsM+q0R122qqaEwsbvudG1d3nW9wRRlBv15rd9zv7P5SwH/j2YDjm1pdunpbITRmcLdL7E+yM8JZj/OeqjPKsl2NGtHrgP8mWYznLvSjFC8eVv2jl11Z/uPCLP5/g/1LyeSJEmSpPFKsmWS17ezz85Lck2SM5J8I8nT22XIFk2/rW8wkYt4bZJt+xWeSvv39de1L69iYtPTWVnUIwKTbAV8n2ZE0WXAQ6tq0JFqU+n8EKdbI603odRbH+AxVfXVIeMZxseAVwC3S3LfqvpZkl1pdj+Gid2Fu3XH3+8zmOr9z8RUybl+fV0+RZlBf17/TKS16+A9rX35tqr6r8kqzna9vBEb9rN6Jk3C+VLgvlV1Pj0m2X16Njrfn99U1V1G0J4kSZIkaTWWZH+a0XC9A3e2bo9HAv+R5ElV9eeF3m9HVV2Y5KPAS2kGE30nyT4zyUsl2ZEm8bcxzQClj1bVRf1r9bdoRwQm2YwmCbg1zaizR1XVlDvZVtVRVZUpjv27ip7enneYZrOFHae4fzbQ2TDiroO8l7nSfvl+3r7cr+f8l6qabA3E07uup3qP0z0bVKevHaf5rHfuuj5jlvF0nnfX34GJ0Whf6FP3jn2ezZfT2/NsP6s7t+cfTZYEbI3iff61Pe/Q7sAtSZIkSUtLli/sYyZvNdmPZhBSJxn3V5qZd6+jmV3XmSV4d+AH7Z4Fw3/EY+p3Em9mYkm02wPHJ3nzdP0luU2SNwPHA9vTJAFPa9sbyqJMBCa5OfBdmkTO9cC+VXXMiJr/WXvemGY68WT9r0GTWb6JdmOMTixPneNhqIPobBryxPZze3L7+hNTlP8tEyPJ9unTbr9ng/pJe96IiU0mJrNvez4f+NMUZXZLMum01HZ4bicR9tOuR91TUif90y7JPYHt+sQ2Ctf3i6E17GfVea/9+ui3ezUMFuf32/PawJOmaU+SJEmStEAl2Qb4CBNLYH0IuH1VvbSq3lRVT6XZCfeP7fNO+QXZ72Sq6hLgsTSz+Ipmnb9XA2cm+V2STyZ5T5I3JHl3+/p3wJltuXXb93EFzazSS4aNadElAtudVL4B3A1YBTy9qr49wi6+TrOjKcA7kky2ft8rgS36tPHe9rwd8O5+I7iSrNV+iefK52mma24I/B+wCc2Xc9JEYLu2YGdNvKe1G7HcSLsu4wtGENs3mNg59p2TjSBrd4Pep335sT4bZazDxK7R3fVD8/MIcAPwma7Hp3ddP2qSuuvS/IEy1y5sz7fuU2bYz6rzLxT3TbLxJHWfADxs2Dir6ufAL7ri7LfTMkmm2gVakiRJkrR6eyPNIBBoBq+8sKputH5+VZ1OM2ClM7DkUUkG2axzdex3UlX1G+C+3Hiz1GU0MxOfCvwH8N/Ai9vXO3LjfN1faJbw+u0o4llUicB259/PM7HD68uBbyVZf4pj3Zn20e42/N/ty3sD30/ygCSbJNkpyXtohmqe3qeN7zKx/t6LaIahPjrJFkk2SrIiycOTvJdm+ubjZxrnDN7P5cAX25dPaM9HVtWZfaq9kWYtuTWBI5I8px22unmSpwFHMZGUGia2a4GXtS/vBhyV5CFJNk2ybZJXAF9un5/JJIm+LqcD/5bk40l2SbJxO5rvK8Cj2zL/U1Vnd/X/DyZG2r0myWuSbJ9ksyQPb5/dlalHIY5KZ0r7o5PskeTmSdZoj7SxDvtZHd6eN6FZt+BB7fvcsR2O/GnglGHjbD2L5l8zNgWOS/Larp/JLZPcLcnzkvwA+OXAn5IkSZIkrfbaXYMX8jHIu2zyLft23XpTVa2arGxVnQx8qetW74amg3+6Y+p3OlX1O5r8wRuZGFyWPgc0eZc3Andv64/EuKeljtpWwN5dr9/THlM5A1gx006q6oNJdqbZWfUBwNE9RX5Mkzj5aG/dLgfQzEl/Ps1Uzn7TOa+daYwz9DFu/IU/tF/hqjo7yWNpRqFtDBzcU+QymqGvP++tO1NV9akkWwJvAe4JfG+SYmcCe1XVpX2a+gRNVn3/9uj1VeC1k9z/N5qf50ZtDG/pDo8m2Xxnmrn+c+UjwLNpEmc/6Hn2IJrE61CfVVX9MMkhNEm6ewJH9tT7I/Cv9P+ZDhrn75LsQfMH7pY0f7C9cYo2h1oEVZIkSZI0FnvRTGsFuJib/h2x1+FMLB+1T5Jn95nxtzr2O612INaBSd5FkwN6EM0U5U2Bm9PkUi4Efk/zd/Ijq+rKKZqbtUU1InA+VdXzaUbqHU3zw7oSOAl4FbAHcN009a+vqn+nWZjyozSJlitopqdeQJNweTNw16o6aI7eRieWY4DODjmXMzFyrF+dI2kSYIfSbIByHfA3mpGOu1bVsSOM723APYBP0iRvr6X5zI8HXgPsPOCuO08Gngccx8TP7DjgOcBj22nPvX2fDOwKfAo4h2bY8D9oEoe7V1W/RPNIVNVJwG40OwWdQ/MdmarsMJ/Vc2gS1CfQJKmvoFkT8kCaz+CcEcZ5HE3y9IU0fzCfR/PZXkXzXfwCzZqEK/r1KUmSJElaLd2t6/pnU43K69K9WenGzP7vguPqd2BVdWVVfaNds/ChVbVrVd2+Pe9VVS9pn488CQiQOUp0aoFJcjLNjrKHVNWzxx3PqCQ5nWbhzzdU1YHjjUZzZddddqrjvvfx6QtKkpaeVddPX0bSwnLjpb40Bvd8+AEc/5s/TrnWvaa266671vHHHz/uMIaS5ISq2nWaMl8GHtO+fF9VvWSAdi+h2b8AYO+q+sYsYhtLvwuJIwJFkl1pkoAwsXahJEmSJEnSbHRvInnWgHW6y22+wPpdMBbbGoGanRe251Oq6qdjjUQaUJLnAs8F2HqLRf9ntSRJkqRF4LKrb+C7vxt6b81x2zRJ97DGg6uqd++ADbquB53ietUU9WdiXP0uGCYCl6gka9AsoPkE4Gnt7f8ZX0TSzLT/oTkYmqnBYw5HkiRJkpaKC6abGgys03Xddw+FLtd0Xa87ZanVs98Fw0TgEpRkBXBaz+1f0OysK0mSJEmSNIyru65vNmCdtbuur5qy1OrZ74JhInBpK5odf78JvKbKVXclSZIkSZorBaxcGpu2Xt51vd6AdbpH410+ZanVs98Fw0TgElRVpwNLYoenqlox7hgkSZIkSVpizum63mLAOltOUX8h9LtguGuwJEmSJEmSRul3Xde3n65wkk2BDaeovxD6XTAcEShJkiRJkjQvimJJTA0+oev6PklS1XdO9P27ri/mpvsarO79LhiOCJQkSZIkSdIoHcHExh0bA3tMU37fruuvTpO8Wx37XTBMBGpkkqyT5DFJPpLkhCSXJLk+yflJfpjk+UnWmb6lhSHJbkmqPVaMOx5JkiRJklYHVXUl8MWuW69NMuleBUnuADy+69YnFlq/C4mJQI3SucCXgQOAu9HMs18D2BTYHfgg8KsktxtbhEuACUpJkiRJWj11dg1eyMcMvA64tr1+APD+JMu7CyTZhiZxt2Z761tVdfRkjSU5vevvugfOV7+LjYlAjdIGNL9snwWeBGxHMxT3LsCHaf7M2xE4Isn6Y4pRkiRJkiTNsao6HXh+160XAn9M8u4k/53kU8DvgZ3a538DnrdQ+x1EkiOTPCnJzeajv8m4WYhG6YPAm6rq3J77FwPPT3IG8HZgBc0v5TvnNzxJkiRJksZr8a9CN6GqPtZOzX0fsD7NgKGXTlL018ATq+qshdzvAHYDHghcnOSTwCFVNa87FTsiUCNTVS+YJAnY7d3Ahe31w+YhJEmSJEmSNEZVdQhwB+CNwK9o8gLX0YzE+xawH3DPqjp1MfQ7gAC3AP4DOCnJT5PsN197KpgI1LypqhuAzi/YbWbTRpKj2vUADm1fPzrJd5Ock2Rlkvf1lF8nyYuTHJPkgiTXJTk7yeFJHjhAf7sn+VaSC5NcleT3Sd4w3dTmQdfp630/U5RZN8lLkvwoyblJrk3yt/Y9vTLJVl1lC/hRV/XTuuKoJKf3tL1Fkucl+Wbb5rVJrkhySpIPJtl+mvd5qyRvT3Jiksvaz/cfSX6T5OAk+/SpO9TPRpIkSZK0MFTV36rq9VV196ratKrWqqqtq+qRVfXJNl8wXRsrqirtceB89TtiTwZ+0F6nPe4NfAz4R/v38LvOZQBODdZ8u1V7vmzYhpK8C3h5n+d3AL4JbNvz6NY0W4Tvm+RdVfXKKeq/Cnhbz+2daBYe3Rd47SxDH1iSXYGvAlv0PNqyPe5P8y8c+8+yi5OBjXru3YxmLccdgWcmeVJVfX2S2O5Ek3TcpOfR5u1xZ+AJk7Q/9M9GkiRJkhaqJTQzWD2q6vPA59vNSp5N83f5zt/3b06zVuHzkvwa+Cjw2aq6YpQxOCJQ86bNancSP8cO2dyeNEnALwL3pdmZeCfgC21fm9MkqbYFzgCew8TmJXel+YUCeEWS59MjySOYSAKeDDwCuCWwPU0icHuaqc5zpt1d+Yc0fyhcTpN4vBPNe9gGeDRwKHBNV7UNgId3vd65vdc57tDTzZ+BdwAPbp9tCtyeZgv1Y4F1gE8n2XKSED9CkwQ8D3huW2+T9rwHzXqQZ07yvob62UiSJEmStJBV1RlV9Vqav9s/CvgacAMTowTvSvN37n8k+b8k9xpV344I1Hx6V3su4OAh29oC+GRV7dd178Ku6/fQJO7Oppnzf17Xs4tpMuznAK8H3pTk0Kq6qqvM/7TnvwL3r6pL2tfnt+X/Cnx6yPcwnQ/T/IvAFcC/VNVJXc8upkmyfT3JP3+Pq+qKJFd3lbuq378eVNU9Jrl9IXBqkq/QJOzuT/OvEv/dKZDk5jQJWIDn9IwYvIhmCviRwKsnaX/Yn40kSZIkSQteVa2iWa/wW0luBfwr8EyawUcA67X3/jXJ72hyKZ/uylHMmCMCNS+SvIJmlBjAh6vqt0M2eQMw1ZTeW9GMaAN4WU+iqdvbaZJsGwN7ddW/F820WIA3TPYLVlWfAY6bVeQDSLITE5/XG3uSgL2xzMmaBlW1Evhc+3KPnsfLu67PHrTNYX82kiRJkrSQFbCyakEfmhtVdW5Vva2qbg/sDnyWZgZgZ5TgHYH3A2cn+WSS+8+mHxOBmnNJ9mJimu1v6bOu3wz8us8OxQ+gGe1awM+TrD/Z0Zb5Y1vn7l31OyPdCrjJ2nhdvjJE/NPZvev6U3PYD0nul+TjSf6Q5PIkqzqbiwAfbIvdvrtOVXVGJAIclGSXAbsb9mcjSZIkSdKiVlVHVdVTaTZafR3NYKiiSQiuDTwVOCrJyUmelWT51K3dmFODNaeS3B04nGYE2d+AR1TV1T1lltOsRTeZ66vq2knun9an2x06TQOnDxjqZl3XK9rzOdMMt/3DgG3Pxnbt+byqOmeuOknyHuAlAxTdcJJ7L6NZk/HewIntdOmjgWOAI6pqspGCw/5sJEmSJEla9JLcj2ZDkX258aw8aP5ODc1eCQcDL0vyjKo6frp2HRGoOZPk9sB3aDapOB94SFX9bZKi96fZDGOy46OTlAe4eor7MHnSajprdV2v156vnKbOSHfu6bFBe758rjpI8jQmkoA/otnhdyeaDUM6m4v8W/v8Jv+6UFVfpNm05ShgFXBbmrUMPg78Lck32w1Pug37s5EkSZKkhaugqhb0obmTZNMkL0vye5pBNs+gyVGEZkTgd4DH0KzjfzwT04Z3pBkhuP2kDXdxRKDmRJKtgO/TjOa6DHhoVc3lCLpunQTepVW10RD11+tbCtbv82zQPx2n+h3sJAA3mOL5KDyvPf8E2LNdpPRGkqzdr4GqOhI4MsnGNFOq70ezw/Kd2vN9ktylKwE87M9GkiRJkqRFpV1S7dk0Owiv2bndnv8BHAL8v6o6s6vawe0eB/9D83fxdWg23Xx6v75MBGrkkmxGkwTcmmbk3qOq6ldTla+qo5j4go/CX9vzhkm2rap+04gnc3p73jzJhlV16RTldpziPjQLenZMNe0Z4NZT3P9ze75lks3naHrwndvzFydLArbuOEhDVXUR8M32eHWSx9NsNLIx8ALgP9uiw/5sJEmSJGlBW+WoOvHPAVSdXYK36txuzwV8j2aW5DfazTxvoqp+kWQ3mtGBuzCx58GUnBqskUpyc+C7NGvBXQ/sW1XHzHMYR9JMVQXYfxb1f9aeAzy6T7l9+jzrTtz1To9tGk+2o5lOO5kju66f1qefyVzfdd1vwdC1+pVJsi793+OUqupwmo1h4MYJ02F/NpIkSZIkLUhJ1kjyuCTfoRko8zqaJGBniu+5NJutbldVD6uqr06VBOxon3+5fblVv7JgIlAj1E4j/QZwN5pkz9Or6tvzHUdVnUWzQQnAK5P0zYgn2SbJP9ehq6pfMLERyOuTbDRJnacA9+oTwxnAee3Lp05SfxnN8N2p6v8B+EH78rVJphyZl6R3ZO+FXddTjTiEiQ1XHjnF8/8BNpmiz02TTPqsfb42sEVvPMP+bCRJkiRJWsD+TrPp5kNoBuV0RgD+AHg8sFVV/VdVnT7Ddi9qz9PuHmwiUCPR7vz7eeAB7a2XA99Ksv4Ux7pzHNKLgbNpttU+Msm7ktyzTWBtmuSOSfZP8lWaabi9a/G9vD3fFjgmycPaerdN8t80G2KcPk0Mn2zPT0jyP23djZPcH/gW8LA2xqn8G836ijcHfpLkNUnukGSjJFsmeUSS/wcc1FPvz209gP9Msn2Stdp/eej+Q6GTkHtQkk8luUuSTdrP6fNt/6dMEdsdaTYE+XSSJyXZIckt2rgeBhxBs+kINN+Lbi9muJ+NJEmSJC1IRTNqZiEfGspmTIz+Ox94J3C7qnpIVX2pqm6YZbt/B46m2WCkr7jji0YhyQomRpgN4oyqWjGLfo4CHgh8oqr2n6bs7YCvADtP0+xKYLOqurin/qtohuRO5hTgv4Evta+37c3YJ9kQ+DnNTry9rqeZGvtc+ryfJPcEvgZs3if+m9RN8nYm1uXr9s/PPcn6NBuF7DJFu1+mSVgeAlBV/1zHsV2D4Ed9YoLmv3FvrqrX9T4Y9mfTa9dddqrjvvfxaZqSJC1Jq66fvoykhaX/LDnNg3s+/ACO/80fR7nO+5Jx2zvsUm/5zLxPnBupp9xtyxOqatdxx7EQJVkJHEWz9t9Xqmre/0fFEYFatKrqVOAuwH40U5b/AVxHs5HH6e295wCbT5Zoqqq3A3vQbM99Mc3GJ38E3kozLfii3jo99S+l2bnn3TRz/6+jmS78ReA+VXXYAO/hOOD2wKtokooXt+38jSbT/wrgvyap+hqakXe/pNmB+CYZ/6q6Arg/8HbgLzTJyYtokoPPAvZl6n/w+RnwYOAdwE+BM4BraT6jU4FD2/d4kyRg2/dQPxtJkiRJkhagHatqj6r6wjiSgOCIQEmLgCMCJUlTckSgtPg4InDsHBE4e7e9w53rzZ9e2CMCn3r3rRwRuIA5IlCSJEmSJEmaY0n+2h7PmkXdp7d1/zJMDL27jUqSJEmSJEkavRU0S3dtOIu6N++qP2smAiVJkiRJkubJKldo0xg5NViSJEmSJElavS1vzzcM04gjAiVJkiRJkuZBAavctFWzs2V7vmyYRhwRKEmSJEmSJK2mkmwBPJEml/ynYdpyRKAkSZIkSZI0Qkn2A/ab4vG/JXnkAM0sBzYBdmBiMN/3honLRKAkSZIkSdJ8KKcGLyErgN246S6/AW7bHoNKez4L+N9hgnJqsCRJkiRJkjQ30nVMdm+Q4yzgI8A9q+qiYYJxRKAkSZIkSZI0Wu8DDu16HeCvNCME3wr8vwHauB64tKquHFVQJgIlLQpJpi8kTaOcprH6qFXjjmB4tXLcEQxvMbyHldeNOwIBZBFMRFq+1rgjGN6yNccdwUhk+XrjDmF4C/13IsvHHcGCVcAi+L8cDaCqLgUu7b7X/r01wMVVdcY44jIRKEmSJEmSJM29B7Xnv4wrABOBkiRJkiRJ0hyrqqPHHYOJQEmSJEmSpHniajQapwW+MIEkSZIkSZKkQTgiUJIkSZIkaR4UsHKVQwIXuyR/7XpZVbXdJPdn65/tzYaJQEmSJEmSJGl0VtDkfdOee+/PVm97M2YiUJIkSZIkSRqtzPD+vDARKEmSJEmSNE9WuVvIoldVk+7JMdX9+TT2ACRJkiRJkiTNPROBkiRJkiRJ0hLg1GBJkiRJkqR5UAUrV407Ci1ljgiUJEmSJEmS5liSk5K8NMmtxhWDiUBJkiRJkiRp7t0ReBfwtyTfSvL4JGvNZwBODZYkSZIkSZoXxSrcNXiJC00+7qHtcWmSzwOfrKqfz3XnjgiUJEmSJEmS5t6ewCeBK2gSggE2Ap4L/CTJn5L8V5Jt5ioAE4GSJEmSJEnSHKuqI6tqf2Bz4BnAD4BVTCQFtwPeCPwlyZFJnpFkvVHGYCJQS0KSA5PUJMf1Sc5L8qMkL06y/rhjlSRJkiQtTkWza/BCPjS8qrqqqj5dVQ8BtgZeDfyOiYTgMuCBwMeBc5IcmmT3UfRtIlBL3RrAZsBuwHuBE5PcbqwRLVBdydX9xx2LJEmSJEkLQVWdXVXvqKo7AbsCBwHnM5EUXA94OvD9JGckefMw/ZkI1FK0M7BBe2wM3Av4UvtsO+BrSdxIR5IkSZI0cquqFvShuVNVv6qq/wBuA+xNk6u4lomk4FY0owdnzUSglqKrquqK9ri4qo6rqn1p5uYD7AQ8dozxSZIkSZKkJaqqVlbVN6vq8TTrCb4HRrPdtIlAaUL38No9xhaFJEmSJEla0pJslOQA4DvAS0bVrolAacLvuq63TPKqds27y6fbpSfJf09Wtl3Qs5Ic1b5+QJIvJfl7khuSfLWr7LRr7HVtenJ6z/1ftve/ON2bTHJcW/bLPfc3TbJfki8mOS3JNUmuSvKXJB9Pctcp2jsqSfe/THx8kk1ZVkxS7zZJ3pHkN0kuTXJ1kj8n+XCS2073PiRJkiRpwSmoBX5o7iRZnuRRSQ4H/gF8iGY5s87UYIDjh+nDddCkCSu7rgN8gmaU4PrA44BP9qn7jPb8xaq6crICSV4IvI+5ScB/hmZR0Uck2bCqLp0ihu2Be3TV6fZ94C6TVLttezw9yQuq6iPDBptkX5rPd92eR9u1x/5JnlZVX7pJZUmSJEmSFpEkd6PJKzwZ2LRzu6vI2cCngU9U1SnD9OWIQGnCTl3XZ1fVP4Dvtq/3n6pSkvsCnZ2GD52i2I40uxIfQzPteDOahNcHZh/ujXyOJpG5Nv3XN3xqe74U+FbPs78D/ws8HLhTG+NtgUfQfA7Lgf9t/4Dq9jCajVc6nsfEZiyd44zOwyR7Ap+nSQL+mGYB1NvQ/GG3B3BU+z4Om2oUoiRJkiRJC1mSWyd5RZLfAr8EXkjz9+LO6L+rgc8CDwW2rqpXDZsEBEcESt1e1XV9VHv+GE0ibLck21TVGTepBfu157/SJPomcyvgSGCvqrqhvXdBW2doVXVOkiOBBwNPAT4+RdEnt+cvVdU1PW08cpLyFwCnAd9Oclhb/6XA07rqXQ2Q/PMfK66tqism67zdjfkQmn+E+B7w8Kpa1VXkyCRHA0cAuwNvpUk0SpIkSdKCV8DKVc6vXaqSrEMzeOcZNH/n7QzQ6x799xOaGXRfqKrLRx2DIwK1pCVZI8lOST4GPKq9fQZweHv9DeB8ml/Kp09Sf23gCe3LT1T1XTHhlV1JwLnQmer7oCSb9z5MsiuwQ0/Zmfh0ex5mI5VHA1vT/PfvX3uSgECzOxLw+vblXkk2HqI/SZIkSZJWF+fSLDu2J82su87ov9OANwDbVdUDquqQuUgCgolALU2ndTaxAK4Hfg88s312DrBPVV0LUFXXM5EA2+8mLTWJrY1oEluf6NPn+VV1wghi7+fLNEOHlwNPmuT5U9rz2UyMeLyRJHdO8sF2A4/Lkqzq+qw6U4k3T7LBZPUHsHt7PgW4LMn6kx3AHzshAb1TkSVJkiRJWojWZyL5dznNLMQHVtV2VfWGqjptrgNwarAE19IkA78OHFRVF/Y8P4Rmq+7tk9yvqn7a9ayTHDxqimnDHXP+y1xVlyf5Bs0IxafQbEwCQJJlTCQHPzvZSLwkLwXeSZNInM6GNH9ozVRnROIdZlB/s1n0I0mSJEmrJWcGL2mrgB/SDCT6SmeprflkIlBL0c7Ame31Db1r5fWqqt8l+SXNbrv7AT8FaKffPqQtdug0fc7XL/dnaBKB90iyfVX9ub3/IODWXWVuJMm/AO9uX/6aJol4As2w5c7n8wAmRgXO9s+ODWdRZ61Z9iVJkiRJ0upk66o6e5wBmAjUUnTVVJtZ9PExmkTgE5L8R5u1fxrN6LnLgS+NOMapTPc7+x3gImBjmlGBb2zvd3YLPqWqfj1Jvee1578C950sOZrkZjMP9yaubM9fq6p9RtCeJEmSJC0YBazsu7S8FrNxJwHBNQKlQX2WZlTfhsA+7b3OtODDq+rKySrNUCf5tk6fMrfu86yzpmFno5OnACRZi2ZXIph6k5A7t+ev9xkhecd+fQ+os0vyXUfQliRJkiRJmgETgdIAqupSms04APZPcjcmEmOHjqibc9rz7SZ72K7zN8iOvZ1k3w5J7g48kokpuYdNUacz/XbS9QGThMk3IOnW2RG53xqD32/PWyd50DTtSZIkSZKkEXJqsDS4j9FMsd2TiY0u/lJVPx5R+78EVgCPS/Lqzs7FXf4D2GaAdn5Cswbi1jSjAle093/WZwei04DbAw9JcrOquq7n+Stp1lbs50LgVvQftfgl4G/AVsBHk9y/qs6dqnCSHarqj1M9lyRJkqQFpdwsZClIcmTXy6qqPSa5P1v/bG82TARKg/sRTcJsW+Bx7b1DR9j+J4DH0yTwvpbk1cDpwJbAv9IkAv8K3LZfI1VVSQ4DXkWTCLxF+2iqacHQTCfei2ZX368lORD4C3Ab4ADg+cApwE592vgV8DBgvyTfB06m2ZGZqrqhPV+XZH/gCJqRj79O8k7ge8A/gLVpkp33BZ4IrAfcqd/7lSRJkiRpNbMbzZKQac+992ert70Zc2qwNKCqKm6c+CvgkyNs/1tMTN3diyaxdhFwEvBi4EPApwZsrpP025xm2u8NwBf6lD+UJhkH8FDgWOB84Dc0ScCfAa+Yps+D2vP2bf0rgOuB65Os6BSqqiOBvYFLaEYPvhf4PXAxTTLwWOA9wL2A3pGJkiRJkiQtBOlzf7bH0BwRKM3MocDraZLoR1bVmSNu/xk0ibB/pRmddx1NMu5/q+rwdqTetKrq5CQnMbEJyBFVdUGf8iuTPAp4GfB0YDuazUtOpdko5SDgftP0+Z0kjwReBNyNZiTipOsFVtW3k2xLs1vxI2hGGm5EsyHL32imSX+ZieSkJEmSJC0Kbhq8JGw7w/vzJuU3UBpYks2Bs2gSXE+vqk+POSQBu+6yU/3yiEPHHYYWAf+buBqpVeOOYHi1ctwRDG8xvIeVDi5fLWQRTERavtb0ZVZ3y9YcdwQjkeU3G3cIw1vgvxP32PMpHH/i70cyOmmp2eL2d6oDPviVcYcxlNc/5HYnVNWu445Ds7Ow//SR5t/TaJKAlzGxi7AkSZIkSdJqz6nB0oCSrEEzlRXgM1V11TjjkSRJkiQtLAXc4LbBS1aSrdvLi6rqihnWXQ/YBGCYZcocESj1kcYaSW5Fs07edsBK4H1jDUwkeW6S45Mcf/5Fl4w7HEmSJEmSpnM6cBrw3FnU3b+t+9dhAnBEoNTffsDHe+69t6r+NI5gNKGqDgYOhmaNwDGHI0mSJEkDcVlqDWHotTlNBEqDuYEm8/5/wHvGHIskSZIkSdKMmQiU+qiqQ4FDxxyGJEmSJEla2jpbpl87TCMmAiVJkiRJkuZBVbHSzUI0Ozu254uGacREoCRJkiRJkrSaSRKanYL3BJ5Ks/H0ycO06a7BkiRJkiRJ0ggleX2Sld1H5xHwrt5nkx00+xWcC3wGWLet/6Vh4jIRuAAl2S1JtceKEba7oqvd3UbV7qglOb2N8cBxxyJJkiRJ0kysWuCHZiQ9x1T3BzkAfgAcMkxATg2WJEmSJEmSRusS4Iyee9vQTO+9BLhsgDauBy4FTgG+CXyxqobKx5oIlCRJkiRJkkaoqt4PvL/7XpJOEu8tVfWe+Y/KRKAkSZIkSdK8KGBVuWvwEpfpi8wdE4GSJEmSJEnSHKuqse/VMfYANLkkuyf5VpILk1yV5PdJ3pBk/QHr3z3JIUn+0ta/LMmvkrwuyc0HbGOtJP+Z5KQkVya5JMkPkzxskrK3SXJDu4nHftO0u1W7A86kZZPcMskHkpyW5Jokf0/y2SR3HSDmo9p2D21fPzrJd5Oc0/b5vp7y2yf5UJI/tZ/T5e37fWuSTQfo7wlJjm4/m8vbz/g/kqyRZP/O5ivTtLFHksOSnNm+30uS/DzJi5OsNUWdG20Yk+TmSd6S5I9Jrm6/N99Icu8+/a6d5BFJDk5ycpIrklyX5OwkX0+yzzRxL0vyjCRHJDk3yfVJLm4/y28meWGSTUb5viVJkiRpoVu5amEfWtgcEbgaSvIq4G09t3cCXgfsC7y2T90AbwdewU2Hm961PZ6V5KFVdUqfMDYAjgHu2XN/d2D3JAdU1cGdm1V1dpIjgIcB+wGf6NP2M2iS0FcAX+yJ/47AkcBmXbdvAzwJeEySJ/Zp90aSvAt4eZ/nT6fZbWfNnkd3ao/nJdm7qn4ySd0AHwWe0/Oo8xnvDXx+mvhu1vb/tJ5HawH3bo9ntj+rf/Rpagvg+8D2XffWBh4J7JVkn6r69iT13ga8eJL7twYeBTwqyaeBZ1TdeOx6kjWArwEP76m7UXvcDngE8Dfgqz11R/W+JUmSJEnSDDgicDWT5BFMJAFPpkmm3JImyfO69vzuPk28AXglTRLwUOB+wKY0ybSnAKcBWwPfTLJBn3beD+wIvAS4bdvGw+H/s3ffYZJUZRuHf88usMiSc845IxkkCUgWEERFEERFRUAxfUZERTCgIJhAhRUJklFBRDKIgJJBcliC5ByWsLvzfn+cU0xtb3dPz0z3dJjnvq5zVVfVqapzqnqmp985gYfy/qMlzV9xzIl5ubmkJeqc+2N5eXZEvF5slDQe+CspCPgG8H/52gsAu5Bm25kAzFHn3IWtSEHAs4GNcvlXAs7M19osn2tG4AHgg8CCpHvzGeAFYC7gwhp1OYj+IOAVwKala/wY2DyXv57fkoJhU4CjgXWAeUizCH0aeA5YHThH0tg65/kjMB74BLAY6f3y4Xz8jMBvJVUGOyEFYs/IedcmvUcWIb1nfpPLtVeua6WP0x8E/CUpYLxQPn6DXP6rqT67fLPqbWZmZmZmZtZxJC1eTrW2DzUNp2xuEdh5jsrLh4BNIuKlvP4s8H1JDwGnVDtQ0orAN/PqtyLiBxVZTpd0BXArKcB2APCjGuVYLF//+tK2i3J30duBWUitE39V2v8XUhBnXlKw7/tVyrghsHxenVCx+yBgyfz6wxHxl9K+P0u6DriZFGwayCLAyRFR7nr8fOn1caRA+OPAxhHxTGnf8ZJuAK4HZifdow+X6jAL8L28eg2wTURMLl3j/yQ9Q/+znI6k99EfEN03Ik4t7X4BOEHSP4EbgQ1J97pWC8N5gHdHxIOlbWdIep0UWF0Y2BqYplVgRNRqWfoE8C9Jt5BaPX5F0nEVrQKL7uHnRcSBVY6/ATihYnuz621mZmZmZtZVIiA8WchoMJE0Nwx5OUOV7UNVPt+guUVgB5G0PqkVHsB3S0HAd+TAyb9rnOJzpGd6D3BEtQwR8RTwi7y6Z53i/KkiCFgcfwcpkAiwbsW+t4EisFNrnMBi+0OkFmPV9l1REQQszv8MUBncrGUKqWXkdCStS+r6C+k+P1OZJyJuJQXBAHaTNGdp9/vpb5X4f6UgYNkxwKN1yle0sru4IhhWLsNdwGl5td6zOrYiCFj4Gym4BhXPqkFFwHlR+oO3haKl3hODPGcz621mZmZmZmbWqVRKtbYPNQ2ZWwR2lo3yMkit62o5j+nH7oM0fh/AVcD4NIxdVXfl5aqSZsoBvEoX17n+/cCapC67lX4PfB5YRtJ7yuPr5QkgijH+/lBuYSZpbvqDoOfXufZ5TNsKsZZbIuLpGvveU3p9bp1znAUcTPo52QD4e95ePKfnIuK6agdGxFRJFwKfrdyXu7tullevUv0JYO7My7Xr5Kn6rCKiT9KDwNxUf1ZIWoDUFfp9pPs/O9V/LywP3Ftav5UUEP14bql5Vo33Uflaza63mZmZmZmZWSe6muot/2ptHzEOBHaWJfPyqWqtAUvuqbG9aLX16ZwGMobUrbTahAz1JmmYlJfvqtwREXdIuokUwNkHKE+0sTNpIolg+slEyuPw1aofEfGUpJcZeJzAh+vsK671VES8UCfff0uvy33wi+PvH6AM99bYvhBpMhZILTertt6sMF+dfUN6VnmcxPNIYyEOpPJ+H00aJ3AxUsvBX0u6htRV+nLgP5UTjND8epuZmZmZmXWdKZ55t+dFxOaD2T6S3DW4s4zPy9fr5kqTPEwjT7QxlMDuuBrbpzZwbK0mh8WkIXtIKgegiq6/V0bEIxXHjC+9HnT9q3ijzr6iJdpA53m19Lo8scqQn1PWyGQnlWaqs2/Qzyp3dT6HFAR8ijSxSjHhxxyk+s5eOmSa91YOVK8H/Bp4OeffnjTRzQ3AA5Iqu/U2u95mZmZmZmZmNggOBHaWIrA0vm6u/kBW2Rv0z9B6SESowTSxWYUvOQ14kxRI2hVA0oLANnn/SVWOKQfVhlL/wSgCdAOdp7y/HBQcznMqHw+wa6PPaoBrDdbupNagU4EtIuKnEfGfiHgqIl6JiNfoHwewqpz3ANLkMBuQZpi+AHibNBnNqZIOKB3SCfU2MzMzMzMzG7UcCOwsE/NyQUn1Wk+tWLkhIvqAopXdWk0u16Dk1mLn5dWiFeBHSYGlV0kt0SqVWwhOV79CDigOpWVZ2cS8XFBSvW6xq5ReP1Ll9XIDXKdygo3CE6RAKbTvWa2el3dERK2u2Ks2cqKImBIRN0TEMRGxEykIWJzzUPUPVtkJ9TYzMzMzM2ubIM0a3M3JupsDgZ3lX3kp0nh6texSY/sleblzxSy37VB0D95K0iL0BwTPiohJlZnzWH1F8GiXOuett69R5XELP1An3+55OYXU3bVQPKd5JW1Q7UBJY4Adqu3Lk2oUMyZ/VFI7xuosuoTXa/U3pBl7I+J/9M+4vABpXMhOqbeZmZmZmZlZR5M0t6SDJP1S0k8k7dSsczsQ2EEi4gb6g2HfqRbMy+OurV/jFMeSugfPAfxO0oy1riVprKRlhlfiui4jtZwbA/wMWC1vn1DnmGICkS0kvb9yp6T5gG8Nt2ARcSNwR179jqR5q1xrdfpn/D2nYvKWvwKv5Nc/rBHQ+jzTToBS6ei8XAb4aanV3HQkjZNU71xDUUymsqKkZatcc2PgU3XKVLPVZla8t95m2m7V7a63mZmZmZlZW02N6OpkQydpfkln5vShGnk2AO4DjgE+A3wROF/SFZJmq3bMYDgQ2Hm+nJdLA1dL2k7SvJKWlvQt0vh6E6sdGBH/Bb6bV3cDrpP0EUlLSJpT0mKStpT0A+AB0phuLZFnjJ2QV/fIywci4po6hx1Hf93+JOkrkpaUNJ+knUkt+cYDLzWhiAeRgqaLAddK2jX/QC4q6VOkmW/HkQJ+XysfGBGvA4fl1c2AiyS9J0fsV5B0JPAT4KFaF4+Iv9M/VuLBwKWSdpa0SH5WS0raXtLRpIDqB5tQ57JzSfWfEbhQ0o6SFpS0lKQvA3+n/qzIF0m6TtIhktaXtEB+n64p6UfA53K+cyJiSgfV28zMzMzMzKxdtiL1PtwN+G/lTkmzAGcDc5N6i5bTpvQ3oBoyd83rMBFxoaSvk2ZfXQ34W0WWu0mt4qqNswfwfVKA5zBgbdLEHbW8NazCDuwk4FD6Z6yt+4aNiNdzc9fLgfmAH+dUeJsUVPw5ubvpUEXEVZI+DvyONJbfuVWyvQi8v8aEKseQxtDbj/SDvFXF/iuBPwG/ofasvp8mTfJyAPDenGpp6rOKiPskfYf0flme1Mqx7EnSL6fpfjFlIk0QUrVrdHYTKdhXqW31NjMzMzMzM2ujInbwSETcWWX/J4GFScNJPkL/sFufBxYkDQW3YURcN9QCuEVgB4qIHwJbAheRglFvAPcCR5C6Bb9Q59iIiMOBlUjBqttJrdqm5nPdROqquzH9rQ9bIiIeIQX1IAUnT27gmDtJAbZfkN70bwNPAWcBG0XEn5tYvpNJE4L8htRC8g3SzLZ3kAKxy0fEP2scGxHxCeAjwDWk7q+vAbeR7uvWwEw5+6s1zjE5Ij5HCtgeT3rGr5HGJHwOuA44HFgrIo4bbn2rXP9wUou7f5LqPSmX4af5mnfVOXxb4AvAX0jd2V/O5X6aNFblp4ANIuK5Ktdta73NzMzMzMzaJiC6PNmwrEIK8t1QY38xVv/rpBjIjyLiR8A2+ThIk7EOmVsEdqiIuJz+IFqlK+lvZVfr+PsZZNff3PKt7nlzvn2BfRs8bTFL7BUR8WiD5XiG1HX3oBr7l6xz7OYNlqvIfz/9YwEOWkT8idTybzqlMRgfH+AcN5P6/Q/mulfS2LPafID9Z5OaHdfaX/Uaeabhe0itM4dkKPU2MzMzMzMz62Lz5eUDlTskzQGsQwr4nRURTxX7IuIOSdeQugfX65k3ILcItJaRtCApag3948KNCnkSjGLW4JvaWRYzMzMzMzMz6whFIPCVKvs2pD9Od2GV/Tfn5ZLDKYBbBForfZb0HnuR2mMadi1Jc0dErW7aXwSK2XjPGqEimZmZmZmZWQcLgj73rx3NZszLcVX2rVd6XW2YsmfycvbhFMCBQGsqSTOQ3tBb0z8G4S8i4s3aR3WtByX9ljRO3n2kcRCXJ00g8omc53rSWI9mZmZmZmZmNrq9CMxP9VZ9W+TlQ3nItEoz5+WU4RTAgUBrtskV6w8z7cy/vWRO4Cs5VXM3sEdE9I1YiczMzMzMzMysU90NLAC8T9KYIl4gaWHgPaTxAatOWgoslJfPD6cADgRaqzxDmuzk/yLitXYXpkU+Qpo9dx3SD/KcpNlz7wTOA47v0ZaQZmZmZmZmNkR9fe4aPIr9A9gcWAT4taSvAbMAvwfGkgKB1cYHBFgzLx8aTgEcCLSmqjXLbC+qN2OwmZmZmZmZmVmF3wNfA2YDPplTIYDHgD9XHiRpHuDdOc/NlfsHw7MGm5mZmZmZmZmZtVhEPEuaV2AqoIo0GfhkRFQOuQawG6nFIMA1wymDWwSamZmZmZmZmY0QTxo8ukXEuZLWBb4IrEUK8N0KHBURt9Q47MC8nAxcMpzrOxBoZmZmZmZmZmY2QiLiNmCfQeRfvVnXdiDQzMzMzMzMzGwERMBUNwm0NvIYgWZmZmZmZmZmZqOAWwSaWU8I/1fNLIm+dpfACho7cJ5O1wt16AV9/rnuCFOrjd3eZXqhDkBMntTuIljf1HaXwMyGyIFAMzMzMzMzM7MR4kYMVpC0AmnCkAWBWWmw525EfG+o13Qg0DqOpCuAzfPqERHxzTYWp60k7QucBBARatI5VwU+AmwFLA7MA0wCngD+A5wPXFBjyvKm8rM2MzMzMzOz0UbSB4DDgFWGeIohBwI9RqB1FEmLAZuWNn1UUlMCYM0mKXLat91laYSk2ST9AbgN+AawHum/DjMCcwArAR8DzgXul7RTi8vTNc/azMzMzMzMrBkkfRc4ixQE1BDSsLhFoHWajzJtgHoJUrDoqvYUpzdImh/4B7BG3nQzcAJwDfA0MAuwFLAd8AnSff8E8NcWFsvP2szMzMzMRpUA+twzeNSS9B7g26S3goD/AX8G7gVezdtbyoFA6zR75eW1pOj4nHmbg0NDJGkMcBr9QcBDgcNj2oEpngceA66WdARwOKnbcCv5WZuZmZmZmdlocmDp9fHA5yPi7ZEsgLsGW8eQtBb9/eNPBM7Orz8oaVx7StUTPg5smV//IiK+H3VGp42IVyPi80DLxuvzszYzMzMzM7NRaCNSq7+7gQNGOggIDgRaZ9k7L98EzgFOzetzAHXHq1PyQUnnSnpM0puSnpV0s6SjJL27zrFrS/q9pAclTZL0Sj7uUEmzV8l/paRyIO2k0niBRVpS0ofz6z5JSw5Q/r0azTsEX87LV4CvN3pQRNxVbbukBSX9RNKdkl6V9LqkeyQdK2mJBk8/5GdtZmZmZmbWzfr6oquTDcv8eXlBvQY6reRAoHUESWOBD+fVCyLiZVIX0cfytr2rHpiOnQ+4GjgT2BVYFBgHzEuahvtLpAkwKo+TpB+RZsrdD1gaeBcwWz7uu8AdklYaYrXOA14k9fv/2AB598nLqyJi4hCvN51c9hXz6pkR8dowz7cVcB8puLgKaXrzWYAVgIOAeyTtNsA5hvyszczMzMzMzLrYS3n5YrsK4ECgdYqtgIXy61MAcnT8tLxtO0nzVB4kaWbgYuA9pOa1vwM2BuYjzYi7OfAj0oQYlb4LfJUUqJuQj5sXWBjYE3iYNE7eBZJmKx23HSlYWPhMXi+nRyLirVL5P1ZrRlxJiwLvzasTquUZhveUXl8znBNJWgH4C6l+T5OCp4uSntuewKPAzMCfJK1X51RDetZmZmZmZmZdL4Lo8mTDcn9eLtyuAjgQaJ2imDjiBeCi0vZT8nJG4ENVjvsyqfUewCci4lMR8a+IeC4ino6IqyLia6Qg3zskrUj/GHjfioiP5+Oej4gnI+J0Ut/9p0ktBQ8ojo2INypa1r0VEa9VpOK344l5uQzTBuXK9ib9LL5G/1h5zbJU6fU9wzzXUaQWk68Bm0bESRHxv4h4Kt+vTYDnSJMQHVvnPEN91mZmZmZmZmbd7E+kxkjbtqsADgRa20kaT+rSC6n76juDZUbEncBteXXviuMEfC6v/i0iTqp1jYiYUrHpc6T3/z3AETWOeQr4RV7dc+CaVD3HzcCteXWfGtmKbsNnRcTrQ7lOHXOVXr881JNIWgDYPq/+PCLuq8wTEY/Sfy/Xl7RKZZ6hPmszMzMzMzOzHnAiKQ6xrKQDB8rcCg4EWif4ADA+vz61yv5i2waSli1tX5nU/Rfgj4O8ZtEV9ypgvKRZqyWgmDBjVUkzDfIahSJA+UFJs5R3SFqf/jH8Jgzx/PWUuyMPpw33RvT/vjinTr6zSq83qbJ/qM/azMzMzMys6wUwtS+6OtnQRcQbwG7AE8DPJP3fMGINQzLDSF7MrIaiq+hE4Noq+08DfkgKRO0FHJa3L1PKcxuDs3xefjqngYwB5gGeHOR1IHV5/TEwO6k1XDkAVrQSfIhBjOGXJ9x4V43dk/P4hJC63xbmbPT8VZRnA646mzBARDwu6WXS7L+LV8ky1GdtZmZmZmZm1tUkHZpf/g34FKlX3RclXQY8ArzRyHki4ntDLUNTA4GSNgbG5tX/5Ehno8fOAqyTV6dExL+aWTbrTJIWBLbMq9cAa9SYU+MOYA2mDQ6VJ+x4dRDXHM/Q3vvjhnAMEfGCpL8AHyQF/k7N5RhH/1h4fxjk1OGbAFfU2PcHYN/8+uHS9hWBfw/iGmWz5uWUUpCxltdIgcDy8xnuszYzMzMzMzPrdofR31uvWM7H4MfJb38gUNJOwPl59aKI2HEwx0fEJElfJc3IiqRtIuLSZpXPOtae9AeP92bgseGWkbRhRFzHtMG/2WodUMUbQB+p1dkhEXHMII4dqhNJgcAtJS0aEY8DOwFzk374/9Ci6/6z9HoT4OQhnqeYHGUGSeMGCAYWQcPK4OxwnrWZmZmZmVlPcOfaUa9ai5iqrWRqGNZbqJktAr9HKvhL1J4UYSAfB+4jtSb6PuBAYO8byqQQewPXAQ+Utq0O3N3IwRHRJ+kR0oy6aw2Uv0n+ATwOLEoq/5H0/5xcERGPDOZkEXElDfyiiIi7Jd1Dag24h6RDKmY8btTE0uuV6J8AZRqSFiH9/EJq1lw2nGdtZmZmZmZm1u0+3u4CNCUQKGl1Ule+AI6LiOeHcp6IeFbSccC3gPUkrRQRDQV3rPvkWWXXzKtfi4gfDZD/T6TmsntI+jwp8PcksBApYHTGIC5/CbA/sLOkOSPipcGVHoAppJ+hsQNlzMHHPwDfBPaRdCL904VPGMK1B+OnwG9JYxQeCRzUyEGSVo6IYjzA6+hvRbkbNQKBwO6l1++0Rhzus46IyY2U2czMzMzMzKxTRUSregM2rFmzBn8gLwP4xTDPdRz9zRx3r5fRul7RQiyAPzWQ//S8nAfYPo+p98u8bQdJH6t1oKTKoPexpMDWHMDvJM1Y59ixkpapsqsIeC/UQNkhzR4cwAr5+jOQus/Wm4W3GU4ErsyvD5T0LdUYnA9A0mySjgF+UGyLiKeBC/Pq56vN6CtpUVKgE+CGiPhvafewnnUD+c3MzMzMzLpCX0RXJ+tuzQoErpeXt0TEs8M5UT7+5ry6/rBKZR0rB6L2zKvXNdg19iJS13PoDywdBdySX58k6XhJG0iaR9L8kt4j6XCmHSuPHKT6bl7dDbhO0kckLSFpTkmLSdpS0g9IXZAPqVKe4n26j6T1JY2XNEOVoGNxzQeBq/PqHnl5ZkRMaqDuQxYRfcBHgDvzpu8DN0raX9JK+V4tKmkTSUcCDwKfZ/qux18BJpHGY7xa0j6SFpa0gKQPke7xfKSWkgcXBzXxWZuZmZmZmZnZMDRrjMCVSS19/tOk8/0HWBtYpUnns86zObBYfn16nXzviIi3JZ0L7AfsWHTplbQt8GdgA1J33/2rHF4t+PR9UqvAw0jvt9PqXL7a5BjHkSa3WRa4vrxD0lIRMbHKMScCm5XWJ9S5ZtNExFN5Vu9fkYJy7waOr3PIQ8BvKs5xr6RdSC0YF6J62d8E9oqI8uzEm9OkZ93IsWZmZmZmZp0qAvr63KrO2qdZLQLnystnmnS+4jxzN+l81nmKVl5TgbMGcVwRSBpHmoWXiHgG2BjYC/gb8DQwOS9vAn4C7FJ5okgOJ01+cQxwO/BKLtOL+dif5XN/ucrxFwE7kiYCeS4fN5Cz8zUAHoiIf9bL3EwR8UpE7EUaz/OHpID7M6R79QppzMWTSfdqhYj4e5VzXELq2nwUcBfwOqmV4L2kwOiKEVHZ1blpz9rMzMzMzMysV+QejUdKulTS7ZIelPSJKvnWkvRuSUsN95rNahE4c16+3aTzFecZ16TzWYeJiP1Irb0Ge9ylVJktN3d/PTWnwZ7zfqp3/W3k2AvpHzuvEVNyAhhwkNCImECTWw1GxB3A14dx/JOkbsJfaTB/U5+1mZmZmZmZWTeTtAgpJrBFeTOpt+0cVQ45GtgEeBxYYjjXblaLwBfyct4mnW+evHyxSecz6xTvJ7V07SO1vjMzMzMzM7NRJKK7kw2PpKWBG0lBQJVSPcflPItK2mQ4129WILCYIGSlJp1v5bx8rknnM+sUB+XlxRHxaFtLYmZmZmZmZmYjRtIY4DxgAVJg7y/Ae4BZBzj0QtKwXADvG04ZmhUIvIVUgfdIGlZ3Xkkzk5o7Bv2zwZp1JSUzSJpb0reBTfOuo9pZLjMzMzMzMzMbcR8CViPFvI6LiF0i4l8RManeQRHxJvBvUuxtveEUoFljBF5BmhBgFuDTwLHDONf++TyRz2vWzTZj+vfx2RFxeTsKY2ZmZmZmZu0TwFTPGjya7ZaXT1JlUtIB3AFsTprAc8ia1SLwr6TZQwG+PdRZTCQtAxyaVycBf25C2cw6QR8wkTRb7z7tLYqZmZmZmZmZtcG6pHjwBRExeZDHFsPnzVM31wCaEgiMiOeB40lNFOcGLpK04mDOkfP/LR8fwPER8UL9o8w6W0RcGRGKiLERsVREfH2gJr9mZmZmZmZm1pPmy8uJQzi2CBzONJwCNKtFIMDhwMP59fLAjZIOl7RwvYMkLSzpcNKMKcuSgoAP5/OZmZmZmZmZmfWGCKLLkw1LEcybcQjHzp+XLw6nAM0aI5CIeEnSB4CrSbOdzAJ8HfiapHuBm0jNGF/N++cD1ib1bS5PlfwqsGtEvNSsspmZmZmZmZmZmbXZM6SY2HJDOHbDvHx8OAVoWiAQICJuk7QRaWy/pfPmMcCKOVUjUitAgAeBXSLiv80sl5mZmZmZmZlZJ/BcIaPa9cAywDaSZmx0nEBJqwPrk+Jn/xxOAZrZNRiAHMRbC/ge8FLerDoJ4OWcf20HAbuPpMhp33aXpdNJGiPpc5JukPSKpL58745pd9nMzMzMzMzMrKWKSXHnBb7ZyAGSxgMTSpvOGk4BmtoisBARrwKHSfoJ8F5gC2BlUkVnB14BngfuAi4HLo+I12uczrqYpAmkWXKviojN21uajvBT4AvtLoSZmZmZmZmZjbhzSLGwlYFvSwrgyIh4u1pmSZsCvwBWJbUGvCYirh1OAVoSCCzk4N5fczIb1STNBnwur54NfAN4ivTDXPWH3szMzMzMzHpHAH2ecGPUioiQ9FHgGmA8cChwkKSrStm2lbQKsAmpG3HhJWDf4ZahpYFAM5vGivTPDHR4RNzfzsKYmZmZmZmZ2cjK82vsCJwBLADMDexC//wZW5ayF0PqPQ3sHBETh3v9po8RaGY1zVJ6/XLbSmFmZmZmZmZmbRMRVwNrkrr9TqL2vBpvA78B1oqIfzfj2m0NBEpaS9JnJP2fpE/kpo+jmqQZJG0h6RhJN0t6WdJkSc9I+oekfSWNHeAcc0v6tqTrJD0n6U1JEyVdmieqmDfnm1nSi3myiu8McM535bIMmDfn3zf3dd8nb9qsNKlIkQ4r5T8sb5uY19eQdLKkRyW9LenWUt6ZJe0g6QRJd0p6Led5QtJfJO0yQNkmlq8vaQ9JV+V7MUnSLZIOrnefJS0t6ThJd0l6Pd/jxyXdJOnnkraorBtwZekUD5fuw8Qq5x8raT9JF0t6OtfvaUkXSNq1TrlG8j6OkfSx/L58Or9PX5R0Xy7nQZLmqXP8lpJOy2V7U9JL+T37BUnj6l3bzMzMzMysW0VEVydrjoh4OiIOBuYhtQL8IvAD4EfAV4GdgHki4oCIeKpZ121a12ClWUzGAkTEKwPkXRY4mTT1ceW+a4BPRsQDzSpbl/kccEyV7fMBW+e0l6SdIuKNykyStgNOA+as2LVETlvmcx0WEW9KOh34LPAxSd+L2j/VHyBN9BLAHwZbqcGQtBtwKlArGHQk1SfcWIj0g7KTpFOAj9WpT3GtXwOfqdi8JvBz0vvzo1WO2ZI07uW7KnYtktO7gc3yeQZN0iL5/GtV7Jof2AHYQdJpwL71phpv5X2UNANptqPtK46dM6flclkfA86vOHYm4PfAXhXHjgM2yOnjkraNiCdr1c/MzMzMzMys20XEW8AVObVcU1oESnoX8CzwIvCvAfIuAVxHCrIUfZ3Ly02BK3O+0egtUhBoX9I9WgxYEFgX+DGpyeiWpCjxNCRtDPyFFIh5Gvg8sAKpv/kywIdJM9RMKR12Yl4uTRqIspaiZd9VDfZJPwWYjRSIAvhnXi+nI6ocNxdpWuy7gfeT+ssvDny9lOc1Ul/6DwNrAwuTAnAbk5rMTiEFmQ4aoIwfAz4NHAusQYrCrw1cmvfvKWmH8gGSxgAnkYKAD5AChcvmY1cGtiU17X2mdNgRub7loNkqpfuwcun844FLSEHA54FDSGMLzp3zHQFMBfakynugpNX38eOl+vwSWI8UQFyEFMj7NHA10Ffl2N/m804BjgbWId2/JfJxzwGrA+cM0Cpzf0k3Srrx2edfqpXNzMzMzMzMzLJmtQjcFJiZ1FrsdwPkPZH0pb9oYRSkmVPnKp1jYeBXpBZFo0pE/IYUhKn0NHCjpMuAi4FPSzqsaH2ZA1Qnkp7p/4ANI+Kx0vEvAg8BZ+TWXMX1bpR0Oynwsg8peDON3EKtGKxyQoP1mAK8JqkIOk6NiNcaOHR20lTam1Tkf6cuEfHtGsc+AfxL0i3A8cBXJB1Xp1XgUsBXIuKo0rYXJL0fuA9YlHRPLiztX5UUnAXYLSJuLx9LCrxdXL5Ingb8bUnlFpyTatyPbwIrkYJ0G0fEvaV9LwLflHQ/KRh5iKRjI+LxKudp9X3cLi/Pi4gDqxx/A3BC5YklvY8UgIXUovHU0u4XgBMk/RO4EdgQ2J0UrJxORJxQXGOdNVZy+3QzMzMzM+t4ETC1z19frH2aNUbgBqXX59bKlFusbUF/EPAkYIGIWASYg9Qa6O28b1tJazSpfD0jIv5Ban05CylQUtgGWD6/PqQiCFh5jikVm07Kyw9KmqUyP7A36b3yGnD2UMo9SIc2GDSs5ZS8XJT+e1LNI8DPKjfmLtdFPdet2F1uofbEUAtYTQ7QFt2Uf1ARBCyXbwKpNeIMwAfrnLKV97G4D4O9B0XrwosrgoDviIi7SN3bIbV8NDMzMzMzM+t5klbO8w5cL+luSddI+omkFZp1jWYFAouxzO6LiEfr5Nu79PrKiPhERDwPEBGTI+K3pK6Qhd2aVL6uIml2SV+SdEVpooh3JtkgjfEH0wZn3puXrwHnDfKSp5ACsLORxgKsVLTgOisiXh/kuQcrqGhRV42kBSR9R9K1kp7PE1UU96dcxnqBwEsjolrXVYD783KBiu33Am/m1ydJWmagsg7CWqSWsQD/lDRrrQQULRHXrnGuVt/HW/Py45I+msf9G+haY0ljJwJcNUD97hygfmZmZmZmZl0poruTNU7SJ5UmIz2ggbxfAG4DDiQ1Sloe2Ig0ichtkj7XjDI1q2vwMqTAw00D5Htf6XW18eEgdfU7jBTsWm/YJesyklYmBXAWbSD7HKXXRUDqriot/uqKiOck/ZUUeN2H/pZgSFqP1FUVGuwWPEzPDtSKTdJmpGDnXPXyZXPU2VdvIopJeTnNhCARMUnSt4CjgB2BHSX9F7gGuAq4pAhuD0E5wn9Ng8fMV2N7q+/j0aRxAhcjvV9+nSf6uQa4HPhPlS7ZC5GCzZB+/mv9DiirVT8zMzMzMzOzjiVpUVKMK0jzOfyqTt496O+xGPTPpVGszwQcK+mpiDhnOOVqVovARfLy4VoZJC0ILJlXX6XGbCi5hdblpEqv2KTydYXcNfQcUhDwVVJAdGPS/Z2T/sklim6/5UBuEWB5dYiXLyYNea+kxUrbi0lCHqLx4NRwTDcTcpmkOUn3aC7S2JJfpn+iijlI92H20iH1gt1Th1LAiPgpqUvujXnTKqQuvacDT0o6Jb/fB6te0LKWWjMCt/Q+RsRLOf+vgZfpnwzlSNL4gA9IquzWO5T6DdjS0MzMzMzMzKwDvbf0+qRamfIEvMfk1aA/cHhkPu4l+oODRzfSI6+eZrUIHJ+X9YJQa+ZlAP+u0yUT0vhn0FhLpV6yOf3Bz90i4pJqmSTNXmVzce9nq7KvEReTJhlZhNSF+4j85vpw3j+hzqQbI2l30mQzU4EtIuKeygw5yNVSEXE2cHYO+G1MmnF5J9Lsyx8FNpS0ZkQMJjBb7oo7Vw62tcqw72NEPAUcIOlgUhfeDUmTyryPdB9OlTRnRBT/9SjXb9eIOH+4lTAzMzMzM+s2nixk1FgnL98CqsZ3sg8BRWOit4HtIuLKYqekrwOXkiYvXYTUCOf8oRaqWS0Ci66o76qTZ83S61sHOF/RpbHe+XrR6nn5Yp0g4KJUb1lVBE9XLs8K3KiImAqcnFeLVoA7AXOTgrcnVzuuDYp7dEe14FW26kgVJiKeiohzIuILwLKkvvuQAmF7DfJ0D5Ver1UzV3M07T5GxJSIuCEijomIIhhanPNQSUWT5ifoH1+x1fUzMzMzMzMza6fV8vLGPClpLcUkoAEcWw4CAkTEM/THaSAFAoesWYHAl/NyqTp51im9vmWA882al2/WzdV7im6eY+vkqTWL6mV5OSuwyxCvX3QPXl7ShvS/0a6IiEeGeM7JeVmvToMxnHvUUpEcTf/Pw2C7tl9Pf8vOfZtVrhpadh8j4n/A8Xl1AVK3diLibeDqvP2jQwlYm5mZmZmZmXWJpUjBvdtrZZA0BnhPadPx1fJFxC2kWJqANYZTqGYFAu8hFea91Xbm2UK3KG26foDzFTO1vjD8onWVYozF2fNEDtOQtBzwjRrHXkKa0RbgZ5IWqZGPWgGYiHiA/nEAvwJsl19PqF/suoqJMxYaxjnKinu0oqRlK3dK2hj4VJOuNR1Ji0gaX2f//PR3zx7UpCE5UFZ0o91b0u4DlGV+SUPtPj+s+yhpoCBnMXnN20w7ZMDRpf0/LbUWrHaNcZKWGOA6ZmZmZmZmXSMIIro7jQRJ60j6haS7JL0s6TVJ90k6SdIWA59h0Ne7UlIMIj0w8FmZJy+frpNnJVIMIYCJEfFQnbw35OWwvic3KxD4r7xcRNLHq+z/IGm8vwAejoiak4pka+W8jdzYXnIx/UGT0yXtmQNPi0r6FPBPUivJ6QKkeczFT5C6aS8G3CjpIEnLSZpT0pKSPiDpDOBrdcpQtArclTSG5KukSSWG6ua8XEbSpyXNJ2mGnIby/jsX6ANmBC6UtKOkBSUtJenLwN+B+4dR3oFsDTwu6XhJu0haJt/fJSTtRuq3P4b0HIZy374P3EEKrJ8h6QRJm+ag39ySVpT0YUmnAhPpD7gN1nDv40WSrpN0iKT1JS0gaV5Ja0r6EVBMa35OeRbriPg7/YOkHgxcKmnn/D4v3qfbSzoaeIT+JtJmZmZmZmbW4ySNlfRz4N+k75UrkSayHA8sR+o9d7mkUyXN0raCNmbmvHyrTp5y675/D3C+J/JyqHNDAM2bLOQU+luq/SLPeHJaXt8O+EUp75/qnUjSbPTfiDubVL6uEBEvSjqQFChZCDi1IssrwPuBP5DG7qs8/lpJu5Bmr10QOLbGpe6uU4yz8nHFG+vMiJjUaB2q+CspYLUk8JucCt8lzYzcsIi4T9J3SAGz5fP5y54kTYTx3yGVtjFzAvvnVM0U4ICIGHQZIuJ1SVuTnsMmpFZ5tVrmBf1drwd7neHeRwEb5FTLTaRgX6VPk2Y1PoDUirhqS+Ks3i9MMzMzMzMz6y0nAPuV1v8FXEX6nr0+qXGOSENZzSlp53LjkyY5AnhxgDwD7Yc0YeZsVInflJTHz79tgPMV9RxWLK8pgcCIuEfSH0mzzc4MHJdToej+N4nawanCzqRWSkF/S8NRIyJOlvQYqdXeBqSx3J4A/gH8JCIerNObkoi4UNIypADMDqSJG8aRAjsPAOcBZ9Y5/nVJZ5JaF8LwugUTEW9I2gQ4lDSj7CL0j0831HMeLuke4POkHxoBjwEXkO7R0/Xu0TCdCTxH+uWzAbAwqSv726QWbFcCv4iIesHWunL5NyP9LOyZrzM/qZ7PkoJzFwLnRsTjw7jOcO7jtsA2pCDe8qTA9XhSd+jbSfdpQrVfyBExGficpN+Tgqmbk94XM5OmRb+fNOblORFx61DrZ2ZmZmZm1nECRqh3bdeR9CH6g4BTgX0i4tSKPFuRZswdT5o042DgZ00uym8jYmITzvMMKRC4Wp085cY1Nw1wvmJosNfq5hqAmtW/W9LspCDImqQgXhFBKF4H8PGIqDv7rKTLSOMJ9gELRcSzTSmgNUzSL0hNcB+IiOXaXR6zgayzxkrx74tPGjij2WgQfe0ugVln6Xu73SUYvj7/XJtZZ1lvh89y4+33tqz1RS+bbbEVY+0vnThwxg521SEb3xQR6wycs3F56LAH6J+E9oiI+GaNvPvTP6nGS8ASEfHKMK9/JVDM1bBUMwKBks4CdiMN8bZYRDxfsX9+4H+kSTynAnNHxKvTnag//xmk4bPuiohVh1quZo0RSL7pmwA/J3VhLQi4D/hAA0HAdeifVOTfDgKOPEnjgI/k1QltLIqZmZmZmZlZz+nri65OLbIp/UHAScBRdfKeCBS94+Yk9abrRBfn5Tim7TVb+A4pCBjA1fWCgNm6Oe+9A+Srq1ljBAKpWylwiKSvkgZxnB14ahCR1IdIA0FCiurayNuT1H99Cv2TOpiZmZmZmZmZtcqupdeXRETNMfgiYoqk84CD8qYPAH9sZeGG6EzgR6Rg5YfyMG6n533bkoYcK/y+3okkLU+aeyHon5R1SJoaCCzkMcDuGsJxL1BlRlxrLUljSeMyrg8cmTefHhFP1D7KzMzMzMzMzKwp3l16/c8G8l9DfyDw3fUyDsEvJK1EmhOgj/6x8C8DToqIlxo5SUS8IukrwO9IAbx1cpomWz533Yl1gQ+XXjdyf2pqSSDQus6DwBKl9RfpnwXazMzMzMzMzJqkWXM19JhVSq/vbyD/A6XXi0uafbjjBJbsULE+C7BY3v49SV+PiF80cqKIOFHSgsB3Sd2AKz0K7BZRe6BvSTMCn8qrrwLXNnLtWpoSCJS0aX75YET8rwnn25DcRDIivjfc81nDXgCuA/5vOLPRmpmZmZmZmZk1Is9VMFdpUyPxiMcq1hdg2vkqhuoB4AbS0HWTcrnWBjYnBfJmBY6TtHxEHNzICSPiCEl/A/YntQicHXiKNIbgLxoYG/A9pHvyOHBNREwZbKXKmtUi8EpSc8avUGPaZkl7AJ8BIiK2HOB8GwGH5XM6ENhiEbFku8tgZmZmZmZmZqPSbBXrrzdwzKQBzjFYfwQ+FxH/rbZT0lKkcfyKCW4PknRzRExo5OQRcStwwFAKFhFXABsO5dhqRrJr8GKkCKrbwJqZmZmZmZnZqBNBK2feHSnzSrqxtH5CRJwwjPO9q2L97QaOebNifZZhXJ+IqDtZR0Q8LGkb4B+k2BbADySdFhGNlLdjeIxAMzMzMzMzMzNr1HMRUTnpxTQkrQJsN8B5Tsjj+r1RsX2mBsowc8V6ZQvBpouIyZI+S5ocV6TJRDYhTSLSNRwINDMzMzMzMzOzZloX+MkAec4mjetXOUbe+AbOX9kCcKBx9poiIu6RdDuwRt60EQ4EmpmZmZmZmZlZNZ41eFoR8Zakl4A586ZFgJsGOGzRivWnm1yseu6nPxC44AhetynGtLsAZmZmZmZmZmbWOyJiQkRogDSxdEh5ko7lG7jEsqXXj+UuxiMlarzuCg4EmpmZmZmZmZlZO5VbAG7cQP5NSq9vbnJZBlIOQj4zwtceNgcCzczMzMzMzMxGSER3pxY5v/R6a0lz1sooaSywS2nTua0pUtVrLw+sWdp0/Uhdu1kcCDQzMzMzMzMzs3a6GpiYX48HvlQn78eBxfPrl4E/t65Y/STNAPyKNGMwpHEJrxqJazeTA4FmZmZmZmZmZiMgCPr6uju15L5ETAW+Udr0NUkfqcwnaUvg6NKmwyPi5WrnlLSkpCilzWvkO1XSIZLmq1U+SUsAFwFbljYfGhFv1axUh/KswWZmZmZmZmZm1lYRcbqkbYGPkeJVp0k6gNTqbiqwPvA++lvkXQz8vAmXXgTYE/iJpJuA24AngTdIMxmvDWzOtDG04yPihCZce8Q1OxD4Pkmz1ti3UfFC0qEDnGejAfabmZmZmZmZmVlv+QTwKnAAKeD3npwqnQF8IiImN/HaY4H1cqrlFeAbEfHLJl53RDU7ELh1TrUUbUi/0+TrmpmZmZmZmZl1toBo4Ywb3S4ipgAHSvoDaSzA9wILk4J0TwLXAidHxGVNvOzHSDMVrw+sBSwIzAvMDrwOPAvcAlwGnBYRrzbx2iOu2YFADZzFzMzMzMzMzMysuoj4D/CfYZ5jIg3EqSLiUeBR4PThXK9bNCsQeDX9rf3MzMzMzMzMzMyswzQlEBgRmzfjPGZmZmZmZmZmvaxVM++aNWJMuwtgZmZmZmZmZmZmredAoJmZmZmZmZmZ2SjQ7MlCzMzMzMzMzMysBk8abO3kFoFmZmZmZmZmZmajgFsEmpmZmZmZmZmNgADCTQKtjRwINLPuJ6GxM7W7FBZ97S6BmTWbeqDzSPjzoSPE1HaXYNiir/vr0AvPAYCpk9tdguGLKe0uwTA5kGXWrXrgrzszMzMzMzMzMzMbiFsEmpmZmZmZmZmNhIC+PreotPZxi0AzMzMzMzMzM7NRwIFAMzMzMzMzMzOzUcBdg83MzMzMzMzMRognDbZ2cotAMzMzMzMzMzOzUcCBQDMzMzMzMzMzs1HAXYPNzMzMzMzMzEZAEPT19bW7GDaKuUWgmZmZmZmZmZnZKOAWgWZmZmZmZmZmIyE8WYi1l1sEmpmZmZmZmZmZjQIOBJqZmZmZmZmZmY0C7hpsZmZmZmZmZjZC+vrcN9jaxy0CzczMzMzMzMzMRgEHAs3MzMzMzMzMzEYBdw02a4CkxYGPAlsCywPzAgJeBO4GbgDOjoibqxy7L3ASQERohIpsZmZmZmZmHSZw12BrLwcCzeqQNBNwJHAgMFOVLAvl9F7g65KuAw6sFhA0MzMzMzMzM2snBwLNapA0K3AhsGne9BDwa+BK4AlgMrAgsCGwG/C+/PoDgAOBZmZmZmZmZtZRHAg0q+14+oOAvwK+EBGTK/I8C9wBnCBpo3yMmZmZmZmZ2fQCwj2DrY0cCDSrQtI2wJ559dyI+NxAx0TEvyRtALy7pYUzMzMzMzMzMxsCzxpsVt1X8nIq8PlGD4qI1yPimkbzS9pcUuS0ZJ18V+Y8E+rkmUXSIZKukPS0pLckPSbpaklflbRYjePmkHSopJskvSzpDUkPSfq9pFUGKP+akk6UdL+kSfnYRyVdL+mHktapc+zCkn4k6bbSdR+Q9GtJS9e7rpmZmZmZWbfq64uuTtbd3CLQrIKk2YDN8+olEfF4G4vTkBxwOx9YpGLXojltAqwM7Ftx3JrARaSxDsuWymkfSYdExHFVrrk3aTbksRW7FstpfWBFYJcqx+4O/AGYpWLXMjntK2mviDin8lgzMzMzMzMzGxq3CDSb3ob0B7eubWdBGiFpOeAyUhDwVeDbwGrA3MASwM7ABODNiuPmA/5OCgK+ChxCCv7ND+wE3Em6D8dK2rXi2LlIE6eMBW4kBfuWAubN1y6u+XKV8m4FnEEKAl4DvB9YOB+7JWkylpmB0yStNZR7YmZmZmZmZmbTc4tAs+ktWXp9b7sKMQi/BmYHXgPeExG3l/a9CDwK/EVS5c/7ocACQB+wQ0WX5gsk/RO4AVgeOE7SXyNiSt6/CTCe1HV6m4h4oXTs86Qg4l8qC5rL8HvSPyEuBraPiL5SlsslXQX8A3gvcASwXWO3wczMzMzMrNMF4dlCrI3cItBsenOVXr9SK5OkcZJmrZZGoIxFGVYitaID+F5FEHAapSBeEZD7WF49vdq4hhHxEvC1vLoIsE1pd9Fi8nXgpUEUeWdgcSCA/SqCgMV1pwLfyavbSJp7EOc3MzMzMzMzsxocCDSbnkqv6/2r5vekLrXV0kh5b+n1Hwdx3GqkVoQA9cbhuwB4I7/epLT9dtK9mR34raSFGrxuUd67gVfqBFKLlpjCszCbmZmZmZmZNYW7BptNr9zNdY62laIxy+TlMxHx1CCOW6L0+q5amSJisqT7gdVJLfmK7Q9K+iVwILAfaXKPW0hj/l0FXBoRr1U55Qp5uTKNB0znazCfmZmZmZlZR4vAM+9aW7lFoNn0JpZer1ArU0TsFREqEvD1lpdserPl5WBbIZa7L1cL2JUV556tYvvBwGdIrfvGAGsDXwDOA56RdFyegblsKIHVcUM4xszMzMzMzMwqOBBoNr3rSZNgAGzc4ms1+q+gWq13awXpBlIO/g00pmGxf5pgYyTHR8TKpBmDPwocDzwBvIvUWvASSeXfM6/n5Z/LQdQB0oRB1s3MzMzMzMzMqnAg0KxCRLwCXJlXt5a0aAsv92bp9bvq5Ks1Bt8DeTm/pAUHcd2Jpdcr1cqUJxVZPq8+UitfREyMiNMi4jOkbsdH513rM+0kIw/l5VqDKKuZmZmZmVnPiIiuTtbdHAg0q+4neTkW+HkLr1Me12+5ahkkLQMsXeP4y0uv9xrEde8AXs6vd6uTbwf6A5T/bOTEeXbi75U2rVh6fUleLi5pi0bOZ2ZmZmZmZmbN4UCgWRURcTFwel79gKRfSpqxBdd5BHgmr360cn/uVntUnePvAS7Nq9+WtGqtvLl1X3HcVODkvPoRSRtVyT878KO8+jhwcWnfUpJmqnUt+icxAXi+9Poc4LH8+nhJC9Q5B5JqjtFoZmZmZmbWlaLLk3U1BwLNatufNAsuwAHAvZK+ImldSQtJmkPS4pLeJ+l3wDdy3sH+aiwCcntIOkrS0pLmlrQJcCGwHWncvVo+C7wCzA78U9I3JK0saU5Ji0raIZfvuIrjvg88TWr1eJGkgyUtIWk+SdvnuheBuINzS7/CPsAjko6WtH0+bs5c9n2Ac3O+V4GLioMi4m1gX9IYjMsBt0j6gqSV8vELSlpf0iGSrgfObvgumpmZmZmZmVldtSYgMBv1IuI1SVuRWsUdQJoQ48cDHHYdcMggL3U4qQvuSsCXcipMJgXO9gcWrlHOByRtDfwZWBD4QU6V/lBx3LOStiUF6hYkdYGu7AY9FTgkIs6rcr4FSbMEf6FGvSYBe0bEsxXXvVzS+4FTSWMfHl3t4OzmOvvMzMzMzMzMbBAcCDSrI7dgO0TS0cDewFaklmzzkFr+vQjcR5pp+IyIuHUI13hZ0sbAN4FdgUWBl4CrgR9GxE2S9h/gHP+WtDwpYLkzaVy+8aQWfw8Df6W/q3P5uFslrUgK5u0MLAvMRGqBeAVwdETcWeWSxwD/Jd2PdUgBvfmAN4AHSWMBHhcRj1U5loj4m6SlgM/QHwSdMx//GPAfUqvCi6sdb2ZmZmZm1q36+vraXQQbxeQZX8ys262z5srxn0tOaXcxLPwHjVnPUQ+MIuPfTZ0hpra7BMMWfd1fh154DgBMndzuEgzfNKPudJ/1djyQG2+/T+0uRzcat8ByseCHftbuYgzLo8e9/6aIWKfd5bCh6YG/7szMzMzMzMzMzGwg7hpsZmZmZmZmZjYCIgL3zLR2cotAM+tKkvaXdKOkG599/sV2F8fMzMzMzMys4zkQaGZdKSJOiIh1ImKd+eaZq93FMTMzMzMzM+t47hpsZmZmZmZmZjZCos9dg6193CLQzMzMzMzMzMxsFHCLQDMzMzMzMzOzEeLJQqyd3CLQzMzMzMzMzMxsFHAg0MzMzMzMzMzMbBRw12AzMzMzMzMzsxHirsHWTm4RaD1H0mGSQtLEdpfFGiNp3/zM/IloZmZmZmZm1iIOBJqZmZmZmZmZmY0C7hpsZmZmZmZmZjYSAqLPHaGsfdwi0MzMzMzMzMzMbBRwINB6hqTN8xhz38mblijGnSulCTnvNGPSSVpK0q8kPSjpTUkvlc47g6QtJB0j6WZJL0uaLOkZSf/I5xpbp1xXVlx7a0kXSXo2X+tuSYdKeledcywg6YeSbpX0iqS3JT0p6TZJJ0japcoxE/J1ryzdn79KelrSG5LukfR9SbMOcF/HStpP0qW5zMW1z5O0Y53jNi/d9yUlzSfpJ/m6r+fta+ZncFLpuMpndmW98pmZmZmZmZlZY9w12EY9SRsDFwJzlDa/WXr9OeCYKofOB2yd016SdoqINwa41teAIwCVNq8IfBfYUtKWETGl4pjVgCuAeSpOt2BOqwN7AHPWue4BwHFMG/xfAfgWsIekzSPiySrHzQVcAGxU5dq7ALtIOhX4eERMrnV9YHlSsG/hOnnMzMzMzMx6WhCeNdjayi0CrZdcA8wGHJnXH83r5fTpKsedAbwA7AksQgpWfay0/y3gr8C+wPrAYqRA2LrAj4FJwJbADwYo32akIODpwHqkwN4qwGl5/6Y1yvebnPcZYH9SUG2evNwS+GGuay3LkQKZ/8755yMFH38E9OXznC2pHJwkr59FCgIGKZC4OjAvsCFwfs76UeCoAep+IjAj8BlgCWABYCfgXtJz+Uwpb+Uz226Ac5uZmZmZmZlZA9wi0HpGREwFXpP0dv+meK2BQ2cE1omIp0rb/lI6729IwbhKTwM3SroMuBj4tKTDIuKVGtdZEvhlRBxY2vaCpL1Iwbh1gH2AXxY7Jc1Of2u8T0XEX8rHAvcDlwNfr1O/hYGbgfeWWiw+B3xN0jPAT/M1dicF/gofIAUOAb4VEUeU9j0v6QOkoOaHgIMk/SYi7q5RhnmAtSPirtK2C0r1fKt43eAzMzMzMzMzM7NBcotAM/hxRRBwUCLiH8CzwCyklnK1vA58o8rxAZySV9eUNGNpd3nswSeGWkbg6zW6LR8DPJxf71uxb7+8fITUenAaudyfByaTujrvV5mn5HcVQUAzMzMzM7NRqa+vr6uTdTcHAs3gooEySJpd0pckXZEn23i7PKEFqbstpJZ9tVxfp7Xg/Xk5IzBXsTEiXqS/2+9xktYYqKxVvAZcVm1HRPTR3/pxo6J7cF5unLf/Obe2rHb808DVeXWTOmUY8B6bmZmZmZmZWWu5a7BZf4u4qiStTOr6u2gD55qjzr7pJuMomVR6XTl78JeAM4ENgFslPQRcRQrA/SMiBmopeH+tQF52T17OCcwOvEyqR1GXgVry/ZfUhXjxOnnq3mMzMzMzM7NRIfBkIdZWbhFoo169mX4lzQCcQwoCvgocRmoptwgpcFZMaPFYPqRecL1eMG6ay1aU72xgK+BK0uQeSwMfJ83C+5ikCyQtV+d8rw9wvfKYfLNWLCv3V/NqXs5WJ0/d2ZTNzMzMzMzMrPXcItCsvs1JM+wC7BYRl1TLlCf1aJmIuBy4XNLcpIk9NgZ2AFbLyw0lrRkRj1U5fPwAp68W9KsWHBzo+Ffr5jIzMzMzMzOztnKLQLP6Vs/LF+sEARelfpfgpomIFyLigoj4ekSsDuxBaiU4N3BgjcOWkzS2xj7oD3S+BBRjGL6c1wFWGqBYq+TlIwPkMzMzMzMzG/WiL7o6WXdzINB60eS8rBf8atS4Bs61ZxOuMyQRcRZwR15dsUa2WUlj+E1H0hjg/Xn1X3km4GJG4Gvz9p1rBRIlzQ9smlf/ObjST6N4ZgwQtDQzMzMzMzOzIXIg0HrR83k5bx7jbziKSS5ml7RZ5c48Nt83hnmNmiTNK2meOvtnJo1XCP31rubInLfSF4Cl8usJFftOzMslgS/XOO/RwExAlPIPRbnsCw3jPGZmZmZmZmZWgwOB1otuzsuZgUMlLShphpwG+56/mP6x706XtKekRSQtKulTpFZwbwIvNKfo01mVNCHIKZI+LGkFSXPl628H/AOYN+c9o8Y5niCNJXi5pC0kzSNpeUlHAj/Jef4FnF1x3HnAZfn1kZKOkbSKpLklrSfpHPpbQx4XEXcPo563kro4AxwmaTFJM+Zn5haCZmZmZmbWE4IgoruTdTcHAq3nRMS/gevz6reBJ0ldTyczyFZrEfEiaey9PlJLtVOBx0mzBJ9ACjZ+iNZOlPEu4KPA6cA9pKDjY8DfgE1IrfG+HxEX1zj+fuAQYAPgcuA54F7ga6TfAfcDu0fFb/S8vgcpSCjg88CdpNZ7NwAfyFlPo3aLwYZExFPAWXn1E8CjwNukZ3ZZrePMzMzMzMzMrHEOBFqv2h74GSlw9uZwThQRJwNbkVrfvQK8ReoyfDzw7oi4anhFretfwNbAj0hj9j2Sr/8GKYA3AdgwIg6td5KI+GU+z9+AZ/M57gN+QKrDkzWOe4E0BuAnSEHE50nBuaeAPwM7RcRHI2JyteMHaV/gMOB2YFITzmdmZmZmZmZmJcMdP82sI+WWfF/Kqdr+CUw/Jl69810BXFFn/5J19m3ewPmvJLW6q9z+NnBpTsMSEZcxhNZ1ETGV1JJysK0pr6RKnerkfxP4bk5mZmZmZmY9yd1rrZ3cItDMzMzMzMzMzGwUcItAMzMzMzMzM7OREBB9bhFo7eMWgWZmZmZmZmZmZqOAA4FmZmZmZmZmZmajgLsGm5mZmZmZmZmNEE8WYu3kFoFmPSoi9o0INTJrsZmZmZmZmZn1PgcCzczMzMzMzMzMRgF3DTYzMzMzMzMzGwFB0NfX1+5i2CjmFoFmZmZmZmZmZmajgAOBZmZmZmZmZmZmo4C7BpuZmZmZmZmZjYTIyaxNHAg0s+43eRLx9C3tLsXwqAcaaGtsu0tgvaRvcrtLYL3i9UntLsHwTZnS7hIYQPTAmF59PRJ96Jva7hIMX7ePEfdWD/xuNRulHAg0MzMzMzMzMxshET0SlLeu1ANNUMysGSRdISly+kHFvs1L+4aSriyd68oaed6U9Jik8yXtLkkjfhPMzMzMzMzMepgDgWaGpMWATUubPtqGQNw4YFFgZ+As4CJJ7xrhMpiZmZmZmZn1LHcNNjOAjzLtPwaWIAUGr8rr1wCz1Tj2G8DX8+tVgEer5Kk2kMujOX/hXcB6wA+ANYBtgJ8D+w9cfDMzMzMzs+7Q1+1jRFpXc4tAMwPYKy+vBV6q2EZETI2I16ol4O3SeSbVyPdGlWtGRZ5nI+JCYAvguZxnP0kLN7eqZmZmZmZmZqOTA4Fmo5yktehvmXcicHZ+/UFJ40a6PBHxIvCLvDoW2Gyky2BmZmZmZmbWi9w12Mz2zss3gXOAh4BPAnMAO9EfGBxJ/y29XrQN1zczMzMzM2uB8KzB1lZuEWg2ikkaC3w4r14QES+TxgV8LG/bu+qBrVceU9CzB5uZmZmZmZk1gQOBZqPbVsBC+fUpkAbuA07L27aTNE8byrVS6fUTbbi+mZmZmZmZWc9xINBsdCsmBHkBuKi0/ZS8nBH40EgWSNKswOfyagBXj+T1zczMzMzMWiUCoi+6Oll3cyDQbJSSNB7YNa+eGRHvzP4bEXcCt+XVEekeLGlmSZsAFwPFTMFnRMSjI3F9MzMzMzMzs17nyULMRq8PAOPz61Or7D8VWAPYQNKyEfFAk6+/hKR6/066HvhMk69pZmZmZmbWVp4sxNrJLQLNRq+iW/BE4Noq+08D+iryttorwJXA/sCmefISMzMzMzMzM2sCtwg0G4UkLQhsmVevAdaQqk7OewepVeBewGFNLsajwCql9bciYnKTr2FmZmZmZmZmmQOBZqPTnsDY/HpvBh4HcBlJG0bEdU0sQ0TEa008n5mZmZmZWedz12BrI3cNNhudhjIByIhMGmJmZmZmZmZmreFAoNkoI2kVYM28+rWIUL0EnJHz7iFpxrYU2szMzMzMzMyGzV2DzUafomVfAH9qIP/pwIeAeYDtgT+3qFxmZmZmZma9LQL6pra7FDaKuUWg2SiiNCPInnn1uoh4pIHDLgJeyq/dPdjMzMzMzMysSzkQaDa6bA4sll+f3sgBEfE2cG5e3VHSnM0vlpmZmZmZmZm1mgOBZqNL0aJvKnDWII4rgobjgA82tURmZmZmZmajSfR1d7Ku5jECzUaRiNgP2G8Ix10KqMa+w4DDBnGuzQd7fTMzMzMzMzMbPgcCzczMzMzMzMxGhCcLsfZy12AzMzMzMzMzM7NRwIFAMzMzMzMzMzOzUcBdg83MzMzMzMzMRkLgCTesrdwi0MzMzMzMzMzMbBRwINDMzMzMzMzMzGwUcNdgMzMzMzMzM7MR4VmDrb3cItDMzMzMzMzMzGwUcCDQzMzMzMzMzMxsFHAg0KxLSZooKSQd1u6ymJmZmZmZWYMiujtZV3Mg0KzD5OBeSNq33WUxMzMzMzMzs97hQKCZmZmZmZmZmdko4FmDzczMzMzMzMxGSvS1uwQ2irlFoJmZmZmZmZmZ2SjgQKD1PEmbSfpTnlzjTUmvSXpY0tWSviNpxZzva3lsvlcljR/gnN+qllfShLz9yry+rqSzJD2Zr/2gpJ9KmqvKOa+UVB559aTSeIFFWrJOmfaQdJWkFyVNknSLpIMlja1zzGq5LldLelbS5Hz8DXn7nAPchzUlnSjp/nzNNyQ9Kul6ST+UtE6dYxeW9CNJt0l6OR/7gKRfS1q63nXNzMzMzMy6UgT0Te3uZF3NXYOtp0n6BvCDis3jgPHAksAmwFzAF4A/AIcDswK7ASfXOfXH8vLsiHi9xrX3Ak4EZixtXhr4IrCdpA0j4uVBVKcmSb8GPlOxeU3g58D6wEerHLMGcGuV080JrJfTJyS9LyLur3L83sBJQGWgcbGc1gdWBHapcuzupPs9S8WuZXLaV9JeEXFOlfKZmZmZmZmZ2RC4RaD1rNzS7/t59RLgfcDiwPzAWsAewFnAGwAR8STw95x/3zrn3QhYLq9OqJFtOeB3wOXAZsC8edsxef9KwLcqjtkOmK20/pm8Xk6PVLnWx4BPA8cCawDzAGsDl+b9e0raocpxAVwPfBl4DykANy+wGnAw8CgpWPonSSofmFs0/poUBLyRFOxbqnT8zqR7M12gU9JWwBmkIOA1wPuBhfOxWwJXAjMDp0laq0q5i/PsL+lGSTc+++KrtbKZmZmZmZmZWeYWgdbL3kcKdj8N7BARk0v7niW1hjur4pgTgR2AzSUtERHVAm/75OVDwNU1rr0w8Bdg14h3RoJ9HjhE0mKkFocfA75SHBARbwCUYm5vRcRrA9QRUgDuKxFxVGnbC5LeD9wHLJrLfGH5oIi4HdiwyvmeB+6UdBZwF/BuUoDu0lKeTUitKqcC20TEC5XH5/pPQ9IMwO9Jz+ViYPvS/QG4XNJVwD+A9wJHkAKk04mIE4ATANZZZcmolsfMzMzMzKzjhL++WPu4RaD1sqLL6nMVQcB6/koKEgrYu3KnpJlJLQkB/hBR9zf4lyqCXIU/5uX8kpZosFz1PAL8rHJjDiyenVfXHexJI+Ip+oN/W1bsLu7t68BLgzjtzqRWmQHsV+3+RMRU4Dt5dRtJcw/i/GZmZmZmZmZWgwOB1stuzctVJP2g2gQdlXLA8JS8uk+VLDuTxtAL0hh3tTwYEQ/U2Fceb2+BgcrUgEtrBBzL16p6HUljJH1E0vl5ko83ypOTAB/MWZevOPR20j2YHfitpIUaLOt78/Ju4BVJs1ZLwL1FEUktEs3MzMzMzMxsmBwItJ4VEVeQWvgBfAN4RtK1ko6UtK2kcTUO/X1eLitp44p9RXDwyhrdhgtP1tk3qfT6XXXyNaqRa013HUmzAVcAp5ECnIuRxuarZo7ySkQ8CPwyr+4HPJ7H6zta0i45mFfNCnm5MvBqnfRM6Zj5atbOzMzMzMysq3jWYGsvBwKt1+0OfJ3UfXYGYCPga8BFwFOSvitppvIBEfFf4D959Z1WgZIWJI07CLUnCSk0+ttRA2cZ0FB/Ex8NbEpq2fc7YFvSeINz0z85yWk5b7XxRA8mTWhyN+l3ydqk2ZfPIwVdj8vBxrI5GLxaAVszMzMzMzMzGwQHAq2nRcTbEfHDiFiSNFPvfsDJwAukLr6H0t8VuOzEvNxDUtGabi/S2HivAue0sNgtJ2k8qT4AR0bEpyLi4oiYGBEvRsRreaKS8bXOEcnxEbEyKYD4UeB44AlSC8QDgUsklX/PvJ6Xf44INZgmNLn6ZmZmZmZmZqOSA4E2akTEPRFxUkTsQ5pJ98y864OSVqrIfjrwBqkF2y55W9E68KyIeJ3utgL9Le3OrJNv1UZOlgOIp0XEZ4AlSK0NAdYHtillfSgv1xpEWc3MzMzMzHpDANHX3cm6mgOBNirlGXWPLG1asWL/y8C5eXVfSe+mPyg2ocXFm5KXY+vmGp5yd9uq15G0HrDMYE8cEVOA75U2le/tJXm5uKQtBntuMzMzMzMzMxs6BwKtZ0larqJbaqVykOv5KvuL7sFbkSYbgTQb8DXNKF8dRVkanYl3KCaWXu9UuVPSLMCvah0saanKsRUr1Lq35wCP5dfHS6o7a7KkFertNzMzMzMz6zp90d3JupoDgdbLvgncL+lwSVtKWlTSXJKWl/Q54Lc536PAdVWOvwJ4mPRzslveNqHVhQZuzst9JK0vabykGSRVm7BjSCLiSeCfefUbkr4haVlJ80naPu9bC7ivxin2AR7JswRvL2kJSXNKWlrSPvS3pnyVNDFLcd23gX1JE5wsB9wi6QuSVsrHL5jrfIik64Gzm1VnMzMzMzMzs9GuaYEFsw61NCkg+M0a+58DPhgRkyt3RERImgB8t9hEmmik1Y4DtgOWBa4v75C0VERMbNJ1PgtcQ5o05Qc5FQL4MrA6sHyN4xckzRL8hRr7JwF7RsSz5Y0Rcbmk9wOnklo9Hl3t4OzmOvvMzMzMzMzMbBAcCLRe9n/AZcDWpIDWQsDcwGvAvcDfgF9GRLVuwYUJwHdIrQIvj4hHW1lggIi4SNKOwMHAu4G5aMF4gRFxp6R1SPXbGpiHFBi9Afh5RFyZA6HVHAP8l9Rteh3SvZ2PNMHKg6SxAI+LiMeqHRwRf5O0FPAZYAfSjM5z5uMfA/5DalV48XDraWZmZmZm1jkC+qYMnM2sRRTh/t1mtUhaEHicFIjbOyJOaXORrIp1Vlky/n3Gt9tdjOGpO5xll1Ar57exUadvuobaZkPz+qR2l2D4pvgLY0fohZkye2Vsr76p7S7B8PV19/tpvc/+khvvfVztLkc3GjPbwjFuzU+2uxjD8uY/v39TRKzT7nLY0PTAN0+zltqLFAR8hf5x78zMzMzMzMzMuo67BpvVkCfn+ExePTUieqBJgZmZmZmZmbVNAO6ZaW3kFoFmJUpmkLQAadKOZUgz3B7T1oKZmZmZmZmZmQ2TWwSaTWsf4KSKbUdHxH3tKIyZmZmZmZmZWbM4EGhW3RTgYeC3wM/aXBYzMzMzMzPrCdEbE95Y13Ig0KwkIiYAE9pcDDMzMzMzMzOzpnMg0MzMzMzMzMxspERfu0tgo5gnCzEzMzMzMzMzMxsFHAg0MzMzMzMzMzMbBdw12MzMzMzMzMxsRAREtLsQNoq5RaCZmZmZmZmZmdko4ECgmZmZmZmZmZnZKOCuwWZmZmZmZmZmIyGAvqntLoWNYm4RaGZmZmZmZmZmNgo4EGhmZmZmZmZmZjYKuGuwmZmZmZmZmdmICIi+dheiY0laAnh3Tmvn5QKlLEtFxMQWl2EdYF/gvcAiwFjgCeBa4OSIuKKV1281BwLNzMzMzMzMzKytJP0J+FAbrz8W+BlwEKCK3cvltK+k04BPRcSkES5iUzgQaGZmZmZmZmY2UjxZSC0zV9n2AjD3CF3/BGC/0vq/gKuAKcD6wNakAOGewJySdo6IKSNUtqZxINDMzMzMzMzMzNrtaeBvwM1FiohHJEWrLyzpQ/QHAacC+0TEqRV5tgLOB8YD2wMHk1oQdhUHAs3MzMzMzMzMrK0i4tPtuK6kMcCRpU0/qgwCAkTEpZK+CByfN31b0u8i4pWRKGezeNZgMzMzMzMzM7OREEBEd6fesymwVH49CTiqTt4Tgcfz6zmBnVtXrNZwINDMzMzMzMzMzEarXUuvL4mIF2tlzGMCnlfa9IGWlapFHAg0MzMzMzMzM7PR6t2l1/9sIP81NY7tCh4j0MzMzMzMzMxsRAREX7sLYdNapfT6/gbyP1B6vbik2btpnEC3CDQzMzMzMzMzs1FH0jhgrtKmx2vlLXmsYn2B5pWo9RwINDMzMzMzMzOz0Wi2ivXXGzhm0gDn6GiK3pzxxcxGEUnPAo+0+DLzAs+1+Bqt5jp0BtehM7gOncF16Ay9UAfojXq4Dp3BdRjYEhExXwvP37Mk/Z30fLrZzMCbpfUTIuKEVl1MUjlwtVRETGziuRcDHi1tWiYiHhrgmDHA1NKmTSKikbEFO4LHCDSzrjcSf4RIujEi1mn1dVrJdegMrkNncB06g+vQGXqhDtAb9XAdOoPrYK0UEdu2uwwjQdIqwHYDZDuhA8bWe6NifaYGjpm5Yr2yhWBHcyDQzMzMzMzMzMyaaV3gJwPkORtodyDw1Yr18Q0cM8sA5+hoHiPQzMzMzMzMzMxGnYh4C3iptGmRBg5btGL96aYVaAQ4EGhm1piWjXkxglyHzuA6dAbXoTO4Dp2hF+oAvVEP16EzuA5mwxQREyJCA6SJ7S5n9t/S6+UbyL9s6fVjHdC9eVA8WYiZmZmZmZmZmXWkVk4Wks//c+DgvHp+ROw6iPx/johdmlmeVnOLQDMzMzMzMzMzG63OL73eWtKctTJKGgvsUtp0bmuK1DoOBJqZmZmZmZmZ2Wh1NTAxvx4PfKlO3o8Di+fXLwN/bl2xWsOBQDMzMzMzMzMz6ymSlpQUpbR5tXwRMRX4RmnT1yR9pMr5tgSOLm06PCJebmaZR8IM7S6AmZmZmZmZmZmNbpJWAb47QLZfSZpUse0XEXHlcK4dEadL2hb4GClWdpqkA4CrgKnA+sD7AOVDLgZ+PpxrtosDgWZmZj1OkqLLZwfr9jpIGpv/24ykMRHR1+4yDZaktYHlgDO6+VmYmXWibv+cM2uS+YDdBsizXZVtFzTp+p8AXgUOIAX83pNTpTOAT0TE5CZdd0S5a7CZ9TxJGjiXNUvl/e6l+99tdZG0sKRZIyK6rewFSRtIWqebvxxJWgL4uKRdAbo0CLgF8B/SH8ZztLk4o1Iv/261kdXr76Vuq08vfFab9YqImBIRB5Ja//0auJcUGJwEPAicDGwVER+OiNfbV9LhURf/XW1m9g5JiwCLAlsCzwLPA9dGxNN5f1e0wOml/wZLGg+MjYhXStu6on6SZgZmBdYGXgRejYi721uqwcljmBwL/B44PiJe75b7X5D0HtLgzQBLRcQj7SzPUEhaB/gpsC4wM7BHRJzd3lINjqTNgEuBsXnTZyPi+DYWadDyZ8TSpFn+XgQeJ7VsfKOd5RqK/PtpbDd+AZE0U0S83W2/iyp1e/kL3fw5Df6sNjMbKncNNrOuJ2l90qCty5CakwepKfe1km4BvhYRleNIdBRJawH/i4hnuvkPQEkrAO/PaT5gdkkXABdHxDnFf7s7uX6SVgcOBLYgvacmAy9I+llE/CTn6fQ6bAH8g/RzsA/wlqQ/RMRrnV72gqRNgCvz6uldGgTciNRVZU7gWuCyvOwa+TlcTnovvQDMDewp6e/d8kwkrQv8GFgRWKC0awtJv4iI/7SnZI3LYyZ9GNiK1CJzZkl/I/3D6/RSvo79+Za0MbCHpKMi4rFOLmstvfBZ3Quf0+DPajOz4XCLQDPrapI2JA3UOitwP+kPwUnAOqVs/wa+DdxQ/q93p8itba4AbgO26dYvGPlZnEhqdTMjaVDdscCUvDwkIo5tXwkHlr+ongUsmDc9Twp8FF11vhkRR7ajbIMh6efAQcDbwEzAf4HfAF3xBaMUBBTw24j4dN7eFS174Z2AwSWk98/JwNeA5yJiSrfUo+I5nAC8BnyR1EXmgxHxj06vS25VehEwHngCeB14C1g1Z/k9sH+H/zxsBJwJLJw3vQG8q5TlZNLvrb9HxNRO/Pkufc69DfwK+GlE/K8Ty1pLL3xW98LnNPiz2sxsuBwINLOuJWl5UmubZYGTgCNI3YKnAjsAewMbk1rj3Af8AjgzIp5pR3mryS1VriJ1GQS4htR18Olu+gMw/1F+GekP2WtIf8w+AGxGGmB3rpx1v4iY0I4yDkTSBqQveeNI76vzSS2h3gtsDXwoZ906Ii5rRxkbJekjwATgHlLAQ8DtwG/p8C8YPRIEnJ0UONsD+BOwV1H2Tr3vlSqew+8j4lOSZgNuILWs+zcpGPJy+0pZX+6WfTnpH0WnAseRPgvmJ/08F7MS7h4R57alkAOQtB7pd+t44FxS66FbSa251gS2z1lvAf4MHNFpwWZJqwF/BxbKm54l/Vz8uFuCgb3wWd0Ln9Pgz2ozs6aICCcnJ6euSvT/E2M/UsuIK4HZquRbDjgYeAroAx4GvgUs2O465PItRvojto80XtWr+fUVwALlunZyAlbL5e8DfgfMVbH/ENKXjT7Sl4/F2l3mKnVYndSitI/UWmIu0rhJkFpJbArckfd/st3lbaA+y+afjV+RguFTc9lvAz4HzNqJ7698n4uynlDaPqb0umqZO6kupNY2j+Sfi/WL8lWWsca2MSNRxgHKv0npORxf2j6eFEzrAx4jBQI7osxV6rAwaVzDPuA0YIbSvrHALMAf8v6Di+2d9F4CFsm/M/tI/+waU7F/VdL4k305PUX6h9eMnfJcSF1Pf5vL9wjwaKmsPwcW6aR7XqMOXf9ZTQ98Tudy+rPaycnJqQnJswabWdeJiOI/o9uT/iP8eES8WuyX0oxrEXE/6Q/eTwBPAksAnwT2ljTPiBa6gqSxwI6kAa6nAH8Bfga8RPrv/J8kLRDR2TPISZoX+CrpS/f5wAER8WLeNyNARBxNasUCsBbpS1XHkLQQ8E3SGEN/Bg6KiBcjdbEbExFTI+Jq0pck6G8R0pHye+sZ4H/Ax0hfKLYkjZ25GrA/sI86bIZCSVuTuvkLmBAR++ftY0hlL4yRNKek1SWtJGkxmOb3QtuU7uX7SO/zx0mtO4D+MkpaW9JngQuB8yUdLWlvSTNERF87n4mkTekfE7DcIrOYnOIk4E1SkGpv6NhZkBcmtVx8HPh5pFZyxWfD1Ejjxhatw58qtudl8Zza/bOxMOn30kPAD4v3Rv6ZICLuBI4iBTQhtXT8OHCcpBnb/V7KNiaN4QZwHrAzcDP9rTK/KmmRTvpdVNYLn9W98DkN/qzu1PeXmXUnBwLNrJu9lZfPAUiaAaYNCETEpIi4EPgIKRi4OPBpYLsif5ssTPoP/AKk7jnfJE148lO64AtGqTyrkP4D/xSp5dDbpS/bk4svrMDhpJYgswDb5HO0/TMol2Er0pfT/wDfjdJMnPmL9FhJswJF8PglSStI2kTS5pJm6qTnk78MvUIaF+1dwBoRcSWpnlW/YBTH5i8mIyoHNsaTxnsaR/pS9LdqP59K4739kvSF6V+kINsNko6QtHK7n0PpXhblmBgRb0gaVwou7UwKAB4FbAvsBHye1CLkIknztOtnPnd/vJzUsqayW3Yx9tzNpJZcAewqadeRLmeD3kf6PTuZ1BKtHOAr3lszkYJsK0n6oqQTJH1D0jY5KBtt/j21LWkMtCmk7rRE8k7gNSKeJI0f+BypJdHMwJ7AoTl427YAuaRxpADH0qTf/z+LiFtIk57cQncEA7v2s7pXPqfBn9Wd8FltZr2lI365m5kN0Zt5uWP+I3xKrYz5v8QfA54mfSn5Evk/3m36Q3dGUpcQgH3zf7VfIrXsOIoO/4JR+oP0YNJ9/C9wXcW+ckuhN0ndX8j5O6kV0RhgBlI3wrvKO/IX6amkbuZLkr5o70hq0XIVKWhyJvCJTvnDvPR+fogUkNol1+MKpm9tsF/puD+QAjsjGiDPgY3XSeM7PU9qafZ/wAclzRYRffn9vwvpC9P+pPeQSAGrBUkTcfw4l7+dremKa4/Ly1UlzR0Rb+X9u5BaRc0P3ER6z51J+vmYifR8/iFpsTYFoZYn/TxUHZux9LN9Ayk4NTOwXs7XMb+fssl5+Tr5s0LSDLk+UyQtAmxH+jw4hPR795OkYMgJwG8lzZQDDO36e7m4p2+SJmqpep8j4iJSy8cxpC7bswK7ksYRbKdxpNZZt5NaoT2ag+IPAB+kO4KBXftZ3WOf0+DP6uK4tnxWm1mPiQ7on+zk5OQ0mET/GIE7Ay8ArwBfAMYNcNwY4KOklhV9pFY5Iz7uCnncJtJ/6Q8iBQDGlvYvCnwj162jxyEiBWxeBrarVb7S8zo51+e0dpe7onwLkVrQzF+xfYa8nI0UtOnLz+QO0heRYvyxPuBOUkvTto/JVSr/sqQgyJkV2zenfxyim/N7sKjLtcC8bShrMcbTaqQv1n35nn8ob9+xdK//QgrWbAN8Dzi7tO9SYPMOuPcbk8YRexTYMG97N6kbWB/wfWD20ntsc+CU0s/8dcXvs5H+mSePaZhf13w/l36eJwNrt/ueVynf5qTARh9wUsW+JUmTnfTlZ3QKcCxwfOm91EcaxH/GNtbhw6SA61Tgs6XtKr8mfYZcDzxICoq/U/4OeA7z5J+H2Urbis/ApUu/WztuzEB65LOaHviczmXzZ3WbP6udnJx6J7W9AE5OTk5DTaT/WD9I/+DWK+Tt9b68zk/6L/4kUuuj9+ftI/1lu/iCMUON/R39BaPii+hHSF1aBjrm16W6dMwf4RXPo3Iw/lnyH+B9wN2kLjsLlPZ/lNSCoo/U4mC5dtelVLaFgCfyF8AlSC0pinpuVfqC8Qr9g+BvW/6iO8LlrRYM/DepK97Lef1wYJaK4+YHvl36ove7Drj3C9A/SdFZedtuVEy+kbcXX8BXyj8jr5TqOmI/61Xe+1V/RkvPaXtgYn4fHU1qHdj2302lci5MmiSkCAZekN8nx5JaE/WRWkitCsxUOm5HUtfzPtI/jQ4sP6cRrsMmpJaAfaTWsBuW9pV/nhclBTRvz+ufLf08fLgDnsV07yWGEQxsx88FXfhZTY99Tlc8D39Wt+mz2snJqTeSuwabWVfKY1U9BnyZ1GJiY+DI3K2ir1bXj4h4hjRY9mTSbHMb5u1RLX+rRH9Xu6rdmSPicdJ/5hvqeqQ84PdIyWUoBqw/PSJuq5W3VNbX8nJG+ru8FXnG1jhmRJSeR2U3qC8Ba5ICzttGxKUR8XRRxog4FfhUzrs5qZVqR4g0dtg/SK0kFs3vtchd8y4FtiZ1PRpH+nn4C/CvyGPBtaG8U/PP7x2kAMjLwDqkZzAbcGxEfCvSJA/vvGfyz/RPSLOlAuwnaYeRLn8hdz19GvgRaRzT3SR9gXS/If3Tolz+yMu7gd+TJ64AVh/J30uV7/0qPwvF9qn55XWkcVdF6tY9S7XfTe0SEU+Q7ueFpOewPfBd4EDSJCJPkoJkdwLvvOcj4gJSYCdIrdk2zdtH9DMiX/Ma+t/X2wAHStox75uSP+tmAn5ACkjdJGlm0kQKRcuhtaC9XbervZdy2cdExEM02E1Y0sL52BF7n3XzZ3WvfU6DP6vpgM9qM+sNDgSaWVcqfSm7gtTCYwqwC+kP8neCCjWO/Sfw27y6vaSZR3rMmEb+eBvoC0ZxHknrAF+UtGzNk7VANDh2VulZPUH6Y/Yt0hdUYJqJCGbKY6iN6JfuAZ7FX4AjgS0i4pGK+haBg1Pon21x3fxMRvTztU4dHs7LrfJyXES8lccW+iKpDjPmtDFpRu22zFCYv6yVg4GbkrrXzgmcS+pO+864SqWAFBHxJukZvJw3LT6CRX9HrkPx3v4nqWsapN9N65Pe98XEFVMrj4+IG0nBK4CtJS3WjufQSJ5Is45+g9RKZTVSa7u2BMwqqX/iqMuA7wAHAFcD5wBH5NeXAPcW77vyez7SBFPH5tPtImmJNjyHImD0Q+CP+fVHgB9KOl7S7pK+SqrT3sCLpNmR38xB0DtJf+dvLWl8JzyXShXBwN2ZNhj4f5IWLYJZkt4NnCDpD/mZdUx9OvmzulZAv0q+jv6cbkBXfFbX0TWf1WbWGzzIqJl1tYh4SdKZpO5FOwIfyd8R9qr2RVv9A0o/kTfNTH+3i6ZSGox+UdKgz8+SuiJfGxFP5z/exlYrY1lEPC7p5Lz6Zfq/YOwREc+SWnscA2wELCLpi7VaLrSoHn2N1CPrI/0xOwfpvr+Rv1z05S+955ICswdFxC9HsA41n0VE3CbpzvwF6J1JE/K+vtK2l4o65i9HTf+CNJg6lL4oX0EK0KyVy/xm/tJwUT7P/0iz8P6AFMz5FNAn6ZSIeLVddSiCgZI2AS4jdR18Idehr+KcRV2vIs2cOgeptW9LNFCHGSK11vqPpONJrRo3JbXkGANsJen6iJhccd7ivfS/vOlNYHIrvmwP93dTqUwPkrrjbQ6sJWnxSBNCtDxQM0AdpkiaMSImR2rxd6ekCblu8wFfB66LNHvqWNJnABV1fzpf6k1gahuew+RclpckHZrL8SlSa8aVgX1IY9aRj905Im4t6k36uTkw5+nYv/dLv0cflrQ7aczPtYA9SK2ijiLNqHo0qaXww8B8pPE2m0JpUpi3h/O+bfdndTPqkLXtcxqGXo8O+6xuuA6d+lltZqNAdED/ZCcnJ6fhJuB9pBYek0l/yJ4BzFfaX4zDVQwq/Y2c78IWlWd94F+kL5N99AcbryG1NJllkOerHIfoStJ/ji/P6y8Bq3RiPUr3/ov52LtIXWCK7TOSumv3kVp0rdppdRjg/OUByo/qpPcTabyh50hjCs1MCkQVXQafAHbM+TYrnfMR0uypTR3farB1KP2sLgXM2sD5lyO1IOwDDuqU50AKOpUnoLiWKmOg0T/23peKn/FOqcMA59u/VLfPtqLMTXgvqfR+2i7n/WN5f+l1MTbXz3K+qzqhDvmYQ4CLSWOdvUT6XfprYLWi7PT/Xt2O9Hl4BzDPSDyXYd6P4r4vxbRjBp5O6obeRwqUrtTk625MGpdwscr3whDPN+Kf1c2oA23+nG7Fs6hy/pH4rB5SHeigz2onJ6fRkdpeACcnJ6fhpIovcNsDfwPezn8kXUwaUHmOimPGk7qG9QGHVp6nCWXakP5Bne8lddEqZqgs0vWkcV9mH8R5FyUFFV7M5yhmIH0OWLEF97ap9SB1X+sDbgfmytvKXy6ebXY9WvUs8rmLL67vIf23/mFg3U55PwFjc7qCNO7Te4G/lr5YbM+0gYPNSudbpt3PgRS8GVM6R63JK4ryf5g08+KdwBKd9PMAHFaR789UD/TMSmrZ2Efq5ja2E95LA9z3d5Em4iiCBys0+943sw6kfxr1AY8Be5S2z1Cq0zzADTnfd9v8HOaoOHYWUtBgmfy6CHAWZVeuyxH5POe08nk0+dkWwfAl6Q/YFMH9pn/O0f87701S4LcpMxYzgp/Vza4DbficbuWzyOcYqc/qIdWB9DncEZ/VTk5Ooye1vQBOTk5OEcP7Y4xpg4GbAifRP1PkPaQv3VuR/lO7PfB3+meWW7zJ9VgeuC+f//f5y9rspODjHvmPuxdKZTsQmH8Q51+MNHj867SohUSr6kEaA2oqqTvhLKSBr1v25aLVzyJfYzxpzKE+UivUuTqtDsCP8/4i3//yz0G1VlDvAVbutDrUOXfxxWgO+lvcnMQgg7oj9By+SmpJU7RavoT0j4q5SMG0VZk2oNYVv5tIX1C/T//vo2Im9qbPONqk5/Bu+oNwFwK7VeyflzTeWB9p9uDFOqkOVHxWVq7nbQuQAolvAh+vlW+I5W9p66PSz/ROpfvwPM3/vbQa6XdhEVB5mgZmLB7E+Vv+Wd2KOjDCn9Mj8SzyOVr9WT3sOtDmz2onJ6fRldpeACcnJ6dyouLLI7mFQAPHlf9QWjZ/eXqt9EdZMfD1G3n5MLB8E8tdfHnZL1/jSmC2KvmWAw4mdXkqyvEtYMEGr7Mq/d2kXqD5XYxaVg/SQPfvBGCBM1vx5aLFdSi/z+al/7/2DwBLdWIdgF1JrTuKLyfTfLGoVrdOq8MAz2Eu+gM39wFLdmodgI+SunsVv4eeIXUFu5vU8qMPeJQmtqobiedACmQV77E7qp2/k+pAGmer+Gy4FzgR2Jc0+/T1efuTdMlnRMXPw/z0B5T/DSzUzGdRus6QPqsbuU+kcTWLbpGtCALOR5owrI/UvfLR/PopmhSAovWf1S2pAyP0OT0C9RiRz+pm1IH+4F5bPqudnJxGZ2p7AZycnJyAFYBPkGY/PI80IPIBDL6rZmVLiTWA35C+2BWBwBtJX/qWblFdzs7XOaVW2Uj/Zd+B/v8eTwS+wgDjOOUvR1fS38Wo6S0BW1mPnHdy/iP5xlZ+uWjlsyAFnrahvwvn47SoO2Qz6kBq0fE10iy27x/pLxEtfg5bl34m/tfJz6GUbzXS77uHSEGOIiB1f75OS7p6tfA5FF9iDykd09A/Nka4DnNXHPOzvL9ooTml9Cxuo4lBwBF6DksAO5PGGOwjdX1ersllb8pn9QDXWI/URbJln3OkWbwfyNc4hjRBQ/GZNOxgICPwWd2qOjDyn9MtexaM0Gd1s+pAmz+rnZycRldqewGcnJxGdyLNoPcAMIn+L2FFuos0EP2g/3tL/5fTmUgtDFYCVs9/aL2rhfU5tfhjMK/PUCfvpvR/0XsA2KtWftKYQ39u9R/lrawHqSvLm/R/8X6+lfVodh1IY/jsSurS+WTOewOwbAfXYVzeNxNNbCnXAe+lrUjjgRZ5b6LJQY9m14GKFlOkViSr5ffUzsAiNLklXaufQ8UxG5OCT6t1ch2YdtzJL5KCTm+QxqK7njS2XlO7A7fyOZA+32Zm2jEGb6LJgUxa9FldcY0Z6A/QPkNrWqCNI818WwRYF8/bl2XaSUqGGkhr+Wd1K+vACH5Ot6oejOBndbPqQH+L4bZ9Vjs5OY2u1PYCODk5jd5E+s9/MRD4pcDJwA9JXSOKwbWfBM4CNigdN5g/youAoEp/aLXsv6ykMZ+KL20LNJB/y9IfqreQv0gxfdBgblIXnXsZgXFhWlEP0pfaotXNM62uR4vqUMyoeDdpZs+WBQyaWYd2phY9h8/Q323y9zR5PL0W1mGaiR26/TlUOaZlgcwm12GmUp65SOP0rUKaFKEpXVxH+jkAa5NaEv0YWLTJZW75Z3XpmEVJgZWmz0qbzz97vke3AtvnbcU/TJZm+IG0ln9Wt7IOjODndIvrMSKf1c2sw1B+XpycnJyGmtpeACcnp9GZgAXpn7n396SuUMWMgUuRulrcm/e/SRp3aovS8R31BxP9QcadSeMBvQJ8ofiDsM5xY0jjhj2b63phZd1K556fBr44dmo9cr7j8v6mjpfU6jowbbe9nUgTDozvpjqMdBqB57ADaQbYObqpDr3wHAa6VjfUgQFmoe6GOpTyFJ+ds9HkFu+04bOa1gdj5yG1YJ2ttK34p+GQA1CM7Gd1S+qQ87T8c3qE6tHyz+pW18HJycmpVantBXBychqdidR64VnSODQr5m2Vg4/PQ+raMZX+MY+2rHPOlgxePsh6LUaaba+PNFbTCtXKVnHM/MBRpC5X5Vk3y0GPkR7Xran1YNruePN1aR165v3UzXWod1y31KEXnoPrMDrqQO9+Vk93vxlG8KZ07Ii9r1pQhxlLr0fkc7pF9ZipFeVsx/vJycnJqVVpDGZmI0iS8suNSF8eXiJ9KSIi+kr5xkbE88DuwGnA66QxtH4jaZMq5x0TEX2SNpR0Qj7fVEljW1mfijIoIh4DvkzqWrMxcGSuS1+tskTEM8D5pDF55gI2zNujlCeqHdsKrahHPq74zHmuS+swtdXlLmvl+2mktOq9NCKFz/wcXIdm6aY69PJndb7mdL9His+piHgI+CCpK/b8wIeAr0paJCKidG+QtHDFsSP2vmpBHSZLmilvbvnndLnM1bYNox5v98r7aYSKb2ajUbsjkU5OTqMzAR8h/Xf0UdL4TNO1CGDa7k4TSAO6TyV1U5pu/CDSLMHFQOant7Fuc5Jab7ydy3JqZZ1qHHcU/TNWzlwvr+vhOrgOroPr4DqMQFl79rO6Tp2LllxLMW1LrmPJ4y+Sumy/G7gA+AMd1s2zF+rQK/XohTo4OTn1XvJ/GsysXV7NywVJ3Y2mlv8rCu+0EhgTEa8CB5NmCRVpps29JM1Rcc6lSF2YAHYq/rM60iLiJdJg4ReQWn18RNIped90rcpK/7l+Ii9npr+LVdv0Qj1cB8B1aArXAXAdmqLL6tCzn9W1RH9LrodJLR2Lllx7AF+RtBhpVtijge1JM+3O167yVtMLdYDeqEcv1MHMeo8DgWbWLn8jjY80A/AjSUtFxHRdavIfUGMj4hXgE6SZ2eYgda1YBFJXo5z3fOCrwH+A9SLiicrzjZSI+DfwG+Aq0he2PSWdIemdP+5KX6aK5cx5+UBETK52P0ZaL9TDdXAdmsV1cB2apYvq0NOf1bXUCd58iDRL7B+ATYAXgR0jdd/uKL1QB+iNevRCHcyst8zQ7gKY2ehT+nJzCbAOafD0z0n6YURMNy5Nbm0wNiJekrQXcBGwBPA9SXuU/sDqi4gzJF0YEa+NWIUq5HGgIiL+IWkG0rhOW5HGiJlT0tHAdRHxcq7fFEnjgW3zKW4on6cNVaB8/W6uh+sAuA5N4ToArkNTdEsdev2zeiCl4ObDknYDzgHWAnYExpNmf35PRNzTznLW0wt1gN6oRy/Uwcx6h9r0N5CZGUrdIf4OrEQahPynpLGSXq2RX6QWEYeTuh/dBmxR5G/2l6LhnK98rKRNgY+TxlqaCbgPuBc4jjSW0hyk+rwvb98mIh4dfg2mL8twjm1nPVwH18F1qF6O4RzrOgxPL9ShwbL27Gf1YM4vaSdSy605SS23NomIu5p5jWacq975W1mH8nWadb5a5/ezMDMbHgcCzaytJK0FXAHMDtxJ6iJxfr1WApK2Ay7Mq2tHxC0tLuOYmH6WxAFnkK34orcsqTXHD4FZcpbJwIzAW8A44BHSF7z7mlyFojxdXw/XwXVoFtfBdWiWXqhDA2Xt2c/qBs4rYG3Ss3kvLQza9EId8vW6vh69UAczs1ocCDSztpP0AeAk0oyDd5AGTD4/Uvei8hel4r+oywM3ArPSoi8XklYgDdi8PWk81SeA/wKnRBoDqdHzTPOfZUlrAJ8F1iR1CZkRuBm4HTg8Ih5qVh3y9bq+Hq6D6+A6uA6uQ2s+Iwajlz+rB7jGesCPgM1I3Tc3iYi7m3HufP6ur0O+RtfXoxfqYGbWkOiAqYudnJxGdwLGAvsCr5BmQbwL+AawSLE/L2fIy62ASaQvIrO3oDwbAQ/ka/RVpLuA/YGlhnDeMXk5EyBSN6vVSS093uV6uA6ug+vgOrgOI1GHIdZ7VHxWV1xjBuBn+ZzPkGZOdh16sB69UAcnJyenRpNbBJpZR5A0I2mg9F+Ruh49AdwEHBoRt5XyzQacCWwDnAJ8OiLeaGI51gMuIw3cfHkuxxOk/w4vCMwLPA38E/hpRFyfj2t4TJmiu0nuHkJExGCOHy31cB1cB9fBdXAdWvMZMVSj6bO6dK1FgWNJdbzTdZju3F1fj16og5nZoLQ7Eunk5ORUTqTxUp6i/7+wrwG/A74FHAVcm7c/wjD/M1vl2gsCV+fz/540TlPRwmEpYBfSQO19wJukVg5blI5Xu+9fr9TDdXAdXAfXwXXo3MQo+6wuzu869F49eqEOTk5OToNNbS+Ak5OTU2UClgbOAO4ufckop9uB5Vtw3bWBZ4FHyd01yF21SnnmAS4BpuayPAZsWeeclce3/I+/XqiH6+A6uA6ug+vQ2Ql/Vo/qOvRKPXqhDk5OTk6DTWMwM+swkQZD/zTwIeAY4K/AbcB5wJeB7aOJsyYW3a9I48PMA7wEPJjL0lfKNzYingd2B04DXgcWAX4jaZMq5y26d20o6YR8vqmSxjar7L1WD9fBdXAdXAfXoTWfEc3mz+rRWYdeqUcv1MHMbMjaHYl0cnJyaiQB40fgGh8h/af3UWAZqvwHl/7uIrMBE4A3SP8hvhpYtUr+NegfePr0EbpXXV8P18F1cB1cB9eh+xL+rB41deiVevRCHZycnJwGm9wi0Mw6Vum/tZD+6Krc1myv5uWCpO4hUyuvl7eNiYhXgYOBv5Fmd1wN2EvSHBXnXIrU5QRgJ0kLt6747+iFergOrkOzuA6uQ7P0Qh2azp/VQ9ILdYDeqEcv1MHMbHDaHYl0cnJy6pQEjAGuIv0H907qDHBO/3+H5wRuzsc8DKxcnKuU90PADcU+18N1cB1cB9fBdWhHHXoh9cJz6IU69Eo9eqEOTk5OToNNbS+Ak5OTUyck0n92x5BmPHwdeJk08+G8dY4p/iBcmTQzYh9wdvGHYMUfhLO6Hq6D6+A6uA6uQ7vq0AupF55DL9ShV+rRC3VwcnJyGkpy12AzMyCSPuAPwETSODA7A7tLmq3GMUX3kYdJfwROBZYExuf9fUX3koh4rdV1yNfp+nq4Dq6D6+A6uA5WTS88h16oQ75O19ejF+pgZjYUDgSamZVExGPAXsArpEGjDwB2ljRrjfwREW8AlwJjgXcDy5b3t7zQ1cvV9fVwHVyHZnEdXIdm6YU69IJeeA69UId83a6vRy/UwcxsMBwINDOrEBG3APuRBpBeFfgK6b/Dc8K0g6CXXj8IdNR/fnuhHq5DZ3AdOoPr0Bl6oQ69oBeeQy/UAXqjHr1QBzOzRjkQaGZW3Z+Bz5P+wFsN+CpwgKRFIiIkjc35iuXiwAzAf0l/GHaKXqiH69AZXIfO4Dp0hl6oQy/ohefQC3WA3qhHL9TBzGxg0QEDFTo5OTl1YgJmBPYEXiINBv046Y/ENSryzQZclPOcDLyr3WXvtXq4Du0vv+vgOrgOvVeHXki98Bx6oQ69Uo9eqIOTk5PTQEkRHsLAzKweSe8FTgPmz5smAX8iDSw9J7BhTo8Bm0fEwyNfyoH1Qj1ch87gOnQG16Ez9EIdekEvPIdeqAP0Rj16oQ5mZrU4EGhm1gBJSwNHAqsDK1TJciewe0TcN6IFG6ReqIfr0Blch87gOnSGXqhDL+iF59ALdYDeqEcv1MHMrBoHAs3MGpQHjF4c2Jc0q9ziwEPAtcCZEfF42wo3CL1QD9ehM7gOncF16Ay9UIde0AvPoRfqAL1Rj16og5lZJQcCzcyGSNL4iHi93eUYrl6oh+vQGVyHzuA6dIZeqEMv6IXn0At1gN6oRy/UwczMswabmQ2CJJVW36iyrSv0Qj1ch87gOnQG16Ez9EIdekEvPIdeqAP0Rj16oQ5mZmVuEWhmZmZmZmZmZjYKuEWgmZmZmZmZmZnZKOBAoJmZmZmZmZmZ2SjgQKCZmZmZmZmZmdko4ECgmZmZmZmZmZnZKOBAoJmZmZmZmZmZ2SjgQKCZmZmZmZmZmdko4ECgmZmZmZmZmZnZKOBAoJmZmZmZmZmZ2SjgQKCZmZmZmZmZmdko4ECgmZmZmZmZmZnZKOBAoJmZmZmNCEmR08R2l6XVJG1eqm9lOqzd5TMbKZKWL733/zmM89xa4+dpYhOLa2bW82ZodwHMzMxqkbQ0sAOwGbAKMDcwF/AW8BLwEHAbcAXw94h4o4FzXpnPNxiPRMSSgzhPH/Aq8DRwC/BX4JyIeHOQ17VRoBQUeikijmljUQalW8tto4ckAf8DFgJujIh121ykntPgPV679Po/I1IwMzOryYFAMzPrOJJWAA4DPgiMrZJlRmBWYFFgU+Ag4HVJfwSOjIhHR6iotYwB5shpeeBDwOGSPhYR17S1ZNaJvpOXjwDHtLEcg9Wt5W6HG4EzSuv/aldBRpn1SQEqgD+3syA9rJF7vE7p9XACgT8D5i+t/2QY5zIzG7UcCDQzs44i6cPA74Dxpc0vANcCDwLPkz6/5gVWADYCZsn5PwNsDyzR4OUuAf7RQL6XB3meMbl8mwAb5G1LAn+XtEVE/LvB8plZb/hvRBzV7kKMQruUXp/fpjL0ul1Kr8+vkacpLQIj4uTyuiQHAs3MhsCBQDMz6xiS9gEmlDbdRWp1dG5E9NU4ZiZgG+DzwJZUb0FYy7+a9OW85nkkbQ+cRQpWzgL8Bnh3E65p1nUiQu0ug40qO+flgxFxZ1tL0rvq3uPcdXitvPoS8MAIlcvMzGrwZCFmZtYRJK0LHF/adD6wbkScXSsICBARb0fEXyNiK1Ig8P7WlnRwIuJvwJdKm9aStHq7ymNmNhpIWh5YMa+6W3ALNHiPlwdmz69vjIhoecHMzKwuBwLNzKwpJK0q6ceSbpT0jKS3JT0r6V+SDpO0wACn+CkwLr++HfhIREwaTBki4nJg6yEUv9VOAaaU1tdrxknzPf+hpOslPZXv+WuS7pV0hqRPSppjgHPMIGlvSedIekTSJEmvSrpf0gRJ2zVYlulmb5S0haTTJT0s6U1Jz0u6RtJBuSXniNa1dJ7hvE+bUldJSxbnKW1eotYssw2WYWdJZ0p6MD/HkLRvxXGrSfqKpL9IeiDfw8n5HtyQ780ydere9HLXuVZXvDdbSdKmkn6fn9Wk/J69QtKHJSnnObVUx23aXeYOsmvp9fkDZfa9HpJG7vF03YIlzS5pf0mXSnpM0luSnpR0liS3mDcza7WIcHJycnJyGnICZiaN6TcViDrpNWDfGufYtCLvFi0s75Wl6xw2UucBnijl//ow6zAr8EfS7MT17nkAN9U5z2rAPQ2c43Jg/gHKVOSdCMwE/HaAc94MzDeCdR32+7SZdSWNGTlQfd5JA5RhduAvNY7dt3TMHxu83hTgUEBVrtu0cg9wjzv+vdnAe3fz0jknDPLYOYHzBijnmaR/ntxU2lb3XoymRJqQJYBngTG+1+25x6R/8hX3bNecnqhzr98Ctm/w+g39PnFycnJymjZ5jEAzMxsySbMAl9E/IcZUUoDsJuBFYC5gM9KsguOBkySNi4jjK05VblVwT0Rc0cpyt0l58pNBtXQskzQXcDWwamnzXcAVwJOkGZUXBzYkddmqOmaipDWAq0gzGwO8AVwA/DefY11gK1LvgS2Af0laPyKeb6CYxwP7koJqf8vlC9LMkTvkc64FnATsOAJ1bdb7tJl1fQH4Sn5dDHj/InBEA9ecpnqkFqc75TJcSAqgiRRMe6uUd768fAm4Lud7gRRkXYgUkF+ddB+/SwoIVpanWeWuqRvem60kaR7S+37lvGkyaUKim4G3SWOM7kSaVf1eUtdLgCci4pmRLW1nyi1718+rf43aY8z6Xg9Ro/eYaWcMXhv4Jul3zmXAv4E3Sb+/dyf9fM8E/FbSUhHxdivKbmY26rU7Eunk5OTk1L2JNLFH8R/5a4FlauTbnjTzbpD+6F+uYv+tpfP8ssVlvrJ0rcNG4jykL5PlFg87DeO6fy+d53ng/XXyrgJ8pcr2ccDdpfPcDCxZJd/6wNOlfOfXuVZlq46/U6VVFfBeUnCqyLduK+vazPdpq+pa2j9xEO+DyjJcDiwwwDFfInWdn6FOnm1Jgb0gBUUWa6AMQyl31WO65b3ZYF03L51rQoPHjAEuLh13TbX3K7Ax8DopWFvkvWA45e2lBOxfui9Vf2/4Xo/IPRbwSilfH3A9sGKVvGvn37tF3k0bKMOgfwc5OTk5OYXHCDQzs6HJ4/jsk1fvAd4XEQ9WyxtpwoyP59Vx9LcoKqxYen1zM8s5gO/UGttsMGOZ1ZPHG/tpadMbpCDiUM61PWmGZEgBiy0j4i+18kfEfyPiJ1V27UX/PX8e2DYiJlY5/gbg/aQvbwA7S1qnMl8VdwC7RMSzVc55OXBcadPu1U7QrLo2+X1azbDr2gQTScHlp+tlioifRsQlETGlTp6/A5/MqzOUXo+Ujn9vtthXgffl1zcDW1V7v0bEtcAPmbYV7C2tL17X2DkvJ5Fa+FXjez08jdzj5YHZSutXAptFxD2VGSPiJuCvpU1LNKGMZmZWhQOBZmY2VAeWXn8nIl6vlzkizgUeyKvFFwgkzUr/JCGQvvzXJekTkr5cJ200iHo0naQxkuaTtCtpDKXNS7uPiohXh3jqz5VeHxsRtw7xPPuWXv846nRxywGXM0ub9mvg/N+NiDfr7D+j9HqtGnmaVdemvE/raEZdh+tHA9VrkM6nv/v6pk08byP2Lb3u1PdmS0iaE/haXn2LNGHSW7WP4JyKdQeneOczZcu8+o+IeKNKnjnxvR6yRu5xVg7Ov8jA9/nx0uvJwyiimZnV4TECzcxsqLYsvb64wWNuAZYF5pe0eEQ8Sv84YIVGgmRfB2rObEoa3+xfDZznEuAfA+R5uYHzfEfSdxrIdzapbIMmaQamDcqcNMTzjCONsVY4s1bekj8BH86vNxkg71QGfj/cW3q9YOXOZtU1a9b7tJph17VJ/jpwlmnlQMgapMk/ZiMF41XK8jb/396dR9tVlncc/z4MIiGFMBQHAgQoIJBAQGkr0AUhFosy3FLGggwVo1IZ2grYVTuItgsolUFCFZmEIiBQrkgVXIkEGURFG6ZSKRBGQSTkhoCGMDz943l3zntP9j7T3SfJzf191rpr73PPu6f37Jus/ZznfR8Yx/Bs3b4aDfdmn51C49/Db7j7o23aP9X0um/BqTRf518QWbpTgA2IQM3zxFybtwA3lQWkzWwaMXS9nWnuPqfNNq8TGbCDwD9XfKmyL40vlwYrjnUKfe5rM5sIHEHMNbkFsDHxdzWPyIy7vPkLjhbX/SrwEnA/MbflNWXXbmY/JqrSf9zdL6s4r58SAbpF7r5uRZuPEp/pQmCTks+1kz6G4RWDZ7bLWgYmZuvPVrYSEZERUSBQRES6lh4KN8t+NWRmVc2rbAw8TcwflBs/glPr1j3ufs5yOM6DwJfd/YoR7GNzGn2ziBjm2otJNB7ghsqGXZbIh2tvU9kqzHf3V9u0yR9gyz7vWq615vu0TB3XOlKL3P25Thub2R5EReC9qSiu0mT9Xk+sB5NY+e/Nfjo0W7+gg/b55zfk7vNqPh8AzGwGcA6NIZ4vAA8Rn9VmwGHp5yUzO9Ldm79cmZqW86n+W3bgvpJtFhAFXSBGMk0CtgVOB/7EzP6wJMNzIC3fIoJZZfrW12a2ejq/zwNrE9f2PPH/wIZEIHVH4CQzO8fd8ykIpqZlft1G9P2WRJbygcA/mdmxJX09lJbNX7AV57YbjSy98Wa2mpcX+TgpLS+pyDYeSMtWfQzDA4HXtmhXyAtD/aKylYiIjIgCgSIi0osNa9jHOgDuvsjMlhCVAjvat7v/XvPvzOwKGnPBLW/NmYVvExkcvwLmuntzNkkv8n550d29x/3kgZ2XOtwmn0/tHWa2TouhqFVDxJZyd88CcmXTlNR1rbXdpxXquNaRGuq0oZmdBpzV5f7Xat+kNqPh3uwLM9sC2C69fMbdH+5gs/z+nlv7SQFmdiYR1HLgYuB8d/+f7P01iIrbpxOFaNYu2U0xxPpqdz+5w0MX23zL3T+VHc+Ak4FziYzWI4FLm87nI+nlnV5SSbqffZ2CgFcRmYBvAmcDX3H3Z7M2mxJTH5xMo5J3ofS603arEVmvZxIV0G82s73dPc9+H0rL0ky/dEyIz7MIMA7LejezbYnP8m3gwpJrbNvH2fkW1zO/XT+b2To0gvnPlc3jKSIi9VAgUEREepH///EK8MUe9vFEtv6/RIYERIXd0iFNK7HlkVnYdSpbBzoNsDW36zUw16m6rrXu+3RlVJbNswwz24vhQcD/JIbePkBkK/3G3Zdk7Z9ieDbl8ray3pv9ks9H+JMOt9kxW699WLCZ/T0R4FsCHOzuywxBT4VnZgOzzezTxDDhZsW13d/F4Uu3SV8KnGdmnwC2B3YjCwQS87FOSOvfbrNvqL+vzyeCgK8AB7n77OYG7v4M8Dkzu57I8is7t2X6KmXu3ZH+ln9IDAH+CsOz7oqg3jIZgWmo8kFEkP0hoq/WY9npL04k/g0erMjK3Yv2fQzDC4V0UgRsZxrB9+VZNExEZMxRIFBERHqRZwCMA85rVYm0A7fTeNCaNoL9rMryPt/YzKzHTLmXs/XmbJQqG2frS9z9N5Ut61HXtdZ9n45mp2Trp3YQuJ7Qv1OpNBruzX55T7b+Qofb7J2ttwwEpurlRxFBqp2JAM0vgZuAL7r7gqb2OwHFvKfHlwUBm7n7v5ccdy0a80w+0G4fJdvMrWj2NBEIbM5YHcjWByu27Utfm9kf0yhyNKMsCJhLVXKXBk47vG7c/XUz+wfgVmAXM9smm+NwKC3LMgJPIJ79vgZsXdbOzNYFjk4vz684hYFsfbDqPBkeoOwksLdLl+1FRKRHqhosIiK9eInG0L01iGFKI3FTtr69me05wv2tip4khhtDPMT3WsThKWLCfYAJZjapg23yB7TlMW/Tk9RzrXXfp6PZB9NyMZFFVCkNXawaWthPo+He7JcJ2fqSqkYFM1ubCOwVKgOBqR9/SmTOTSMCro8DmwB/BdyTisfkLiLmxfuuu1/V9uyrTSH+9t4COhmCm2/jVAcPN03L5oISB6Tl/S3mmJyQrdfZ12en5Q3ufl1Fm1by636wTdsfZut5hmOR3dcc4HsnMIMo8DKTxty8zX/nxxH/5t7v7ndUHLuTPobhFYO7DQSWZZaKiEhNFAgUEZGupeysfE6840e4vzuAu7NfnZuyIyRJmWz5g9lxPe7ndSIoUDi0qm1Fmzt7OW43arzWWu/TPiqyFDsp3tGrYv69BekeaOXwNu8Xaj3v0XBv9lFeNGlSB+0/T1TuhQjulhbhSAG+7xEZ11cCm7r7Nu6+PZEV9jMi0P532Ta7EUNuAf6l4ysoVwSp/s/d287R2LTNYxVViP8A2CG9vDX7/ftpBAhbDVmtva/NbDqNQh9ndLDPMvl1tyxqk/qy6JsNsreG0rJ5aPBRxDyH17n78zSK4ixtl+ZfLDIaS7MBu+hj6D4jsNv2IiLSIwUCRUSkV3mlxaPSsKiOWHnp1r+hkZ2xM3B1ysSQhouy9ZPMbGqP+7k8Wz/NzCqHYZrZrkRF0MLymr+xrmut+z7thyKLZ4M+HrMYdvsuM3tPVaM0j9jpHe6zH+c9Gu7Nfsiz5aanIZqlzOzDQF5p9sEWQ97PIwJ9X3X3Y1IQCIBUxOiT6eVAts0haTnP3fMvaHoxNS3fZ2Ze8fPBim3mNu8sZYvfkF7Ocvc52dsD2fpgi3PqR1//aVo+4O7tsvmqTE3LtnMppr+34v/HvNJ1aUYgMe8fxP0A5RmB+xLB4V8D11QceiBbH2xxfnmhkIVEBmql9H99kfn9YjeV0EVEpHsKBIqISE/c/cdEhglERtCgmX0iVU0sZWa7mNlMSiqXpv2dkP3qz4CfmNlB6aGikpn9ETFx+irN3b8L3JZergXMMrP9q9qb2WQzO7XkratpZLVsCNxmZpuXbL8rcDONjK9vp3mt+q6ua637Pu2TR9JyHI0hvHUrMixXAy4tGQqKme1IzNe5IZ0VPUQjmAAACMpJREFU3ejHea/092af3EMjWLsucEma128pC8cThV7WzN6aW7ZDM9sB+BgxPP6zFcctht5ukv1u97QsHRpqZttVBPS+X9J8aUYgkfXd/HNXyfkX2+xuZnelnx+Z2bPAHGBiOrfmjNED0/Jpd281Z2LtfU1jbtvbWxy3neK6q46R25DGc1w+z+FQWuaZftOIjNA7s7+RV5rbASel5dfcfXHFcTvt422B8Wn9vzuY43UnGnPXKxtQRKTPVCxERERG4pPEMKFpRDDgYuAfzWwWMI/I8FuPyDLYlcaQopllO3P3S81scdrPOGAycCPwspndBTxGPMC9TTzAbUkEIDbNdvNboiLiqupIYgjkdsTD4M1m9jDxAPo88dC6OTG0b1siu+Rf8x2kyeYPJ+aZWpd4AH3EzG4h+m4N4vPah8bD5uPAx/t6Zcsa8bUmtd6nfXALsEdav9nMvklUK16aeeTuF47wGGcRFUPXIDJ/njCzQeL630lc93Ti876C6KtlAnD9Pu9RdG/Wyt0Xm9lZNILPhwC7ps/o18C7gY8AWxFDQm8DPpzaVgVkjqDRR7dVJG0W7+eBn63S8lHKvZvhUzlMJv5+hgVw0hc4RRGoGU3Ze6Watnlv+oGYO/JFYj7Za4AbUxXdYrstiTn2oM2Q1T719aS0fKTi/ZaarruT6sq7puVbDB9OX5YReHJanpf9blhGoJltQ/xNvQEsU/Qltem4j1GhEBGRlZoCgSIi0rP0QLUP8CWiKulaRGbJMS02e53qB0zc/Woz+zkxz9JBxIPqBjQmKK+yCLgWOMPdmyeQX2W4+/w0lO7rNIbw7UBjzqxmpZPhu/v9ZrYHcD0RRFs77e+QkuZzgMPcfX7Je31T47XWfp/W7EIi6DmFCHieWNGmZ+7+czM7DriEuP71KZ978UrgU3RWeKMv5z0a7s0+OYcIBh2ZXk9ieLVniGGtxxFFPgr3VOyvqHS7Ufpp5YlsfZ20XFDW0N1vpxEAxsweoyQQCGyT7Wtum+OXbbNRF5/rQLY+2EH72vo6ZROOSy8XNr/foW77at+0vNfd82MOpWUR4NsC2J8owJQH75qHBp8IGHC9u/+y4pgD2fpgm/PT/IAiIisxBQJFRGRE0nxJnzOz84nAyt40MrjWIB6MniSyHH4AfM/dSx8ws30+AhxiZlsB+wF7AtunfU4gslcWEFVGf0Zkp9zSxWT0o1p68DvUzHYhhv7tBWxGPIz/FniOyFy5jcZ8WmX7edDMJgN/Tsxx9X7gd4kskxeIh95r0zDdFaLGa639Pq2Lu7+WAp6fJh7atyPu8zVbbdfDcf7DzO4jAh7TiSGWbxHZlT8CrnT3WQCdTPnXz/MeDfdm3dz9bTP7GJFpOYOYM24c8CviHr+BuOY3rVFZ/WWqK+tOTMut3f2xLk5lIbAxUT22JTNbj8jMhmUrvU5Ny6fcfajDYxfbPNtlcHcgLRcwvKJuqTr72t2XmNnrRIB9fPP7HZqalvPbfZGVhvUfm15e1PR2ERQcn7IMP0N8mXaBu7+VtVs6NDjNkVh8KVJaJCQZSMtO+lgZgSIiKzFrP2WDiIiIiIh0w8z2ojFn3Dfc/dia9jsdmJVeXubupcOizWw+kU092d0fLmtTsd1sIlB+o7sf3KbtnkRW5kJg/XwuuDT89jRi/saBDo9dbHOLu1fOCdq0zUZEcHh14Cp3P7qT7Trcd6d9fR8R/LrU3buuTp5d9w/cfXqbttcR8yP+AtghD/CloF4RDJxIZDWuBkx091eydrsT8zNeQQQ+zyeyC0vn+uxnH4+EmRX321PuPmlFnouIyGiiYiEiIiIiIqNAyvI6I/vV11s0fzot9+nyMEVm7QGp4EgrRYGLsoIQ3RS/GMk2+5MVjeliu5a67OtvpeXhaThuq/2+y6Iaca647sr5Ac1sdTP7KhEEXAwc3pTlBzFFRjF34klE5vTleRAwKV5PAP4yrbfKBuxLH4uIyIqhQKCIiIiISH/tYGafzX5263E/ZxLFcSCy5u5t0bYITn3JzI6wpvHeZraFmf2tme3XtN1lRGGWNYHvm9mB1lS53cw2MrPPEENPYdlhwdAY7jq31QXVsM1AWi4Gbu1iu3a66euZxJyi6wCzUybhMGb2PjM7k8jke2/T21PTcm7Jduub2ZHAg0Tho0XAwe6+TNsUjC2CfCcQQcELSs63aPMhYn7C52gxtQL96+OumNnR+d/RijoPEZHRTnMEioiIiIj01wfST+ELZIUnUiGXN4Dr3P2N5o3NbBPg34DD0q8WEBlfrZxLVLvdE/gmMNPM5hFFISYScy7C8PnZisrNHwW+QxTUGAReMbPHU5N8W4ihs5eVnG/RZm6b8+x5m+RuYnjrM+7+WgfHqb2v03yZ+xLZcpOBWWb2ElGVe02iz4qCLcMq/TZd96lmNiOtvyNtsxmNbLzZwCnu/lCL01lIZPqNJ4ZlP17SpggEFnMaXpTmUa3SVR/30V8DO63A44uIrBIUCBQRERERWbH2Jaoif9nM5hBZY68RAZ1diCI5RRGWV4ED3H1eqx2matkfIqpAH0EEqKYA84FngeuA/6JkOKq7P21mv08U6DmUCL5MITLCXiSKy9wF3FRRiKQY6jrk7k+2u/imbRYxvIpxS+5+dqdtk9r7Op3HE2b2AaLPDiH6bCrwJlGQ5y4iuHqzu7+Ubbpztj45Ld8mgnULiOJFdwPfcfdOCmkMAZun9fMq2uRDhRcDF7faYQ99LCIiKzEVCxERERERWYHM7FFg6w6a3gsc4+6P9vmUVlnqaxERGesUCBQRERERWYHMbAqwHzFnWzH09neIYZ7PEMOIr3f3OSvqHFcV6msRERnrFAgUEREREREREREZA1Q1WEREREREREREZAxQIFBERERERERERGQMUCBQRERERERERERkDFAgUEREREREREREZAxQIFBERERERERERGQMUCBQRERERERERERkDFAgUEREREREREREZAxQIFBERERERERERGQM+H8c7mdToJBfNAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#supplemental figure: mutant strain\n", - "fontsize = 26\n", - "vmin =-1\n", - "vmax=1\n", - "width = 20\n", - "height = 13\n", - "cmap = None\n", - "\n", - "\n", - "#supplemental figure (GECKO)\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs = gridspec.GridSpec(1, 1, figure=fig)\n", - "#adjust labels for better readibility\n", - "x_csc_label_pam = adjust_heatmap_labels(x_csc_top5_pam)\n", - "x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam)\n", - "\n", - "fig_pam = make_heatmap_subfigure(results = results_pam, csc_matrix=csc_top5_pam, esc_matrix =esc_top5_pam,cbar =True,\n", - " x_csc=x_csc_label_pam, x_esc=x_esc_label_pam, yaxis = eGFP_RANGE, xlabels = True,\n", - " fig = fig, grdspc = gs[0], vmin = vmin, vmax=vmax, fontsize = fontsize, \n", - " cmap = cmap)\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/Figure3_sensitivities_protein-overproduction_wt.png', dpi =200,bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "id": "5acc414c-f74f-40a4-8017-9cd24f5cfee2", - "metadata": {}, - "source": [ - "## 4.2 plot normalized growth rate as function of eGFP concentration\n", - "Similar to what has been done by [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "316180b7-a40d-4e3d-8a65-4b08c9aa6ab1", - "metadata": {}, - "outputs": [], - "source": [ - "#get eGFP data from Bienick et al. (2014)\n", - "egfp_exp = pd.read_excel(eGFP_BEINICK_DATA_PATH, sheet_name='eGFPvsMu')\n", - "mu_wt = 0.75\n", - "\n", - "egfp_exp['mu_normalized'] = egfp_exp['Growth rate'].apply(lambda x: x/mu_wt)\n", - "egfp_exp['mu_error_normalized'] = egfp_exp['Growth rate error'].apply(lambda x: x/mu_wt)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "b13a70c0-e06b-4746-a620-7308de72b7b7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(results_pam['results']['eGFP'], results_pam['results']['mu_normalized'], label = 'default PAM')\n", - "ax.plot(results_inc['results']['eGFP'], results_inc['results']['mu_normalized'], label = 'PAM with $\\phi_{E} = 0.31 g_{P}/g_{CDW}$')\n", - "ax.plot(results_atp['results']['eGFP'], results_atp['results']['mu_normalized'], label = 'PAM with $2 \\cdot k_{cat,ATPsynt}$')\n", - "\n", - "ax.scatter(egfp_exp['eGFP concentration'], egfp_exp['mu_normalized'],\n", - " color='purple', marker='o', s=30, linewidths=1.3,\n", - " facecolors=None, zorder=0,\n", - " label='Bienick et al. (2014)')\n", - "ax.errorbar(egfp_exp['eGFP concentration'], egfp_exp['mu_normalized'], \n", - " yerr= egfp_exp['mu_error_normalized'], xerr = egfp_exp['eGFP concentration error'],\n", - " fmt=\"o\", color='purple')\n", - "\n", - "\n", - "# Set the tick labels font\n", - "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n", - " label.set_fontsize(15)\n", - "ax.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}$]', fontsize = 15)\n", - "ax.set_ylabel('normalized growth rate', fontsize =15)\n", - "\n", - "ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1)\n", - "\n", - "\n", - "plt.show()\n", - "\n", - "fig.savefig('Figures/SuppFigure6_eGFP-normalized_mu.png', dpi =200,bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "id": "9e43d84f-2d6e-418e-a7b7-9c05fc1018e3", - "metadata": {}, - "source": [ - "## 4.3 plot predicted exchange rates\n", - "Similar to what has been done by [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "14fe29c7-81a3-4a80-84f4-19d90375ef08", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_23069/2479724123.py:48: UserWarning: This figure was using constrained_layout, but that is incompatible with subplots_adjust and/or tight_layout; disabling constrained_layout.\n", - " plt.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 10\n", - "\n", - "#plot flux changes with eGFP concentration\n", - "import matplotlib.transforms as mtransforms\n", - "fig, axs = plt.subplot_mosaic([['A', 'B'], ['C', 'D'], ['E', '']],\n", - " layout='constrained', dpi =100)\n", - "for label, ax in axs.items():\n", - " # label physical distance to the left and up:\n", - " trans = mtransforms.ScaledTranslation(-20/72, 7/72, fig.dpi_scale_trans)\n", - " ax.text(0.0, 1.0, label, transform=ax.transAxes + trans,\n", - " fontsize='medium', va='bottom', fontfamily='serif', weight = 'bold')\n", - "\n", - "rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'EX_pyr_e', BIOMASS_RXNID]\n", - "ylabels = ['Acetate excretion [$mmol_{ac}/g_{CDW}/h$]', 'CO2 excretion [$mmol_{CO2}/g_{CDW}/h$]', \n", - " 'Oxygen uptake [$mmol_{O2}/g_{CDW}/h]$', 'Pyruvate excretion [$mmol_{for}/g_{CDW}/h$]','Growth rate [$h^{-1}$]']\n", - "# fig, axs = plt.subplots(2,2, dpi=100)\n", - "for i,r in enumerate(rxn_id):\n", - " ax_label = ['A', 'B', 'C', 'D', 'E'][i]\n", - " ax = axs[ax_label]\n", - " if r != BIOMASS_RXNID:\n", - " ax.set_ylim([0,27])\n", - " # plot simulation\n", - " line_pam = ax.plot(eGFP_RANGE, [abs(f[r]) for f in results_pam['fluxes']], linewidth=2.5,\n", - " zorder=5, color ='#440154')\n", - " \n", - " # plot simulation with increases protein capacity\n", - " line_atp = ax.plot(eGFP_RANGE, [abs(f[r]) for f in results_atp['fluxes']],linewidth=2.5,\n", - " zorder=5, color = '#21918c')\n", - " \n", - " \n", - " # options\n", - " ax.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}$]', fontsize = fontsize)\n", - " ax.set_ylabel(ylabels[i], fontsize = fontsize)\n", - " # set grid\n", - " ax.grid(True, axis='both', linestyle='--', linewidth=0.5, alpha=0.6 )\n", - " ax.set_axisbelow(True)\n", - " # show legend\n", - " # ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1)\n", - " \n", - "axs[''].axis('off')\n", - "# Manually create legend handles (patches)\n", - "blue_patch = matplotlib.patches.Patch(color='#440154', label='default PAM')\n", - "orange_patch = matplotlib.patches.Patch(color='#21918c', label='PAM with $2 \\cdot k_{cat,ATPsynt}$')\n", - "\n", - "# Add legend to bottom-right ax\n", - "axs[''].legend(handles=[blue_patch, orange_patch], loc='center', fontsize = fontsize+7)\n", - "\n", - "plt.tight_layout()\n", - "plt.subplots_adjust(wspace=0.2, hspace=0.2)\n", - "fig.set_figheight(13)\n", - "fig.set_figwidth(10)\n", - "fig.savefig('Figures/SuppFigure4_simulated-physiology.png')\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a2977be-a4c9-4c75-9369-cd210d95bd0d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/.ipynb_checkpoints/SuppFigure1_FAC-distributions-checkpoint.ipynb b/Figures/.ipynb_checkpoints/SuppFigure1_FAC-distributions-checkpoint.ipynb deleted file mode 100644 index d5c990c..0000000 --- a/Figures/.ipynb_checkpoints/SuppFigure1_FAC-distributions-checkpoint.ipynb +++ /dev/null @@ -1,371 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "103b5cd5-1305-46a1-b1b5-02f13237c53c", - "metadata": {}, - "source": [ - "# Code to generate Supplemtary Figure 1 in the publication\n", - "Distribution of flux allocation coefficients and finite difference coefficients for the core *E.coli* Protein Allocation Model (PAM)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "6b112820-0936-4550-8972-24673f8ee8dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading PAModelpy modules\n" - ] - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "\n", - "sys.path.append('../Scripts/')\n", - "from pam_generation import set_up_ecolicore_pam, parse_coefficients\n", - "from numeric_error_estimation_schemes_fac import (first_central_numeric_fac_optimizations, first_forward_numeric_fac_optimizations,\n", - " fcc_numeric_fac_optimizations, first_forward_numeric_fac_calculation,\n", - " first_central_numeric_fac_calculation, fcc_numeric_fac_calculation)\n", - "sys.path.append('../Scripts/')\n", - "from pam_generation import set_up_ecoli_pam\n", - "\n", - "GLC_UPTAKE = 9.81 #mmol/gcdw/hpamodel_inc.add_enzymes([eGFP_enzyme])\n", - "RESULT_DIR = os.path.join(os.path.split(os.getcwd())[0], 'Results')" - ] - }, - { - "cell_type": "markdown", - "id": "dee026a3-d8aa-41f0-9b54-2a9d02d7bba1", - "metadata": {}, - "source": [ - "## 1. set up *E.coli* core PAM" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "a642c93c-cfba-45d2-8d40-769c26b44c75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2024-03-07\n", - "No enzyme information found for reaction: FRD7\n", - "Read LP format model from file /tmp/tmpnuh27_s1.lp\n", - "Reading time = 0.00 seconds\n", - ": 72 rows, 190 columns, 720 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/EnzymeSectors.py:196: UserWarning: FORt: reaction directionality does not match provided kcat values. Skip reaction\n", - " warn(reaction.id + ': reaction directionality does not match provided kcat values. Skip reaction')\n" - ] - } - ], - "source": [ - "ecolicore_pam = set_up_ecolicore_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "d1d983b9-2d6f-4a5f-b5e8-138367f318df", - "metadata": {}, - "source": [ - "## 2. Calculate sensitivity coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "04130b6d-6035-4614-b824-952a93252372", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.\n", - " warn(f\"Solver status is '{status}'.\", UserWarning)\n", - "/home/samiralvdb/.local/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.\n", - " warn(f\"Solver status is '{status}'.\", UserWarning)\n", - "/home/samiralvdb/.local/lib/python3.10/site-packages/cobra/util/solver.py:554: UserWarning: Solver status is 'infeasible'.\n", - " warn(f\"Solver status is '{status}'.\", UserWarning)\n" - ] - } - ], - "source": [ - "#set glucose uptake rate in the ecoli models to 9.81 for reproducible results\n", - "ecolicore_pam.change_reaction_bounds(rxn_id = 'EX_glc__D_e',\n", - " lower_bound = -GLC_UPTAKE, upper_bound = -GLC_UPTAKE)\n", - "\n", - "ecolicore_pam.optimize()\n", - "#calculate flux control coefficients\n", - "fcc_fac = fcc_numeric_fac_optimizations(ecolicore_pam)\n", - "ecolicore_pam.optimize()\n", - "#calculate first order central difference coefficients\n", - "fcn_fac = first_central_numeric_fac_optimizations(ecolicore_pam)\n", - "ecolicore_pam.optimize()\n", - "#calculate flux allocation coefficients\n", - "Ccac,Cfac = parse_coefficients(ecolicore_pam)" - ] - }, - { - "cell_type": "markdown", - "id": "92640927-cc99-4a08-ab24-8e2599049635", - "metadata": {}, - "source": [ - "## 3. Plot distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "4129e80b-4f6a-4df9-ab48-e287a2fa7ca2", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_log_hist(axes, data, logbins, fontsize = 16, color = 'blue', annotate = None):\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " axes.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.1), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize+5, weight = 'bold')\n", - " \n", - " axes.hist(data, bins=logbins, color = color, alpha = 0.5)\n", - " axes.tick_params(axis='x', labelsize=fontsize)\n", - " axes.tick_params(axis='y', labelsize=fontsize)\n", - " axes.set_ylabel('Frequency', fontsize = fontsize)\n", - " axes.set_xscale('log')" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "66ce6aff-3956-4dd3-a7dc-302c52cba10c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_15286/4171700997.py:35: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 30\n", - "width =25\n", - "height =10\n", - "numbins =50\n", - "\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "#set 3 grids for the three methods, with individual axes\n", - "gs0 = gridspec.GridSpec(1, 3, figure=fig)\n", - "gs_fcc = gs0[0]\n", - "gs_fcn = gs0[1]\n", - "gs_fac = gs0[2]\n", - "\n", - "ax_fcc = fig.add_subplot(gs_fcc)\n", - "ax_fcn = fig.add_subplot(gs_fcn)\n", - "ax_fac = fig.add_subplot(gs_fac)\n", - "\n", - "#plot logarithmic histograms to see the distribution of the calculated coefficients\n", - "logbins = np.logspace(np.log10(1e-9),np.log10(1e4), numbins)\n", - "plot_log_hist(ax_fcc, fcc_fac, logbins, fontsize = fontsize, color = 'darkgreen', annotate = 'A')\n", - "plot_log_hist(ax_fcn, fcn_fac, logbins, fontsize = fontsize, annotate = 'B')\n", - "plot_log_hist(ax_fac, Cfac, logbins, fontsize = fontsize, color = 'orange', annotate = 'C')\n", - "\n", - "#set common xlabel\n", - "ax_xlabel = fig.add_subplot(gs0[0, :2])\n", - "ax_xlabel.set_xticks([])\n", - "ax_xlabel.set_yticks([])\n", - "ax_xlabel.set_frame_on(False)\n", - "ax_xlabel.set_xlabel('Sensitivity coefficients', fontsize = fontsize*1.25)\n", - "ax_xlabel.xaxis.set_label_coords(0.75, -.15)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.savefig('SuppFigure1_FAC-distributions.png')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "16a5425d-8c24-405a-925c-dcdfb00030ee", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_15286/2514543222.py:22: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 16\n", - "width =10\n", - "height =5\n", - "numbins =50\n", - "\n", - "fig,ax = plt.subplots()\n", - "\n", - "#plot logarithmic histograms to see the distribution of the calculated coefficients\n", - "logbins = np.logspace(np.log10(1e-9),np.log10(1e4), numbins)\n", - "plt.hist(fcc_fac, bins=logbins, alpha =0.5, histtype='step', fill =False, label = 'flux control coefficient')\n", - "plt.hist(fcn_fac, bins=logbins, alpha =0.5, histtype='step',fill =False,label = 'finite central \\ndifference coefficient')\n", - "plt.hist(Cfac, bins=logbins, alpha =0.4, histtype='step',fill =False,label = 'flux allocation coefficient', color ='red')\n", - "plt.xscale('log')\n", - "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = fontsize)\n", - "\n", - "plt.xticks(fontsize = fontsize)\n", - "plt.yticks(fontsize = fontsize)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.savefig('SuppFigure1_FAC-distributions.png')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "3ed8771d-4b45-46df-b08b-4ca16d3cb1fb", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1000.0000000001102\n", - "sum fcc: 0.601988103752719\n" - ] - } - ], - "source": [ - "sum = 0\n", - "for fac in fcc_fac:\n", - " if fac<1:\n", - " sum += fac\n", - " else:\n", - " print(fac)\n", - "\n", - "print('sum fcc: ', sum)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "c04ce90d-eb03-40c1-8a06-eb7c41adcc3b", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1000.0973978499744\n", - "sum fcn: 0.6520833231930596\n" - ] - } - ], - "source": [ - "sum = 0\n", - "for fac in fcn_fac:\n", - " if fac<1:\n", - " sum += fac\n", - " else:\n", - " print(fac)\n", - "\n", - "print('sum fcn: ', sum)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83a02342-fde5-43b2-9f21-94419fbcefd6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/.ipynb_checkpoints/SuppFigure1_VSC-distributions-checkpoint.ipynb b/Figures/.ipynb_checkpoints/SuppFigure1_VSC-distributions-checkpoint.ipynb deleted file mode 100644 index bd1229c..0000000 --- a/Figures/.ipynb_checkpoints/SuppFigure1_VSC-distributions-checkpoint.ipynb +++ /dev/null @@ -1,308 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "103b5cd5-1305-46a1-b1b5-02f13237c53c", - "metadata": {}, - "source": [ - "# Code to generate Supplemtary Figure 1 in the publication\n", - "Distribution of variable sensitivity coefficients and finite difference coefficients for the core *E.coli* Protein Allocation Model (PAM)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "6b112820-0936-4550-8972-24673f8ee8dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy\n", - "Loading PAModelpy modules version 0.0.3.3\n", - "Loading PAModelpy modules version 0.0.3.11\n" - ] - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "\n", - "if os.path.split(os.getcwd())[1] == 'Figures':\n", - " os.chdir('..')\n", - " \n", - "from Scripts.pam_generation import set_up_ecolicore_pam, parse_esc, set_up_ecoli_pam\n", - "\n", - "from Scripts.numeric_error_estimation_schemes_esc import (first_central_numeric_esc_optimizations,\n", - " fcc_numeric_esc_optimizations,\n", - " first_central_numeric_esc_calculation, fcc_numeric_esc_calculation)\n", - "\n", - "GLC_UPTAKE = 9.81 #mmol/gcdw/h\n", - "RESULT_DIR = 'Results'" - ] - }, - { - "cell_type": "markdown", - "id": "dee026a3-d8aa-41f0-9b54-2a9d02d7bba1", - "metadata": {}, - "source": [ - "## 1. set up *E.coli* core PAM" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "a642c93c-cfba-45d2-8d40-769c26b44c75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "No enzyme information found for reaction: FRD7\n", - "Read LP format model from file /tmp/tmpzuxhq3n1.lp\n", - "Reading time = 0.00 seconds\n", - ": 72 rows, 190 columns, 720 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: \n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/EnzymeSectors.py:219: UserWarning: FORt: reaction directionality does not match provided kcat values. Skip reaction\n", - " warn(reaction.id + ': reaction directionality does not match provided kcat values. Skip reaction')\n" - ] - } - ], - "source": [ - "ecolicore_pam = set_up_ecolicore_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "d1d983b9-2d6f-4a5f-b5e8-138367f318df", - "metadata": {}, - "source": [ - "## 2. Calculate sensitivity coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "04130b6d-6035-4614-b824-952a93252372", - "metadata": {}, - "outputs": [], - "source": [ - "#set glucose uptake rate in the ecoli models to 9.81 for reproducible results\n", - "ecolicore_pam.change_reaction_bounds(rxn_id = 'EX_glc__D_e',\n", - " lower_bound = -GLC_UPTAKE, upper_bound = -GLC_UPTAKE)\n", - "\n", - "ecolicore_pam.optimize()\n", - "#calculate flux control coefficients\n", - "fcc_esc = fcc_numeric_esc_optimizations(ecolicore_pam)\n", - "ecolicore_pam.optimize()\n", - "#calculate first order central difference coefficients\n", - "fcn_esc = first_central_numeric_esc_optimizations(ecolicore_pam)\n", - "ecolicore_pam.optimize()\n", - "#calculate enzyme variable sensitivity coefficients\n", - "Cesc = parse_esc(ecolicore_pam)" - ] - }, - { - "cell_type": "markdown", - "id": "92640927-cc99-4a08-ab24-8e2599049635", - "metadata": {}, - "source": [ - "## 3. Plot distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "4129e80b-4f6a-4df9-ab48-e287a2fa7ca2", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_log_hist(axes, data, logbins, fontsize = 16, color = 'blue', annotate = None):\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " axes.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.1), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize+5, weight = 'bold')\n", - " \n", - " axes.hist(data, bins=logbins, color = color, alpha = 0.5)\n", - " axes.tick_params(axis='x', labelsize=fontsize)\n", - " axes.tick_params(axis='y', labelsize=fontsize)\n", - " axes.set_ylabel('Frequency', fontsize = fontsize)\n", - " axes.set_xscale('log')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "66ce6aff-3956-4dd3-a7dc-302c52cba10c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_11560/2052224552.py:35: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 30\n", - "width =25\n", - "height =10\n", - "numbins =50\n", - "\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "#set 3 grids for the three methods, with individual axes\n", - "gs0 = gridspec.GridSpec(1, 3, figure=fig)\n", - "gs_fcc = gs0[0]\n", - "gs_fcn = gs0[1]\n", - "gs_esc = gs0[2]\n", - "\n", - "ax_fcc = fig.add_subplot(gs_fcc)\n", - "ax_fcn = fig.add_subplot(gs_fcn)\n", - "ax_esc = fig.add_subplot(gs_esc)\n", - "\n", - "#plot logarithmic histograms to see the distribution of the calculated coefficients\n", - "logbins = np.logspace(np.log10(1e-9),np.log10(1e4), numbins)\n", - "plot_log_hist(ax_fcc, fcc_esc, logbins, fontsize = fontsize, color = 'darkgreen', annotate = 'A')\n", - "plot_log_hist(ax_fcn, fcn_esc, logbins, fontsize = fontsize, annotate = 'B')\n", - "plot_log_hist(ax_esc, Cesc, logbins, fontsize = fontsize, color = 'orange', annotate = 'C')\n", - "\n", - "#set common xlabel\n", - "ax_xlabel = fig.add_subplot(gs0[0, :2])\n", - "ax_xlabel.set_xticks([])\n", - "ax_xlabel.set_yticks([])\n", - "ax_xlabel.set_frame_on(False)\n", - "ax_xlabel.set_xlabel('Enzyme Sensitivity coefficients', fontsize = fontsize*1.25)\n", - "ax_xlabel.xaxis.set_label_coords(0.75, -.15)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.savefig('SuppFigure1_ESC-distributions.png')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "16a5425d-8c24-405a-925c-dcdfb00030ee", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_11560/218524460.py:22: UserWarning: Matplotlib is currently using module://matplotlib_inline.backend_inline, which is a non-GUI backend, so cannot show the figure.\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 16\n", - "width =10\n", - "height =5\n", - "numbins =50\n", - "\n", - "fig,ax = plt.subplots()\n", - "\n", - "#plot logarithmic histograms to see the distribution of the calculated coefficients\n", - "logbins = np.logspace(np.log10(1e-9),np.log10(1e4), numbins)\n", - "plt.hist(fcc_esc, bins=logbins, alpha =0.5, histtype='step', fill =False, label = 'flux control coefficient')\n", - "plt.hist(fcn_esc, bins=logbins, alpha =0.5, histtype='step',fill =False,label = 'finite central \\ndifference coefficient')\n", - "plt.hist(Cesc, bins=logbins, alpha =0.4, histtype='step',fill =False,label = 'enzyme sensitivity coefficient', color ='red')\n", - "plt.xscale('log')\n", - "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = fontsize)\n", - "\n", - "plt.xticks(fontsize = fontsize)\n", - "plt.yticks(fontsize = fontsize)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "# fig.savefig('SuppFigure1_ESC-distributions.png')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83a02342-fde5-43b2-9f21-94419fbcefd6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/Figure1_toy-model.png b/Figures/Figure1_toy-model.png deleted file mode 100644 index 9e23fd1..0000000 Binary files a/Figures/Figure1_toy-model.png and /dev/null differ diff --git a/Figures/Figure1_toy-model_overflow.png b/Figures/Figure1_toy-model_overflow.png deleted file mode 100644 index 0c99c8c..0000000 Binary files a/Figures/Figure1_toy-model_overflow.png and /dev/null differ diff --git a/Figures/Figure1_toy_ec_pam_overflow.py b/Figures/Figure1_toy_ec_pam_overflow.py deleted file mode 100644 index baf427a..0000000 --- a/Figures/Figure1_toy_ec_pam_overflow.py +++ /dev/null @@ -1,283 +0,0 @@ -from cobra import Configuration -from cobra import Model, Reaction, Metabolite -import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec -from mpl_toolkits.axes_grid1.inset_locator import inset_axes -from PIL import Image -Image.MAX_IMAGE_PIXELS = None #to make sure the image is loaded properly in high quality - -import numpy as np - -#importing the tools from the PAModelpy package -from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector -from PAModelpy.PAModel import PAModel -from PAModelpy.configuration import Config - -Config.BIOMASS_REACTION = 'R7' -Config.GLUCOSE_EXCHANGE_RXNID = 'R1' -Config.CO2_EXHANGE_RXNID = 'R8' -Config.ACETATE_EXCRETION_RXNID = 'R9' - -#need to have gurobipy installed - - -#global variables: -global metabolites, n, m, Etot -#global variables: -global metabolites, n, m, Etot -metabolites = ['Substrate', 'ATP', 'CO2', 'Precursor', 'Biomass', 'Byproduct', 'Intermediate'] -n = 9 -m = 7 -Etot = 0.6*1e-3 #will be adjusted in the model with 1e3 - -#functions: -def build_toy_gem(): - ''' - Rebuild the toymodel as in the MATLAB script. - sub int byp atp co2 pre bio -R1 = [ 1, 0, 0, 0, 0, 0, 0]; -R2 = [-1, 1, 0, 0, 1, 0, 0]; -R3 = [ 0, -1, 1, 1, 0, 0, 0]; -R3r= -R3; -R4 = [ 0, -1, 0, 2, 1, 0, 0]; -R5 = [ 0, -1, 0, 0, 0, 1, 0]; -R6 = [ 0, 0, 0, -1, 0, -1, 1]; -R7 = [ 0, 0, 0, 0, 0, 0, -1]; -R8 = [ 0, 0, 0, 0, -1, 0, 0]; -R9 = [ 0, 0, -1, 0, 0, 0, 0]; -S = [R1;R2;R3;R3r;R4;R5;R6;R7;R8;R9]'; - - :return: Cobrapy model instance as model - ''' - #set up model basics - model = Model('toy_model') - cobra_config = Configuration() - cobra_config.solver = 'gurobi' - for i in range(1, n + 1): - rxn = Reaction('R' + str(i)) - lower_bound = 0 - upper_bound = 1e6 - #force flux through the system - if i == 1: - lower_bound = 1 - #reversible reactions 3, 5 and 9 - if i ==3 or i==5 or 1==9: - lower_bound = -1e6 - #constrain nutrient (substrate or byproduct) uptake rate - if i != 1 or i != 9: - upper_bound = 100 - else: - upper_bound = 10 - - rxn.lower_bound = lower_bound - rxn.upper_bound = upper_bound - model.add_reactions([rxn]) - - - # add metabolites to the reactions: - # R1: - r1 = model.reactions.get_by_id('R1') - r1.add_metabolites({Metabolite('Substrate'): 1}) - # R2: - r2 = model.reactions.get_by_id('R2') - r2.add_metabolites({Metabolite('Substrate'): -1, Metabolite('Intermediate'): 1, Metabolite('ATP'): 0.5}) - # R3: - r3 = model.reactions.get_by_id('R3') - r3.add_metabolites({Metabolite('Intermediate'): -1, Metabolite('Byproduct'):1, Metabolite('ATP'):1}) - # R4: - r4 = model.reactions.get_by_id('R4') - r4.add_metabolites({Metabolite('Intermediate'): -1, Metabolite('ATP'): 2, Metabolite('CO2'):1}) - # R5: - r5 = model.reactions.get_by_id('R5') - r5.add_metabolites({Metabolite('Intermediate'): -1, Metabolite('Precursor'): 1}) - # R6: - r6 = model.reactions.get_by_id('R6') - r6.add_metabolites({Metabolite('ATP'): -1, Metabolite('Precursor'): -1, Metabolite('Biomass'): 1}) - # Exchange reactions - # R7: - r7 = model.reactions.get_by_id('R7') - r7.add_metabolites({Metabolite('Biomass'): -1}) - # R8: - r8 = model.reactions.get_by_id('R8') - r8.add_metabolites({Metabolite('CO2'): -1}) - # R9: - r9 = model.reactions.get_by_id('R9') - r9.add_metabolites({Metabolite('Byproduct'): -1}) - - return model - -def build_active_enzyme_sector(Config): - kcat_fwd = [1, 0.5, 5, 0.1, 0.25 ,0.45, 1.5] # High turnover for exchange reactions [1, 0.5, 1, 1, 0.5 ,0.45, 1.5] - kcat_rev = [kcat for kcat in kcat_fwd] - rxn2kcat = {} - for i in range(n-3): # all reactions have an enzyme, except excretion reactions - rxn_id = f'R{i+1}' - # 1e-6 to correct for the unit transformation in the model (meant to make the calculations preciser for different unit dimensions) - #dummy molmass like in MATLAB script - rxn2kcat = {**rxn2kcat, **{rxn_id: {f'E{i+1}':{'f': kcat_fwd[i]/(3600*1e-6), 'b': kcat_rev[i]/(3600*1e-6), 'molmass': 1e6}}}} - - return ActiveEnzymeSector(rxn2protein = rxn2kcat, configuration=Config) - -def build_unused_protein_sector(Config): - return UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=Config) - -def build_translational_protein_sector(Config): - return TransEnzymeSector(id_list = ['R7'], tps_mu=[0.01*1e-3], tps_0=[0.01*1e-3], mol_mass= [1], configuration=Config) - -def run_simulations(pamodel, substrate_axis): - substrate_axis = list() - Ccsc = list() - Cesc = list() - x_axis_csc = list() - mu_list = list() - substrate_consumption = list() - byproduct_formation = list() - total_active_protein = list() - - for substrate in substrate_rates: - pamodel.change_reaction_bounds(rxn_id='R1', - lower_bound=0, upper_bound=substrate) - pamodel.optimize() - if pamodel.solver.status == 'optimal' and model.objective.value>0: - print('Running simulations with ', substrate, 'mmol/g_cdw/h of substrate going into the system') - substrate_axis += [substrate] - mu_list += [pamodel.objective.value] - substrate_consumption += [pamodel.reactions.R1.flux] - byproduct_formation += [pamodel.reactions.R3.flux] - #phi_active = total_protein - translational_protein - unused_enzymes - total_active_protein += [Etot*1e3 - (0.01 + 0.01*pamodel.reactions.R7.flux) - (0.1-0.01*pamodel.reactions.R1.flux)] - - Ccsc_new = list() - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - Ccsc_new += pamodel.capacity_sensitivity_coefficients[pamodel.capacity_sensitivity_coefficients['constraint'] == csc].coefficient.to_list() - Ccsc += [Ccsc_new] - - Cesc += [pamodel.enzyme_sensitivity_coefficients.coefficient.to_list()] - - print('Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t ', round(sum(Ccsc_new),6)) - print('Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t \t ', round(sum(Cesc[-1]), 6), '\n') - - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - if csc == 'flux_ub' or csc == 'flux_lb': - x_axis_csc += [rid +'_' + csc for rid in pamodel.capacity_sensitivity_coefficients[pamodel.capacity_sensitivity_coefficients['constraint'] == csc].rxn_id.to_list()] - else: - x_axis_csc += [rid +'_' + csc for rid in pamodel.capacity_sensitivity_coefficients[pamodel.capacity_sensitivity_coefficients['constraint'] == csc].enzyme_id.to_list()] - - x_axis_esc = pamodel.enzyme_sensitivity_coefficients.enzyme_id.to_list() - - return {'substrate_axis': substrate_axis, 'mu_list': mu_list, - 'byproduct_formation': byproduct_formation, 'substrate_consumption':substrate_consumption, - 'total_active_protein': total_active_protein, - 'Ccsc':Ccsc, 'Cesc':Cesc, - 'x_axis_csc': x_axis_csc,'x_axis_esc': x_axis_esc} - -def plot_sensitivities(fig, grdspec, glc_rates, mu_list, substrate, byproduct, total_active_protein, - tot_prot_csc, substrate_csc, e1_esc, pie_data, pie_labels): - gs = gridspec.GridSpecFromSubplotSpec(2, 1, - height_ratios=[1,1], hspace=0, subplot_spec=grdspec) - sens_ax = fig.add_subplot(gs[1, 0]) # sensitivity coefficient linegraph - mu_ax = fig.add_subplot(gs[0, 0], sharex=sens_ax) # mu vs v_s linegraph - - mu = mu_ax.plot(glc_rates, mu_list, color = 'black', linewidth= 3)#(35/255,158/255,137/255) - sub = mu_ax.plot(glc_rates, substrate, color='orange', linewidth=3) - byp = mu_ax.plot(glc_rates, byproduct, color='purple', linewidth=3) - prot_ax = mu_ax.twinx() - etotact = prot_ax.plot(glc_rates, [p*10 for p in total_active_protein], color = 'darkred', linewidth = 3, linestyle = 'dotted') - prot_ax.set_ylabel('active enzyme sector (AES) ($g/g_{protein}$)') - prot_ax.set_ylim([4.9,4.9022]) - prot_ax.annotate('$\cdot 10^{-1}$',xy=(0.98, 0.1), xycoords='figure fraction', - xytext=(0.98, 1.1), textcoords='axes fraction', - va='top', ha='left', fontsize=FONTSIZE*0.65) - prot_ax.set_yticks([4.9,4.901,4.902]) - mu_ax.legend([sub, byp, mu, etotact], labels = ['R1', 'R9', 'R7', 'AES'], handles=sub+byp+mu+etotact,loc = 'upper left') - mu_ax.xaxis.set_visible(False) - # mu_ax.legend([mu], labels=['growth rate'], loc='center left') - mu_ax.set_ylabel('flux rate', fontsize = FONTSIZE*3/4) - # add B panel annotation - mu_ax.annotate('B', xy=(-0.1, 0.1), xycoords='figure fraction', - xytext=(-0.1, 1.1), textcoords='axes fraction', - va='top', ha='left', fontsize=FONTSIZE*1.5, weight='bold') - mu_ax.set_ylim([-0.001,0.07]) - - # plot the sensitivity coefficients - vs = sens_ax.plot(glc_rates, substrate_csc, color ='orange', linewidth= 3) #(62/255,174/255, 137/255) - e1 = sens_ax.plot(glc_rates, e1_esc, color =(0.00,0.45,0.81), linewidth= 3, linestyle ='dashed') - e_tot = sens_ax.plot(glc_rates, tot_prot_csc, color ='darkred', linewidth= 3, linestyle ='dotted')#(62/255, 64/255, 137/255) - - sens_ax.legend([vs, e1, e_tot], labels=['$FCSC_{R1}$','$ESC_{E_{1}}$','$PCSC$'], loc='center left') - sens_ax.set_ylim([-0.01,1.1]) - sens_ax.set_xlim([0,0.1]) - sens_ax.set_ylabel('Sensitivity Coefficients', fontsize = FONTSIZE*3/4) - sens_ax.set_xlabel('$v_{substrate,max}$ $(mmol_{substrate}/g_{CDW}/h)$', fontsize = FONTSIZE*3/4) - - # Add a pie chart showing the enzyme sensitivities as an inset in the sensitivity linegraph - inset_ax = inset_axes(sens_ax, width="100%", height="100%", loc=3, bbox_to_anchor=(0.5, 0.15, 0.7, 0.7), bbox_transform=sens_ax.transAxes) #TODO - inset_ax.pie(pie_data, labels=pie_labels, textprops={'fontweight': 'bold'}, startangle=90, labeldistance = 0.6) - - return fig - -if __name__ == "__main__": - #NOTE:RUN THIS SCRIPT FROM THE MAIN DIRECTORY IN THE COMMAND LINE. - # otherwise the relative paths set in this script will result in errors - FONTSIZE = 16 - width = 10 - height = 12 - model = build_toy_gem() - active_enzyme = build_active_enzyme_sector(Config) - unused_enzyme = build_unused_protein_sector(Config) - translation_enzyme = build_translational_protein_sector(Config) - pamodel = PAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme, - translational_sector = translation_enzyme, - unused_sector = unused_enzyme, p_tot=Etot, - configuration = Config) - #optimize biomass formation - pamodel.objective={pamodel.reactions.get_by_id('R7') :1} - #over a range of substrate uptake rates - substrate_rates = np.arange(1e-3, 1e-1, 1e-3) - - simulation_results = run_simulations(pamodel, substrate_rates) - - #plot figure with multiple pannels - # gridspec inside gridspec - fig = plt.figure() - - gs0 = gridspec.GridSpec(2, 1, figure=fig, height_ratios=[3,2]) - gs_toymodel = gs0[0] - gs_sensitivities = gs0[1] - - image_path = 'Figures/Figure1_toy-model_overflow.png' - toy_model = np.asarray(Image.open(image_path)) - ax_fig = fig.add_subplot(gs_toymodel) - ax_fig.imshow(toy_model, aspect='equal') - ax_fig.annotate('A', xy=(2, 1), xycoords='data', - xytext=(-0.1, 1), textcoords='axes fraction', - va='top', ha='left', fontsize=FONTSIZE*1.5, weight='bold') - ax_fig.axis('off') - ax_fig.set_xticks([]) - ax_fig.set_yticks([]) - - for index, id in enumerate(simulation_results['x_axis_csc']): - if 'TotalProteinConstraint_proteome' in id: - tot_prot_csc = [row[index] for row in simulation_results['Ccsc']] - if 'R1_flux_ub' in id: - substrate_csc = [row[index] for row in simulation_results['Ccsc']] - - pielabels = [] - for index, id in enumerate(simulation_results['x_axis_esc']): - if 'E1' in id: - e1_esc = [row[index] for row in simulation_results['Cesc']] - if 'E4' in id: - pielabels.append('') - else: - pielabels.append(id) - - fig = plot_sensitivities(fig, gs_sensitivities, simulation_results['substrate_axis'], simulation_results['mu_list'], - simulation_results['substrate_consumption'],simulation_results['byproduct_formation'], - simulation_results['total_active_protein'], - tot_prot_csc, substrate_csc, e1_esc, simulation_results['Cesc'][-1], pielabels) - fig.set_figwidth(width) - fig.set_figheight(height) - plt.tight_layout() - - fig.savefig('Figures/Figure1_toy_model-sensitivities_overflow.png',bbox_inches='tight', dpi=600) - plt.show() \ No newline at end of file diff --git a/Figures/Figure1_toy_model-sensitivities_overflow.png b/Figures/Figure1_toy_model-sensitivities_overflow.png deleted file mode 100644 index 454aeea..0000000 Binary files a/Figures/Figure1_toy_model-sensitivities_overflow.png and /dev/null differ diff --git a/Figures/Figure2_mapped_sensitivities.png b/Figures/Figure2_mapped_sensitivities.png deleted file mode 100644 index 4abc03a..0000000 Binary files a/Figures/Figure2_mapped_sensitivities.png and /dev/null differ diff --git a/Figures/Figure2_sensitivities_gecko_pam.ipynb b/Figures/Figure2_sensitivities_gecko_pam.ipynb deleted file mode 100644 index 7366776..0000000 --- a/Figures/Figure2_sensitivities_gecko_pam.ipynb +++ /dev/null @@ -1,1055 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6d2a8cae-9e89-4faa-9b23-2c97b6186037", - "metadata": {}, - "source": [ - "# Code to generate figure 2 in the publication\n", - "Analysis of sensitive enzymes and reactions in the model simulations" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "3e02abee-b41a-4257-ae81-9f22b71d17b3", - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "import matplotlib.colors as mcolors\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "import sys\n", - "\n", - "import numpy as np\n", - "from PIL import Image\n", - "\n", - "from PAModelpy.configuration import Config\n", - "from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector, CustomSector\n", - "from PAModelpy import CatalyticEvent\n", - "\n", - "\n", - "if os.path.split(os.getcwd())[1] == 'Figures':\n", - " os.chdir(os.path.split(os.getcwd())[0])\n", - "# sys.path.append('../Scripts/')\n", - "from Scripts.pam_generation import set_up_ecoli_pam\n", - "\n", - "BIOMASS_RXNID = Config.BIOMASS_REACTION\n", - "DATA_DIR = 'Data'#os.path.join(os.path.split(os.getcwd())[0], 'Data')\n", - "PAM_DATA_FILE_PATH = os.path.join(DATA_DIR, 'proteinAllocationModel_iML1515_EnzymaticData_py.xls')\n", - "glc_uptake_rates = list(np.linspace(0.5, 10, 20))" - ] - }, - { - "cell_type": "markdown", - "id": "47bbc17e-1d0f-4375-922a-4f630333f787", - "metadata": {}, - "source": [ - "## sensitivities of GECKO and PAM" - ] - }, - { - "cell_type": "markdown", - "id": "fd5ff0a1-bc1e-42fe-bc4c-7d93571a5c27", - "metadata": {}, - "source": [ - "### 1 Usefull functions" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "69b86edc-c37a-420f-9c88-2fa9fd186b59", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_sensitivities(pamodel):\n", - " glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - " Ccsc = []\n", - " Cesc = []\n", - " y_axis = []\n", - " fluxes = []\n", - " \n", - " # disable pyruvate formate lyase (inhibited by oxygen)\n", - " pamodel.change_reaction_bounds(rxn_id = 'PFL', upper_bound = 0)\n", - " \n", - " for glc in glc_uptake_rates:\n", - " print('glucose uptake rate ', glc, ' mmol/gcdw/h')\n", - " with pamodel:\n", - " # change glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -glc, upper_bound = -glc)\n", - " # solve the model\n", - " sol_pam = pamodel.optimize()\n", - " fluxes.append(sol_pam.fluxes)\n", - " if pamodel.solver.status == 'optimal': y_axis += [glc]\n", - " # save data\n", - " Ccsc_new = list()\n", - " \n", - " if pamodel.solver.status == 'optimal':\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - "\n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of enzyme sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6),'\\n')\n", - "\n", - " return {'Ccsc':Ccsc, 'Cesc':Cesc, 'y_axis':y_axis, 'fluxes':fluxes, 'capacity coefficients':capacity_coeff, 'enzyme coefficients':enzyme_coeff}\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e47c6d59-277f-41bc-89a6-647ecc476120", - "metadata": {}, - "outputs": [], - "source": [ - "def parse_x_axis_heatmap(capacity_coeff, enzyme_coeff):\n", - " x_axis_csc = []\n", - " \n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if csc == 'flux_ub' or csc == 'flux_lb':\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[capacity_coeff['constraint'] == csc].rxn_id.to_list()]\n", - " else:\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == csc].enzyme_id.to_list()]\n", - " \n", - " x_axis_esc = enzyme_coeff.enzyme_id.to_list()\n", - " return x_axis_csc, x_axis_esc" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "cf73a888-2270-4ea9-9cc8-4c9e0f5eceaa", - "metadata": {}, - "outputs": [], - "source": [ - "def make_heatmap_subfigure(results, csc_matrix, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc,\n", - " ylabels = True, xlabels=False, cbar =True, title=None, fontsize = 16, \n", - " vmin = -1.5, vmax = 1.5, annotate = None, phenotype_data = None, cmap=None\n", - " #cmap = plt.cm.get_cmap('viridis')\n", - "):\n", - " # fig = plt.figure()\n", - " if cmap is None:\n", - " # Create separate colormaps for positive and negative values and a color for zero\n", - " colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128))\n", - "# colors_pos = plt.cm.PuOr(np.linspace(0.5, 1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128))\n", - "# colors_pos = plt.cm.PuOr(np.linspace(0.5, 0, 64)) # Use part of the PuOr colormap\n", - "# colors_pos = np.vstack((colors_pos, plt.cm.PuOr(np.linspace(0.5, 1, 64))))\n", - "# colors_pos = lighten_colormap(plt.cm.plasma)(np.linspace(0, 0.5, 64)) # Use part of the PuOr colormap\n", - "# colors_pos = np.vstack((colors_pos, plt.cm.plasma(np.linspace(0.5, 1, 64))))\n", - " colors_pos = plt.cm.OrRd(np.linspace(0.1,1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128))\n", - "\n", - " colors_zero = np.array([[1,1,1, 1]]) # gray for zero\n", - "\n", - " # Combine them into a single colormap\n", - " colors = np.vstack((colors_neg, colors_zero, colors_pos))\n", - " combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap')\n", - "\n", - " # Create a norm that handles the zero color properly\n", - " bounds = np.linspace(vmin, vmax, len(colors))\n", - " norm = mcolors.BoundaryNorm(bounds, combined_cmap.N)\n", - " \n", - "\n", - " if cbar:\n", - " gs = gridspec.GridSpecFromSubplotSpec(3, 2, width_ratios=[len(yaxis), 1], \n", - " height_ratios=[1,len(x_csc), len(x_esc)], hspace =0,\n", - " subplot_spec=grdspc)\n", - " else:\n", - " gs = gridspec.GridSpecFromSubplotSpec(3, 1, width_ratios=[len(yaxis)], \n", - " height_ratios=[1,len(x_csc), len(x_esc)], hspace =0,\n", - " subplot_spec=grdspc)\n", - "\n", - " \n", - " esc_ax = fig.add_subplot(gs[2,0]) #ESC heatmap\n", - " acetate_ax = fig.add_subplot(gs[0,0]) #acetate production\n", - " csc_ax = fig.add_subplot(gs[1,0],sharex = esc_ax) #CSC heatmap\n", - " if cbar:\n", - " cbar_ax = fig.add_subplot(gs[1:,1]) #colorbar\n", - "\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " acetate_ax.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.5), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize*1.5, weight = 'bold')\n", - "\n", - " glc_fluxes = [-sim.EX_glc__D_e for sim in results['fluxes']]\n", - " \n", - " #add arrow indicating growth regime\n", - " #0. remove the box to improve readability of the text\n", - " acetate_ax.spines['top'].set_visible(False) \n", - " acetate_ax.spines['right'].set_visible(False) \n", - " \n", - " #1. Find the start of the overflow regime (which is when acetate is being produced)\n", - " for i,ac in enumerate([sim.EX_ac_e for sim in results['fluxes']]):\n", - " if ac >0.01:\n", - " glc_onset = glc_fluxes[i]\n", - " break\n", - " #2. determine the dx covered by respiration\n", - " dx_respiration = glc_onset-glc_fluxes[0]\n", - " #3 create respiration arrow\n", - " #forward arrow\n", - " acetate_ax.arrow(\n", - " glc_fluxes[0], 11, dx_respiration, 0,\n", - " linewidth = 2, color = 'purple', label = 'Respiration', length_includes_head = True, head_width = 3, head_length = 0.5\n", - " )\n", - " #reverse arrow\n", - " acetate_ax.arrow(\n", - " glc_onset, 11, -dx_respiration, 0, head_starts_at_zero = True,\n", - " linewidth = 2, color = 'purple', label = 'Respiration', length_includes_head = True, head_width = 3, head_length = 0.5\n", - " )\n", - " #annotate\n", - " acetate_ax.annotate('Respiration',\n", - " xy=(dx_respiration/3, 15),\n", - " xytext=(10, -10), fontsize = fontsize,\n", - " textcoords='offset points', color = 'purple')\n", - " #remove the box\n", - " acetate_ax.spines['top'].set_visible(False) \n", - " acetate_ax.spines['right'].set_visible(False) \n", - "\n", - " #4. create overflow arrow\n", - " #forward arrow\n", - " acetate_ax.arrow(\n", - " glc_onset, 11, 10-glc_onset,0,\n", - " linewidth = 2, color = 'black', label = 'Overflow', length_includes_head = True,head_width = 3, head_length = 0.5\n", - " )\n", - " #reverse arrow\n", - " acetate_ax.arrow(\n", - " 10, 11, -(10-glc_onset),0,\n", - " linewidth = 2, color = 'black', label = 'Overflow', length_includes_head = True,head_width = 3, head_length = 0.5\n", - " )\n", - " #annotate\n", - " acetate_ax.annotate('Overflow',fontsize = fontsize,\n", - " xy=((10-glc_onset-2)/2+glc_onset, 15),\n", - " xytext=(10, -10),\n", - " textcoords='offset points', color = 'black')\n", - " \n", - " #acetate graph\n", - " acetate_ax.plot([-sim.EX_glc__D_e for sim in results['fluxes']], [sim.EX_ac_e for sim in results['fluxes']], \n", - " linewidth = 4, color = 'darkblue')\n", - " acetate_ax.tick_params(axis='y', labelsize=fontsize)\n", - " acetate_ax.set_xlim([0, 10.5])\n", - " acetate_ax.set_ylim([-0.5, 15])\n", - " acetate_ax.xaxis.set_visible(False)\n", - " if ylabels:\n", - " acetate_ax.set_ylabel(r'Acetate' '\\n' '[$mmol_{ac}/g_{CDW}/h$]',fontsize =fontsize, rotation = 0,\n", - " labelpad = 30)\n", - "\n", - " #add phenotype data if this is given\n", - " if phenotype_data is not None:\n", - " acetate_ax.scatter(phenotype_data['EX_glc__D_e'], phenotype_data['EX_ac_e'],\n", - " color='purple', marker='o', s=40, linewidths=1.3,\n", - " facecolors=None, zorder=0,\n", - " label='Data')\n", - "\n", - " if title is not None: acetate_ax.set_title(title, fontsize = fontsize*1.5)\n", - " \n", - " #CAC heatmap\n", - " im_csc = csc_ax.imshow(csc_matrix, aspect=\"auto\", cmap=combined_cmap, norm=norm)\n", - " csc_ax.set_yticks(np.arange(len(x_csc)), labels=x_csc, fontsize =fontsize)\n", - " csc_ax.xaxis.set_visible(False)\n", - " if ylabels:\n", - " csc_ax.set_ylabel('CSC', fontsize = fontsize*1.25, labelpad = 30)\n", - "\n", - " #Make line between CSC and ESC data more clear\n", - " axis = 'bottom'\n", - " csc_ax.spines[axis].set_linewidth(10)\n", - " csc_ax.spines[axis].set_color(\"black\")\n", - " csc_ax.spines[axis].set_zorder(0)\n", - " \n", - " #ESC heatmap\n", - " im_esc = esc_ax.imshow(esc_matrix, aspect=\"auto\", cmap=combined_cmap, norm=norm)\n", - " esc_ax.set_yticks(np.arange(len(x_esc)), labels=x_esc, fontsize =fontsize)\n", - " esc_ax.set_xticks(np.arange(len(yaxis)),labels = yaxis, fontsize =fontsize, rotation=45, ha='right')\n", - " if ylabels:\n", - " esc_ax.set_ylabel('ESC', fontsize = fontsize*1.25, \n", - " labelpad = 30)\n", - " if xlabels:\n", - " esc_ax.set_xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - " \n", - " #colorbar\n", - " if cbar:\n", - " cbar_ax.xaxis.set_visible(False)\n", - " make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm,\n", - " vmin = vmin, vmax=vmax, fontsize=fontsize*1.25)\n", - " # fig.set_figwidth(24)\n", - " # fig.set_figheight(7)\n", - " # fig.align_labels()\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "f1fc7967-5869-4911-8ed4-2cee897adbd7", - "metadata": {}, - "outputs": [], - "source": [ - " \n", - "def make_scaled_colorbar(ax, fig, cmap, norm,vmin, vmax,\n", - " fontsize=16, cbarlabel='Sensitivity Coefficient'):\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " \n", - " cbar = fig.colorbar(sm, ax=ax, cax=ax, shrink=1, fraction=1)\n", - " \n", - " # Adjust the tick intervals\n", - " tick_locations = np.linspace(vmin, vmax, num=5) # Adjust num to the desired number of ticks\n", - " cbar.set_ticks(tick_locations)\n", - " cbar.set_ticklabels([f\"{tick:.1f}\" for tick in tick_locations]) # Optional: customize tick labels\n", - "\n", - " # Setting the fontsize of the colorbar\n", - " cbar.set_label(cbarlabel, fontsize=fontsize, labelpad = 30)\n", - " cbar.ax.tick_params(labelsize=fontsize)\n", - " cbar.ax.yaxis.get_offset_text().set(size=fontsize)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "23c93efe", - "metadata": {}, - "outputs": [], - "source": [ - "def parse_reaction_id(input_str: str) -> str:\n", - " new_id = CatalyticEvent._extract_reaction_id_from_catalytic_reaction_id(\n", - " input_str, protein_id_pattern = r'^\\d+\\.\\d+\\.\\d+\\.\\d+$|^\\d+\\.\\d+\\.\\d+\\.-$')\n", - " return new_id\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ba775506-b0e9-4fc3-a403-7c68515059e7", - "metadata": {}, - "outputs": [], - "source": [ - "#adjust labels for better readibility\n", - "def adjust_heatmap_labels(labels):\n", - " new_labels = labels.copy()\n", - "\n", - " for i, label in enumerate(labels):\n", - " if 'EX_glc__D_e' in label or label[:-3] == 'EX_glc__D_e':\n", - " if label[-1] == 'B': new_labels[i] = 'EX_glc_'+label[-2:]\n", - " else: new_labels[i] = 'EX_glc_lb'\n", - " if label == 'TotalProteinConstraint_proteome':\n", - " new_labels[i] = 'Protein pool'\n", - " if label[0].isdigit(): #all enzyme ids start with a digit\n", - " rxn_ids = [parse_reaction_id(rid).split('_')[0] for rid in pamodel.get_reactions_with_enzyme_id(label)]\n", - " rxn_name = pamodel.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0]\n", - " new_labels[i] = '\\n'.join([part for part in rxn_name.split(' ')])\n", - " return new_labels\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f0b9ed5c-3082-4af5-8e58-5f56e30e5f79", - "metadata": {}, - "outputs": [], - "source": [ - "def find_nonzero_sensitivities(Cv, x_axis):\n", - " indices = []\n", - " for row in Cv:\n", - " for index, coeff in enumerate(row):\n", - " if abs(coeff)>0 and index not in indices:\n", - " indices.append(index)\n", - " \n", - " coeff_nonzero = []\n", - " for row in Cv:\n", - " coeff_nonzero.append([coeff for i, coeff in enumerate(row) if i in indices])\n", - " x_coeff_nonzero = [coeff for i, coeff in enumerate(x_axis) if i in indices]\n", - "\n", - " return coeff_nonzero, x_coeff_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "f3415083-0114-49b5-84f1-6d280deeef8f", - "metadata": {}, - "outputs": [], - "source": [ - "def find_top5_sensitivities(Cv, x_axis, yaxis, threshold = 0.05):\n", - " #top 5 enzymes per simulation\n", - " Cv_df = pd.DataFrame(Cv, columns = x_axis, index =yaxis)\n", - " largest = list()\n", - " for i, row in Cv_df.iterrows():\n", - " top5 = abs(row).nlargest()\n", - " if top5.iloc[0]:\n", - " largest += [index for index, value in top5.items() if abs(value)>threshold]\n", - " #remove duplicates\n", - " largest_list = list(set(largest))\n", - "\n", - " #extract non duplicate top5 enzymes\n", - " top5_df = Cv_df[largest_list].T.drop_duplicates().sort_index()\n", - " largest_list = top5_df.index.values\n", - "\n", - " top5_matrix = [list(row) for i, row in top5_df.iterrows()]\n", - " return top5_matrix, largest_list\n" - ] - }, - { - "cell_type": "markdown", - "id": "c259d078-bc25-4abc-9725-54b159fcbe79", - "metadata": {}, - "source": [ - "### 2 Run GECKO simulations\n", - "#### 2.1 Adjust the total protein constraint for the lack of translational and unused protein" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "edf9d83b-43b2-469d-a031-2a487f22efd7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.11123186965376783 g_p/g_cdw/h\n" - ] - } - ], - "source": [ - "#calculate average protein which is allocated to sectors (5 mmol_glc/gdw/h and growth rate of 0.35)\n", - "translational_info = pd.read_excel(PAM_DATA_FILE_PATH , sheet_name='Translational')\n", - "unused_protein_info = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name = 'ExcessEnzymes')\n", - "\n", - "#protein allocated to unused enzymes\n", - "ups_0 = unused_protein_info[unused_protein_info.Parameter == 'ups_0'].loc[2, 'Value']\n", - "smax = unused_protein_info[unused_protein_info.Parameter == 's_max_uptake'].loc[1, 'Value']\n", - "ups_mu = ups_0 / smax\n", - "\n", - "ups = ups_0 - (5*ups_mu)\n", - "\n", - "#protein allocated to translational enzymes\n", - "tps_0=translational_info[translational_info.Parameter == 'tps_0'].loc[1,'Value']\n", - "tps_mu=translational_info[translational_info.Parameter == 'tps_mu'].loc[2,'Value']\n", - "\n", - "tps = tps_0 + (0.35*tps_mu)\n", - "\n", - "#total protein left for the active enzyme sector\n", - "total_protein = 0.258 - tps - ups\n", - "\n", - "print(total_protein,' g_p/g_cdw/h')" - ] - }, - { - "cell_type": "markdown", - "id": "54a7cc6d-577a-48cc-bd51-e6965f3484e2", - "metadata": {}, - "source": [ - "#### 2.2 Build GECKO model" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "41159a71-2c44-4815-a5ec-41c380741133", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "Read LP format model from file /tmp/tmpddxh55pe.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.11123186965376783 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "# build the GECKO model with PAModelpy\n", - "pamodel_gecko = set_up_ecoli_pam(unused_enzymes = False, translational_enzymes = False, total_protein =total_protein)" - ] - }, - { - "cell_type": "markdown", - "id": "2b696f7d-d722-49ba-b5ff-4ac0e846aa79", - "metadata": {}, - "source": [ - "#### 2.3 Run simulations for glucose uptake of 0-10 mmol/gcdw/h" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "6b9e3329-f635-4e9f-be49-bfe044aeab30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "glucose uptake rate 0.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000222\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.019923 \n", - "\n", - "glucose uptake rate 2.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000759\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.070857 \n", - "\n", - "glucose uptake rate 2.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000616\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.059771 \n", - "\n", - "glucose uptake rate 3.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000602\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.052536 \n", - "\n", - "glucose uptake rate 3.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001816\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.164562 \n", - "\n", - "glucose uptake rate 4.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001337\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.160092 \n", - "\n", - "glucose uptake rate 4.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.009369\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.150594 \n", - "\n", - "glucose uptake rate 5.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.00918\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.126258 \n", - "\n", - "glucose uptake rate 5.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008899\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.091465 \n", - "\n", - "glucose uptake rate 6.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008635\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.058735 \n", - "\n", - "glucose uptake rate 6.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008834\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.084114 \n", - "\n", - "glucose uptake rate 7.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.009218\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.140851 \n", - "\n", - "glucose uptake rate 7.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.009026\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.124982 \n", - "\n", - "glucose uptake rate 8.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.008842\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.109759 \n", - "\n", - "glucose uptake rate 8.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.011449\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.443317 \n", - "\n", - "glucose uptake rate 9.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.012052\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.522156 \n", - "\n", - "glucose uptake rate 9.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.012018\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.520266 \n", - "\n", - "glucose uptake rate 10.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.011991\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.518 \n", - "\n" - ] - } - ], - "source": [ - "results_gecko = calculate_sensitivities(pamodel_gecko)\n", - "x_axis_csc_gecko,x_axis_esc_gecko = parse_x_axis_heatmap(results_gecko['capacity coefficients'], results_gecko['enzyme coefficients'])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "0e45ccd4-69f1-4ca5-96fc-5e9c77523197", - "metadata": {}, - "outputs": [], - "source": [ - "#get all nonzero sensitivities\n", - "csc_nonzero_gecko, x_csc_nonzero_gecko = find_nonzero_sensitivities(results_gecko['Ccsc'], x_axis = x_axis_csc_gecko)\n", - "esc_nonzero_gecko, x_esc_nonzero_gecko = find_nonzero_sensitivities(results_gecko['Cesc'], x_axis = x_axis_esc_gecko)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d285624b-a4e2-4189-a7c1-c3abd74b7e05", - "metadata": {}, - "outputs": [], - "source": [ - "csc_nonzero_gecko_t = np.transpose(np.array(csc_nonzero_gecko))\n", - "esc_nonzero_gecko_t = np.transpose(np.array(esc_nonzero_gecko))" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "54338172-b87a-4b41-9cca-d189c740e15a", - "metadata": {}, - "outputs": [], - "source": [ - "#get top 5 nonzero sensitivities\n", - "csc_top5_gecko, x_csc_top5_gecko = find_top5_sensitivities(results_gecko['Ccsc'], x_axis = x_axis_csc_gecko, yaxis = glc_uptake_rates)\n", - "esc_top5_gecko, x_esc_top5_gecko = find_top5_sensitivities(results_gecko['Cesc'], x_axis = x_axis_esc_gecko, yaxis = glc_uptake_rates)\n", - "csc_top5_gecko_t = np.transpose(np.array(csc_top5_gecko))\n", - "esc_top5_gecko_t = np.transpose(np.array(esc_top5_gecko))" - ] - }, - { - "cell_type": "markdown", - "id": "e58822c2-7c17-4c99-b0c5-cd52c6862db9", - "metadata": {}, - "source": [ - "### 3 Run PAM simulations\n", - "#### 3.1 Build PAModel" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bf9cd6cb-6992-4df6-a10b-47405063969e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmpm41if_f3.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.258 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel = set_up_ecoli_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "a87b7228-cc6b-42d7-81b1-ed069b20b55d", - "metadata": {}, - "source": [ - "#### 3.2 Run simulations for glucose uptake of 0-10 mmol/gcdw/h" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "191d8735-d3a4-4fc0-afd7-960dc42bb628", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "glucose uptake rate 0.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002288\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.246793 \n", - "\n", - "glucose uptake rate 1.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000682\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.09303 \n", - "\n", - "glucose uptake rate 1.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0004\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0661 \n", - "\n", - "glucose uptake rate 2.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000288\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.055724 \n", - "\n", - "glucose uptake rate 2.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.00103\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.157977 \n", - "\n", - "glucose uptake rate 3.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000931\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.16114 \n", - "\n", - "glucose uptake rate 3.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.000581\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.162681 \n", - "\n", - "glucose uptake rate 4.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002886\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.891683 \n", - "\n", - "glucose uptake rate 4.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.003329\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.128506 \n", - "\n", - "glucose uptake rate 5.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.003113\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.13419 \n", - "\n", - "glucose uptake rate 5.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002869\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.117895 \n", - "\n", - "glucose uptake rate 6.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.00266\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.103972 \n", - "\n", - "glucose uptake rate 6.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002479\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.091937 \n", - "\n", - "glucose uptake rate 7.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002351\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.095135 \n", - "\n", - "glucose uptake rate 7.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002211\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.086089 \n", - "\n", - "glucose uptake rate 8.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.002087\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.07806 \n", - "\n", - "glucose uptake rate 8.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001976\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.070886 \n", - "\n", - "glucose uptake rate 9.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001876\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.064437 \n", - "\n", - "glucose uptake rate 9.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001786\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.058608 \n", - "\n", - "glucose uptake rate 10.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.001704\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.053314 \n", - "\n" - ] - } - ], - "source": [ - "results_pam = calculate_sensitivities(pamodel)\n", - "x_axis_csc_pam,x_axis_esc_pam = parse_x_axis_heatmap(results_pam['capacity coefficients'], results_pam['enzyme coefficients'])" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "3f87f6f6-a880-4adb-b1f8-3daf07f8a9f6", - "metadata": {}, - "outputs": [], - "source": [ - "#get nonzero sensitivities\n", - "csc_nonzero_pam, x_csc_nonzero_pam = find_nonzero_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam)\n", - "esc_nonzero_pam, x_esc_nonzero_pam = find_nonzero_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam)\n", - "csc_nonzero_pam_t = np.transpose(np.array(csc_nonzero_pam))\n", - "esc_nonzero_pam_t = np.transpose(np.array(esc_nonzero_pam))" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "b893abf9-cc29-470d-85ab-23e575200079", - "metadata": {}, - "outputs": [], - "source": [ - "#get top5 nonzero sensitivities\n", - "csc_top5_pam, x_csc_top5_pam = find_top5_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam, yaxis = glc_uptake_rates)\n", - "esc_top5_pam, x_esc_top5_pam = find_top5_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam, yaxis = glc_uptake_rates)\n", - "csc_top5_pam_t = np.transpose(np.array(csc_top5_pam))\n", - "esc_top5_pam_t = np.transpose(np.array(esc_top5_pam))" - ] - }, - { - "cell_type": "markdown", - "id": "999ba86b-fa39-470a-ad63-9c99da737ee7", - "metadata": {}, - "source": [ - "### 4 Create plot" - ] - }, - { - "cell_type": "markdown", - "id": "df74f52e-fdf7-4b82-b797-297bd4a139a7", - "metadata": {}, - "source": [ - "#### 4.1 Load phenotypic data" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "b11ae446-27b5-4e18-b3bb-9553acbb397c", - "metadata": {}, - "outputs": [], - "source": [ - "# load phenotype data from excel file\n", - "pt_data = pd.read_excel(os.path.join(DATA_DIR, 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'), sheet_name='Yields', index_col=None)\n", - "pt_data['EX_glc__D_e'] = -pt_data['EX_glc__D_e']" - ] - }, - { - "cell_type": "markdown", - "id": "1feb4cc2-599f-4947-af30-daa66489dd3f", - "metadata": {}, - "source": [ - "### 4.2 Load manually created plot with mapped sensitivities" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7280d7de-3ee7-46ef-aa2c-945f4401db78", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Software/anaconda3/envs/PAModelpy/lib/python3.9/site-packages/PIL/Image.py:3182: DecompressionBombWarning: Image size (109343152 pixels) exceeds limit of 89478485 pixels, could be decompression bomb DOS attack.\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "image_path = os.path.join('Figures','Figure2_mapped_sensitivities.png')\n", - "\n", - "sensitivities_mapped =np.asarray(Image.open(image_path))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "419a3716", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_138825/167582513.py:24: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig_pam.subplots_adjust(left=0.3)\n", - "/tmp/ipykernel_138825/167582513.py:40: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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MLi1Yrpy/N3q3rYNX2tVF5dK+0CgKxwsQkUUMkyMiIqJHMEyucGKYXOHCMLnCh2FyhQ/D5AoXhskVQoX0ZRdmhS1siRgmR4VHYfv3jWFyBR/D5IiIiKgwk1Ji9ss/4eSG03ldyqME8NyEzuj8QUcOlLWTkAPn0f6rHYjTOgOW3lMpUScyFMPO/gU3U2qeBMppPdzQfPYnKNXpaZv2vSolouOTsfPENSz4IwQHzt5EfJIRkIDMzYuwQkARQBF3F1Qr44dW9Sri6VplUbNCCfh6ukCn1fy/GT/fREREREREREREREREREREeUFKCaPJjLCIeOw+dQ0bDl7Agb9v4n5MIlQpHX6vlxACihCoVqYoBnZugJda10ZxHw8ohfTeYSKyjGFyRERE9AiGyRVODJMrXBgmV/gwTK7wYZhc4cIwuUKokL7swqywhS0Rw+So8Chs/749aWFyC1A4w+QGAggMDIRGo0FwcDCCg4PzuiwiIiKiXGFIMuDDwI+QEJmU16U8pky90vhw77tQNEpel1LgmZKSsbXzUGwNN+Gnqi2QrNFnGijX9N5lDLiwB85mY+4HygEQOi0afPImqgzrBaFkvf/l/wPkth+/ioV/HMX+v28iMcWYKzOGpxFCQKdVUNrPE02ql0H7BhXRpGZZBBT1hrNeCyEYHEdERERERERERERERERERJSfSSmRajTh+t0obDt6BWv3nMWJy2G5MgZBCAG9ToOnqpfBiG5N0L5BJXi4OnGsAREBALR5XQARERERERERERERERERUUEREhJSoAMBiYiIiGwRGx6HxOjkvC4jQ/evRMCQZIBzIZ1Ay16kqiLkwymIPn0RDaVEokaHZYHNYNBYmKxJCBzyrwRXowG9rxyAXs39wHBpNOHY+GkwJaeixtv9MwyUk1IiPikVe8/cwJzfD2PXyeu5FiCXNlDbSadBxZK+aF23PNoHVULDKqXh5+UGrUbhYG4iIiIiIiIiIiIiIiIiIqICRggBZ70O1cr6o2qZYniz21OIS0zByat3sWbPWfxx6AJC78XCrKp271tKiVSDCTtPXsOuU9fh7e6Ebs1r4PXnG6NWxRLQcSwCUaHGMDkiIiIiIiIiIiIiIiIiIiIiIiKyKDosBrD/+Fa7MCQZkBiTxDC5HJBSInzvUVxbthGQEgqAFmEXkKhzwpoKjWBWNBmvJxTsLFUd7qYUPH/9GLQy9z8kUlVxatJsQAI1Rj0MlJNSIsVgwrFLdzBr/SFsOXIJMYmpuRYgp9MqKFvMC23qVUC3FrVQr3IJFPFwhUYRHLBNRERERERERERERERERET0BBFCQAjA28MFrepWQMs65fHdiM64ER6NLUcuYeWuMzh+MQwpRpPdxy1IKREdn4KFm49j0dbjqFzKB8Ofb4JebeqgmLcbxygQFUIMkyMiIiIiIiIiIiIiIiIiIiIiojzx70GSaf/974GMHNSYP8TcjQXg+CAuW0hVwpxqyusyCjRzqgFH3vkK8l8zYmsg0Sn0NBK1zthctg5UoWS4rqoo2FS2LtyMqehw6wyUPPicSFXFqS/mQOq0UJ5pi0XbT2LF9lO4G53wcLkDQ+TSBoX7e7ujRd3y6NmyJprWKIOiXm7QcrZvIiIiIiIiIiIiIiIiIiKiQuXhJHQaVC7th0qlfBH8QlNExCVi3+kbWLLtBPaevo6YBPtOiCchIVXg4q1IjJ7xB8bP34YOQZXxVo+maFK9LHRaDccvEBUSDJMjIiIiIiIiIiIiIiIiIqto/v9TmKS93qCgIGg0GgQHByM4ODhPayIiItslJycjPDw8y3ZCCAQEBECjyfwvX3a35wg6nQ6lSpV6ZMBfTuoRQsDZ2RkuLi5wdnaGTqf7f1iSfQYUSilhMpkQGhqKI0eO4OjRowgNDUVkZCQSEh4GPxUtWhSenp4oW7Ysypcvj8DAQJQrVw7FihWDs7MzFMVxAU1msxm3b9+G+q9QrYx4enrC19c3W9uUUiIsLAwGg8GqWtLedzc3N+j1+vT9kbYsV6kSUgjAgaFcOZFPyyoQpJS4uvR3xF+79dgbqZUqXrh+FIk6J+wuWe3hZyADJqHBmoqN4GpKRfO7F5Fx7JxjSbMZJz/5ERtmb8Q6/wowwzG/I2m/e65OWtStVAI9WtbCM40DUdbfG046LQdfExEREREREREREREREREREYB/Jqgr5u2O7i1qotvTNZCQbEDIhdtYvPUYthy+hIi4ZEhIu83xKKVEYooR6/eex2/7z6N88SJ4o1tTvNKuDvy83DiugegJxzA5IiIiIiIiIiIiIiIiIqJsCgkJgaenZ16XQUREOSClxJQpUzBx4sQs2yqKgv3796NRo0aZtjt8+DDatm1rrxKtUrlyZZw9e/aRwLuDBw+iffv2Nm0vLcBMp9PBy8sLlStXRrt27dC9e3dUqVIFWq1tYUlSSiQmJmLRokX44YcfcPXqVaiqmuUsu2l9CSHg6emJ8uXLo2XLlmjfvj1atmwJNzc3m16npRqXLFmCIUOGZFnX22+/jW+//TZb74WUEs8//zxOnDhhdU1CCCiKAr1eDx8fH1SsWBEtWrRA165dUatWLej1+lwZ5OlV3MvhfdhMABp9YYs6th9zcir+nrrQYiKfXjWh1+WDSNQ6IaRYBSCjz5sQSFW0WF65GdxMqaj/4IaDotwyJ1QVHcNv4I7OFQeLFM+4Vlu2+//B3f7e7ujUuDJeal0LQVUC4O3uAiHyINyRiIiIiIiIiIiIiIiIiAjIcmwDYN/vM6WU6ZMIJiUlISUlBUajEQaDAS4uLtDpdHBzc4OTk5NNEwVm5/X8V259X+uo9zq39yHlLSEEPFyd0KZ+RbSuVwFJKUacunoXizYfxcaDF3AvJsmm34OMSEhIFbgaFoXRM//AhAV/4vnm1TD6xadRq0JxaDV5MVUgETkaw+SIiIiIiIiIiIiIiIiIiIiIqNAwGAxYsGABVFXNsq2UEjNnzkRQUFCWAzPTBszmtoxeR1otOanHZDIhOTkZ4eHh2LdvHz755BPUqFEDkydPxjPPPPNIeF1WpJTYv38/Xn31Vdy4ccOqutLaSikRExODEydO4MSJE5g2bRrWrFmDbt26Wf3aLLl9+zbeeecdmM3mbNeVXVLKbH3mMmI2m2E0GpGYmIhbt25h9+7d+Pzzz1GhQgV8/PHHePnll6HVOnYYmFcJLwhhMW8sT+mcdXDzds3rMgqssD/3Izk8wuJyAcDFbED/i3uQrNXjb5/SFgPlkrR6LKrSEq5GA6rGhOVJoJxequgVdhm3Xdxxy9nd5kA5IQS0ikCl0r54sWUtvPB0dVQtUxROOtsCNYmIiIiIiIiIiIiIiIiI7O3IkSNYunSpxeVarRZffPEFXFxcbO5DSgmDwYDz589j48aN+Ouvv3D58mVERUXBZDKlj7EQQkCj0cDNzQ0BAQFo0qQJunTpgqeeegpFihSBomQeXCWlxLx583D69OlM26X1U7RoUTg7O0Ov18PDwwOlSpWCn58fihUrBm9vbzg7O9sUaJcRVVXx6aefIiLC8nfrzZs3x8svv2xVfzExMfjoo48yHU/y2muvoUGDBlbVSwWDEAJuLno8VbMsmtYog+RUI45evIMFfxzFpkMXERWfDMC2kMX/klIiIdmA5X+exi9/nUHdSsUxplcLPNukKlyddRwHQfQEYZgcERERERERERERERERERERERUap06dws2bN7PVVkqJDRs2ID4+Hp6eng6uLP9KC6Y7c+YMnn/+efTs2RPz58+Hh4dHttb95Zdf8Nprr8FgMNhv9twchLNlxGQyYdCgQYiJibHbNh0lbX9cuXIF/fr1w6+//orly5fD3d3dYX16l/CEk5sTUuJTHNaHrXwCisDJzSmvyyiQpKri3MxlWaYECgAexhQMOr8TP9bqiKuexSwGysXqXTCvemu8eWYbysU/yPVAOQHA25SKPncuYlr5ukjWZH+IpBACTjoN6lQsjlfa1UXXp6qhdFEvaBTBgdNERERERERERERERERElO9cuHABM2bMsLjc2dkZH3/8sU1hclJKREdHY/bs2Zg7dy5u3bqV5cSGZrMZBoMB0dHROHPmDObNmwcXFxe0adMG7733Hpo2bZrpZHlbtmzBunXrrK7139/nKooCd3d3lCtXDm3btkXPnj1Rr149ODk52fy9r6qq+Pnnn3H16lWLbYxGI15++WWrtpuQkICZM2dmOunh008/zTC5QkAIAVdnPVrUKY+na5dDYooRh86FYvbvh/Dn0atISLbPmCsJCbMqcexSGHp/9iv8i7hhaNdGGNolCCV8PTk2gugJkHl0KxERERERERERERERERHRf2gK6Q8ABAUFoXr16pkOwCIiovxLSok5c+ZYNbguOjoaO3bscGBVBYuqqli1ahXatm2L+Pj4LNufPHkSgwcPRmpqqt2C5OxNSonFixfjr7/+yrc1WiKlxMaNG9G3b1+YTCaH9ePk5oTS9Uo5bPu2EkKgRsfqEBoOZrVFUvgDRJ28kK22AoBPaiKGnNuBUonRlgPohECEswfmVm+Du67eyIvfKAGgakI02kXcyjIoD0LAWa9Fi9rlMH9Md1xbMQb7fxyON7o1RbniRaDV2GemeiIiIiIiIiIiIiIiIiKigsJoNGLu3LkIDAzE+PHjcfPmTaiqatWYirTguaSkJGzatAmtWrXC4MGDHTIuI60vKSXMZjNiY2Nx6tQpTJ06Fc2aNUNgYCCmTp2K+Pj4AjcuhAofIQTcXfRo16ASVn3cBzd+eQ/Lx7+M5rXKQq/V2G0Mg5QS4VEJ+HTxDgT2+w79J6/C39fDYbbj5J5ElPsYJkdERERERERERERERERElE0hISE4d+4cgoOD87oUIiKyQXx8PNatW2f14NYffvgBKgfKpZNS4ujRo3j99dczfV+MRiOGDBmC5OTkbG1XCPHIT265desW3nnnnQK7j6WU2LBhAzZv3uywPoQi0GpQ8/wXqiWAJr0b5r+6Coi7Ow9DNWY/hFAAKJEUg6HndsAvJT7TQLk7bkUwr3obRDm55UmgnAKg4/2bCEhJeKxOAUCv06BZrbJY8F4PXF8xBjumDsGATvVR3McDisIAOSIiIiIiIiIiIiIiIiIqnCIiItClSxe8/vrriIyMtEv4mpQSqqri7t27dqjQun6llLh16xbeffdd1KxZE3/88UeBHR9ChY8QAkU8XPBym9rY9f0QXPr5HXwxpAMql/aFogjATmMbklKMWLb9FBoM+RHtRs3HrhPXYDSZGb5IVABp87oAIiIiIiIiIiIiIiIiIiIiIqLcsH37dsTExFi93v79+3H37l2UKlXKYpvsBqClDbLLbLCdNWFqtgQeCSHg5eUFZ2fnx2pLSEhAUlJSljVKKfHLL79gyJAhaNmyZYZtQkJCcOLECYvbEULA2dkZzZs3R/v27VGpUiX4+vpCSonIyEiEhobixIkTOHbsGK5du4aUlBS7D1I0mUwYNGgQ4uLi7Lpda2m1Wvj6+j6yP41GI+Lj42E0GrN83aqq4vPPP0fnzp2h0WgcUmOtZ2rAzc8VCQ8SHbJ9awkhULpOKZSqWTKvSymQpJS4tWm35UA4CwSAcvEPMOTcTsys2R6xepeMBycLgauexfBTtdYYfnY7PIwpyO14Ng+zEc+FX8fssjWhCgGtVkHtCsXRv1MDvNCsGkr6eT0cXE1ERERERERERERERERERAgLC0ObNm1w6dKlJy5ESkqJ0NBQPP/88xg1ahQmT54MnU6X12URZZuiKCjj740xvVrg7Reb48zVu/hx3UGs33cecUmpOf6dlZAwmiV2n76Bdu/+hGplimFc35Z4rll1uDjpOCkfUQHBMDkiIiIiIiIiIiIiIiIiIiIieuKpqopZs2bZNHDOYDBg1apVeOuttzIcGBcUFIS///47W9u6e/cunn/+eSQkJFhs89JLL+Gjjz7K1iA8vV4PRVGy1fe/7dixA9WrV3/s+ZSUFFy8eBGTJk3Cxo0bM52N2Ww2Y/LkyXj66acfq0FKiWXLlllcXwiBpk2bYvny5QgICLAYoJc2O3V0dDRCQkKwePFibN26FbGxsVa+4sdJKbFo0SL89ddfj3wuhBBQFAVmsznHfWRX27ZtsW7dukfeR1VVERsbi127dmHcuHG4fv16pp/fU6dOITo6Gn5+fg6p0cndCe3fbov14zfkj0HjAuj+6XPQaB0TnvekUw1GRB0/a9O6AkDVmDAMuLgb86q1QZJWbzFQ7m+f0lhcpQUGnd8FF7MhVwPlBIB68RFo7WTG033bo0+7eijr7w1FyX5gJxERERERERERERERERFRYfDgwQOrguTSxla4urrCy8sLQgjEx8cjOTkZBoMBQOaTGOYVs9mMKVOmICoqCrNnz2agHBU4QgjotRo0qFIaC97vialvpGDjwQuYtno/Tl4JhzmTsV7ZpaoSZ2/cQ59Jq1C6qCc+6N0Sr7SvC09XJ463IMrnGCZHRERERERERERERERERFZR/v9TmKS93qCgIGg0GgQHByM4ODhPayIiIuuEh4dj3759Fpe7ubkhNTUVJpPpsWVSSsydOxfBwcEZDiJ1c3NDtWrVslVHuXLloNfrM21Tt25dVKtWzaGD74oWLQonJ6fHnndyckKjRo2wdu1aTJkyBR9++GGmgXL79u1DTEwMfHx8HnleVVUcPHjQ4nru7u5YvXo1SpQokWmdQghoNBr4+fnhmWeeQadOnRAbG4uNGzfC398/i1eZudDQUIwZM+axILlq1aqhcePGWLhwYY62bw03Nzc4Ozs/ts9dXFzw8ssvo02bNggKCkJoaKjFbRgMBty+fdthYXJCCLQZ3gL7lxzE/YsPHNKHFcWgeoeqqNo6MG/rKMBSo2OREhVj8/oCQL0HN9BHux9LqjyNVI2FAfZC4GixCnAzpaLPpf1wUh//N9aRdFLFB+Vd0bpfGw5oJiIiIiIiIiIiIiIiIiLKgMFgQK9evbIVJKcoCurXr48RI0agdevW8PPzSx9/YjQakZycjOvXr+PPP//EihUrcO7cOaiqapdgOZ1Ohxo1akCj+WfSuaSkJERERCA2NhZGozFb/UgpsXDhQhQpUgRff/21TZM4EuUHQgh4u7ugb/t66N2mDi7eisDM3w7ilx1nEB2fnOPfOyklbt2PxRvTN+DjRX/h7Z7NMOz5Riji7sIxGET5FP+iERERERERERERERERERFlU0hICM6dO8cgOSKiAkZKiZUrVyI1NTXD5UIIjB07Fp06dbK4jYsXL+L8+fOOKjHf0Wg0GDVqFOrWrZtpu+TkZFy7du2x500mE+7cuWNxvXr16tkUBieEgLe3N/r06YNmzZpZvX4ao9GIgQMHIjY29pHn9Xo9Fi9ejKefftrmbdubEAJFixbFBx98kOVAzPj4eIfWonfVY8iiAdC55N3M3EIIeBZzx6szX4Gi4fA3WyXevgfVaM7RNhQAT929hB5Xj0CbSUicFAJ7SlbF+vINYRS5u8+ElLi/cSeSwyNytV8iIiIiIiIiIiIiIiIiooJASonZs2dj586dmQZPCSHg7++P9evX4+DBg3jttddQrlw5uLu7Q6fTQafTwdXVFb6+vmjYsCE++OADHD9+HCEhIejatSu0Wm2Oa61ZsyYOHz6MI0eOpP+cOXMGoaGhuHnzJjZt2oQePXpkOJlfRq/7+++/x8aNG+0SdEfWkVI+8kM5p9EoqF6uGH4Y+RyuLn8XC9/vgdoVikNRRI6D36SUeBCbiPEL/kRgnyn4cO5mhEfFc98R5UMcTUdERERERERERERERERERERETzSTyYR58+ZZHMCm1+vRv39/DB061OLgOSllptt4Emm1Wrz66qtZDii8evXqY8+pqgqj0WhxHbM5ZyFWQtg+0FFKiblz5z42EFoIgTFjxqBBgwaPzGKdHwgh0KJFi7wuAwAQUKc0+kx/GYoub4ae6Vx0GP7LYHgV98yT/p8USbfD7TJLtAYS7W7/jWdvnoSiqhbbqULBljK1sbVMbZiRu7NTG5OScWXhmkL17zcRERER5V//vUlSSgmpZvAcj1/zjQz3GfdXvsJ9RERERERERGS7yMhIfPzxx1kGyZUpUwYHDhxAly5dshUMJ4SAVqtFvXr1sG7dOuzbtw+VK1fOUa0ajQZarRaKoqT/aDQaODs7o3jx4njmmWewatUq/P333+jYsWOW34mbzWaMGDHC4RP32ZvBYEBMTIzFn5SUFKu3mZycnOk2Mxrnk5SUlOk6JtOjk9KpqorQ0FAsW7YM7777Ll555RW8+uqrmDhxIpKSkmx+P+hRQgh4uTmjX4d6ODo3GIdmvI6XWtWEm7POLqFyUfHJ+ObX/ajSbwrGzGKoHFF+k/PoViIiIiIiIiIiIiIiIiIiIiKifOzChQu4ePGixeUNGjRAiRIl0KJFC3h6eiI2NvaxNlJKrFq1Cl999RVcXV0dWW6+IYRAjRo1Mm0jpURCQkKG62YWyHbixAncuHEDFSpUyHGd1rp27Ro+/PDDx4Lk6tSpg3HjxtklYMsR3NzcIITIdABmbnw2hRBo0icIqYkp+PXdtVBNlgPE7E3vqseQpQNQoXG5fLufCoqE0DDAToN5tVJF1xvHkah1wl+la0CKjIMGzYoG68s3hJsxFS3DzufaTLgCwOUl61D97f7QurrkUq9ERERERA/Pmc1GMxKjkhB1Oxr3rtxH+MVwxIUnIOZODIypJphNZkhVQigCGq0GOhctvEt5wauYF4pVKopiFYvCJ6AI3HzdoNNrAQGHnA+lhdrlJ0KxPUjeVlJKmA1mJEQlIvJmFO5duY97l+4jJiwGsffiYUo1pZ8HK1oFOictigQUQZEAbwTUKIXiVf1RpKQ39K56u+0rW/eNPd+/9DA2Gz8iQggIxY61mCWS41MQFRqF8Ev3ce/SPUTfiUHU7WgYU01QjQ/3kdAI6Jw08PL3gk+ZIvCv4o+S1YrDr6wvnD2c8+QzRkRERERERJSfSCkxZ84cxMTEZNrO09MT27Zts3mMh6IoaNy4MRo0aGDT+tYQQqBixYr4/fff8fHHH+PLL7+EmsnEaGFhYZg7dy7eeeedAnOdICwsDHXr1oXBYMhweVqonqJk71t5s9mMjh074ujRoxkud3Nzw8WLF+Hj45P+nJQS7777LhYtWmRxu9u2bUPz5s0hpcSVK1fw9ttvY/v27TAajY+MfdHpdHjzzTcLzXis3CKEgFajQcOqpbF8Qi/ci0rAT5uPYvZvhxEWmbMAOCkl4pMMmLp6P+ZuDMGQZxtiTK8W8PdxLzC/R0RPKobJEREREREREREREREREZFVNP//KUzSXm9QUBA0Gg2Cg4MRHBycpzUREVH2SCkxb948iwNDhRAYNGgQFEWBp6cnOnbsiFWrVmU4YO7+/fvYu3cvOnbs6Oiy843szFac0eBTnU4Hf39/REREZLhOcnIy2rdvjzlz5qB58+ZwcnLKlcGERqMRAwYMeCwAz9nZGUuWLIGzs7PDa7BVTExMpgM5dTodAgICcqUWIQRaDn0aTh5OWP7mKhiSjXYLJsu4Q8Ddzw2vrxiMik9V4MDTHJJSwpRk/UzkmdGpZrx49TASdU446F8ZsLCPDIoWv1RuCjdTKoLuX0Nu7cnk+9G4f/g0SrZunEs9EhEREVFhJKWEalYRdSsaF3Zdwpk/zuLmiVuIuxcHaZbpbbIr7dxHKAKuPq4oUcUfVdsEolbHGihetTj0Ljq7nR8dW3MSf3y11S7bsgdFEXhj/XB4l/ByaD9pQW0xd2Nx/q+LOLXpDG4eC0Xc/XhIs3yYnZbNfSbEw/9x8XRG6dqlUO/5Oqj1TA34lvHJUWjZpb1X8Os7a6xaR++ix9t/BMPZ3cmmPv9NqhJHfj2KP6ftgGq2/tzfJ6AI+v74MrxLetteg5RITTTg+tEbOPn7aVzYcQmRoVEwpZgAyGxfkhAQgAB0LjoUDyyG2l1qomHP+vCvVAyKJrciz4mIiIiIiIjyD4PBgJ9++inTa1ZCCHz55ZeoXLlyjvvTanMv4kan0+HTTz/FrVu38PPPP1t8jVJKTJs2DW+88Ua+HjPyb2XKlEHTpk2xZcuWDJdv3rwZ9+7dQ4kSJbK1vfDwcBw+fDjDcDohBLp06YIiRYo8tsxoNCI5OTnDbQohoKoqpJRYu3YtBgwYgMTExBwFmJHthBAo7uuBsX1a4e2ezbDxwHl8tXwPTl8Px8MhdbbtFyklEpJT8f2aA5j/x1EM6RKEMb1aoJi3G8f2EOURhskREVG+9+8DxYkTJ+Ljjz/Ou2JyaNGiRXjttdfSH1+/fh3lypXLsG2rVq2we/duAEDLli2xa9euXKiQiIiIiIiIiIiIiDITEhICT0/PvC6DiIiskJSUhJUrV1ocjOji4oKuXbsCePjd5LBhw7Bq1aoM20opMWPGDHTo0KFQDHiTUmb5PaUQAkWLFn3seUVR0KRJE5w9e9bitq9fv46OHTuiVKlSaNeuHTp27Ij69eujZMmScHFxgRC232htqc/Zs2dj//79j3wehBAYN24catasabe+7E1KaXEQcJpq1ao9Mgu0owkh0KR3I5SuVQoLBizB3fP3HDLoVwiBSs0rYtCCfvAu5V0ofvdygzSZ7bo9AcDZbES/i/uQpHXCKd8yGQfKCYFkjR6Lq7SAq8mAGlG3cy1Q7sqSdSjRqhE/Q0RERERkd1KViAmLxZFfj+LQ8iMIv3gfUpU5PkdKW1+aJRIeJODygwRc3n8NGz/bAjdfN9RoXw3NBzZF+YZloXXS2nysK6XE6T/O4M7fYTmq157cfF3h6u3isO1LKZEYmYhj605i36IDuH0mDNKUs30m5cP/SYpJxqU9V3BpzxWs+mAdAmqVRrs3W6FO11rQu+qt2k9SSpzbfsHqfVOiqj/0zjorX8HjTAYTfvt4E7ZP3wnVnPFEDZYIIVCxWXn0n9MH7n5uNvWvmlTc/vsOds7cjVOb/kZS9MObk23dTxISkIAhyYDQk7cRevIO/vhiGwLqlELX8Z1RvV1VaHSFbTolIiIiIiIiKsyuXLmCmzdvZtomICAA/fv3L5Dfs2o0GkybNg1bt27F/fv3Lba7c+cOTp48iSZNmuRidbZTFAXvv/8+tm3bluHkmikpKVi6dCnGjBmT5X6TUuL333+H0WjMcLkQAiNHjrRp/5tMJmzevBl9+vRBamqq1euT/Qkh4Oasx8tt6qBHy5o4cv42vly2C9uOXoHBpCInoXLxSamYumo/5m8MwcjuTTDqpRbwdncukP92EBVknDaFiCibbty4kT5QPSc/devWfWzbw4cPf6TNhAkTrK7v3r178PPzS9+Gq6srLl++bIdXTkRERERERERERERERERUcO3duzfTAaHNmjWDr69v+uPGjRs/8vi/duzYgQcPHti1xvwqKioq09mZgf8HfVWqlOHzffv2haJYHp4kpYSqqrh16xYWLlyI3r17o2rVqihdujSaNGmC0aNHY9OmTbh37x7MZnOOb8K/evUqxo4d+1iQXP369fHuu+/m28GLUkpcuXIF3377rcX3QAiB9957DxpN7t7wLIRAQK3S+HDfGLzweVe4+rja7X0UQsC7lBf6z+uDUZuCUaR0kXy7jwokxf7vpQDgZkrFwPO7EBgb/v8khYwaCsTrnDG/Witc9Sxm41BkK0mJ8F0hMCUm5UZvRERERFRIqGYVN47dxMyeczGh5qdYN34Dws6FQzWrDgnbBgDIh4FnCREJOPLLUXzX4Qd8VOdzbPnuTyTFJNnUrzRL3DwW6oBibedfuRh0TjkPQ/svKSUirkdixdurMLbaJ1jx1iqEHr8N1eiYfaYaVdw8HooFA5diQu3PsXvOPhiSM74xN+OCgSsHrlrdb9n6ZSA0OTvvS4pJwuxe8/Hn9zusD5JTBJoPaoq3NwTDo6i71efzZpMZZ7efx1etvsMXzafg4M8hSIx6+Pm2736SUM0qbh6/hRk95mJyi29x/ehNx/3+EhEREREREeUzf/31V5ZjQvr37w9nZ+dcrMq+vL29MWLEiCyvT6xfv94u1wTSrl9k98dWTz31FKpUqWKxhtmzZ8NgMGS5HVVVsWDBAou1BAQEICgoyKYaly9fjldffTVbQXK8HpP7tBoNnqpZFusnvYozC0ZiUOcGcHXS5WhsjpQScUmpmLRsD6r0m4JvftmDuMQU7l+iXKTN6wKIiAj4+uuvsWnTJty+fRsA8NVXX6Fnz56oU6dOtrcRHByMyMjI9MeffvopKleubPdaiYiIiIiIiIiIiIiIiIgKCiklZs2aZXG5EAKDBg16JPDM1dUVnTt3xpIlSzJcJykpCevXr8eQIUOe2GArKSUiIyPRu3fvTIP4AKBo0aIoW7ZshsuaNWuG+vXr49ixY9kaFJg2UDYmJgYhISEICQnBtGnToNfrUadOHQwaNAgvvfQSvLy8rH7vDQYDBgwYgMTExEeed3FxweLFi+Hk5GTV9uwtKioKUVFR0Gr/Gc5lNptx7949bNiwAd9++y2ioqIyXFcIgaeffhovvvhinn0m9S46dBzVFk8PaIq9Px3A7nn7EH0nBpDWDfgVRVLVmAABAABJREFU4uH/+FcuhnZvtkajXg2gd9U/sb9reUnROWbooADgbUjCkHM7ML1WR9xy9/3/jv1vQ4EoJ3fMrd4GI89sRanEaDh6Lxti4xF99gqKNc7+eBwiIiIiooxIKXH7zB2s/nA9Lu2+AlWVlsOUHVwHAESFRuO3CZtw91w4Xpvfz+rtpCSkIOp2jJ2ry5mKTSrAnicJaQF8Gz7fjANLD8OUbILMnWjr9P5jw2Lxy+jVcC/qjoY96mVrPWOqCeGX7lnVlxAClZtXsvlcWkqJB9ci8EP32XhwOcLK83oBrV6DHl+8gJZDm0PRWJ5kwFLfoadv49dRq3Ht8A2rryvkhJQSd06F4du209Dpvfbo/F4HaHS5G9pPRERERERElJuklNi3b1+W597dunUr0N/ZCyHQq1cvfPbZZzCbzRm2kVJi7969kFLa/Fq3bt2KsWPHZjrp4n9duXIFqmpdiH8anU6H0aNHY+jQoRnuw5s3b+LYsWN46qmnMt3O7du3cerUqQyXCSHw2muvQa/X21TjokWLbH59lHsURaByQFHMfbcbPhvUHj+uPYhZvx9GTILtIXBSSkTEJuHDedswfc1+fDqwA3q3rQNnvbZA/3tCVBAwTI6IKAdsmdH73wO/03h6emLOnDl49tlnAQBGoxEDBw7E4cOHM2z/X6tWrcKaNWvSHzdq1AijRo2yujYiIiIiIiIiIiIiouxQFAGlkH2Zr0gJqBJBQUHQaDQIDg5GcHBwXpdFRERZiIiIyHQWZTc3N7Rr1+6R54QQGDhwIJYuXZrhelJKzJ07F4MGDbLpO+P8YvPmzWjQoMEjz6mqioiICGzevBkrVqxARETmNwwLIdCjRw+LM1DrdDosXboUTZs2RWxsrE0DDKWUSE1NxZEjRxASEoKxY8fivffew8iRI7M983VaqOCBAwceqUEIgfHjx6N69epW12Vve/bsQUBAwCMDJs1mM4xGY6azUQshUL9+faxduxY6nS63yrVYi5uPGzq+2w5t32yFO3+H4ejq4zi34yIib0QhNTEVUAEIpA/AllJC0Shw8nBCsUp+qPNsbdTtWgvFA/2haBUOIHUQIQScvDwdt30AxZLjMOz/gXL3XTwtBsqFu3pjbvU2ePPMNvilxDs8UC5s+wEUbVSbny0iIiIisomUEokxSVj/0QYcWHIYqlHNtaCr7CjbsIxNx7qRN6NgTDY6oCLbCACVm1W023G72WTGwaVHsHb8b0iKSs7VELnHCKBU9RLZbh7/IA5JUclWd1OuQRmr1wEefsYv7r6MuX0WICk62eogOVcfVwz9+TVUaVnZ6v2XmmjAhs83YcfMPXn2uyWlhMlgwqZJW3D33F289lM/6Jzz9noLERERERERkaOoqorz589n2sbZ2Rnly5fPpYocp2zZsvDz88O9e5ZD+2/cuAGj0WjzZIQ3b97El19+aWuJVhNCoGfPnnjvvfcQHR392HJVVfH999+jadOmFq/TSCmxdu1amEymDJfrdDoMGDDAput0UspHwvuEEHB1dUWVKlVQvnx5KIqCa9eu4fLly0hOtv76F9mfEALFfTzw2aD2eOflp7Fo8zF8++tehEcn5ChULiwyAUO+XYcvlu3ClBGd8UzjKtBpC+6YO6L8jmFyREQ2atmyJXbt2mW37XXu3Bl9+/bFzz//DAA4fvw4vvnmG3z44YeZrhcZGYk33ngj/bFer8eCBQsK9E0LRERERERERERERET5VUhICDw9HRe8QURE9iOlxLp165CUlJThciEE2rRpAx8fn8eWBQUFoWjRorh//36G6548eRJXr15FYGCgXWvOTcOGDbO4LLsDAN3c3PD+++9nOmi0SpUq2LZtG7p164awsLAc3QgspURkZCQ++OADrFq1Cps2bUKxYsWyXO/q1asYP378Y0FyTZs2xTvvvJMvQqVUVbVqcKwQAs7OzhgxYgQ++eQTuLm5ObA66wghoHPWoVzDsijboAy6qxKGJAMSo5OQGJWE+AfxUM0qNDoNPIt5wM3HDa7eLtC56CCEyBf7ozBwK1vyYcCbg27OFwACEiIx5NwOzKjVATF6V4uBcjc8imJe9dYY8fef8DIkOy5QTkrc3XUYdT4clnEtRERERESZkFLiwq5LWDjkZ8SGxSIv88gsKR9U1qb1rh+9aedKckZoFJSqWTLH25FSIi48DgsHL8WFnZfzRfCf3lWPIqW9s90+7Gw4pGpd3ToXHXzKPH69KyuqWcWOWbuxbtwGmAwZ30BsiRACJaoVx4jVQ+BXzteqc3spJcIv3cO8VxYi7Hx4vthPUkocX3cKqlnF4CUDoNXz9jsiIiIiIiJ68pjNZkRFRWXaxtvbG66urrlUkePo9XqULVs20zC52NhYGAwGm8Pk8oKXlxf69++PadOmZXhN5Y8//kBERASKFi2a4fqqqmLJkiUWr8c0bdoUAQEBOapRCAEXFxeMHTsWw4cPR5EiRdKvHUkpkZCQgJCQkCfic/akEEKgiIcL3n6xGYZ0DcLSLSfw1S+7cetBXI5C5a6GRaH7R8tQv1IJTBvZFY2rlYGicOwGkb0peV0AERH94/vvv4e/v3/6408++QQXLlzIdJ2RI0c+cgPD+PHjUaNGDYfVSERERERERERERERERERUEKiqijlz5mQ6iG3IkCEZ3tzq4uKCHj16WLzxVVVVLFy4MF/c3GoLKWWmP9mhKAq++uqrLAeNCiHQsGFDnDx5Ev3794eTk1OOw8KklDh27Bjat2+PhISETNsaDAYMGDAAiYmJjzzv5uaGRYsWQa/X56iWvKDRaNCvXz9cvHgR33zzTb4KkvsvIQQUjQJnD2f4lvFBmbqlUaN9NdTqVAPV21ZF6VqlUKSUN5zcnKAoCoPkcpFbQHGH9yEABMaGY+D5XXAzpVoOrhMCF7xLYlHVlkjS6h2ayRF/JRTmVIMDeyAiIiKiJ5HJaMJvn27C9OdnIfZO/gyS0zhpUKxCxjeFZkZKicv7ruSraxwuRVzg6e+Ro21IKXF5/xV83vRrXNiRP4LkAKBoeV/oXbN3LUJKiSv7r1pdu09AETi7W3fTsyHZgJ/f/BWr319vU5Bcrc418N6Ot1G0vJ/VQXKnt/yNL1tMyTdBcmmklDj5+xms/nCd1YF+RERERERERAWByWSyOEFjGi8vL2g0mlyqyHGEEFlOImwymWAwFKzvkoUQePPNN6HT6TJcnpSUhF9++cXiNZdbt27h7NmzFrc9cuRIKErOYomcnZ2xadMmjB07Fr6+vuljQ4QQUBQFnp6eaNu2Ldzd3XPUD9mfEALuLk4Y/kJjnF8yGos/7ImKJYvkaGyPqkocu3QXLUbOQ8+JP+PynYh8dU2Q6EnAMDkionzE19cXP/74Y/rj1NRUDBo0CKqqZth+w4YNWL58efrjOnXq4IMPPnB4nURERERERERERERERERE+d21a9dw6tQpi8u9vLzQokWLDJcJIfDaa69ZHPwmpcTPP/+M1NRUu9Ra0CiKgvfffx/Dhg3L1gBBIQT8/PywYMECXLhwAR988AHKly8PjUZj8wBDKSXOnDmDzz77zOKgQiklZsyYgQMHDjzSRgiBSZMmoVKlSjb1nddUVcWxY8dw9OhRi+MJiLLiWrwoFH3GA8rtSQCoExmKfhf3wdlszDRQ7oRfOSyr3AypitZh2RzG+CSkRsU6aOtERERE9CRKSUjB7N4/YctXf0I15t9zMJ+AInDxdLF6PWmWCD1+ywEV2a5EYHHonGw/X5FS4sCSQ5jedRbi78dD5qP0v/KNykEo2bwWIoGrB69b3Ue5BmUgNNnrQ0qJuPtx+P7ZGdi/6KDVoWmKVkHHd9th+IpBcPGy7vMnpcS+hQcxp9cCpMSl5subRqWU2D1nP85uP58v6yMiIiIiIiLKiexMOOjq6vrETAqXVSie2WyG0WjMpWrsp1y5cmjdunWGy9LGzWT0uqSUWLt2LUymjCcW8PX1Rfv27XNUmxACn332GVq2bPnEfI4KIyEEXJx06NOuLs4sfBs/j3sJlUr62j7mCxJmVcX6fedRd9APeOuHDYiMTeT1NyI7YZgcEVE+07NnT3Tv3j398YEDB/DDDz881i42NhbDhw9Pf6zVarFgwQKLydFERERERERERERERPaiaBRoCtmPonn41WpQUBCqV6+OGTNm5PFeICKizEgpsXDhQotBW0IIdOzYER4eHha3UatWLZQsWdLi8jt37iAkJCTHtRYkaaFwS5cuxeeff2717NNCCJQrVw6TJk3ChQsXcO7cOfz000/o3bs3KlSoAL1enz77cHZIKTFv3jzEx8dnuPzKlSuYMGHCY0FyzZs3x+uvv15gB6pKKXHu3Dn07NkTr776KlJSUvK6JCqAdB5ucPH3zZW+BIAm967gpauHoFPNFttJIbC/RCDWVGwEk3DM0Eapqki6+8Ah2yYiIiKiJ09STDKmdZmJv/84a58b2f5/zqvRa+BaxBVFSnuheNXiKF61OHzK+sDN1w16Vx2EIiAAq85by9Yvk34d2xopCSmIvBVt9XqOIoRAxafKPTyRsIFUJbb/uBM/v/ErjCkmi3nW2a8HEIqAk4cTPIt7oHiVYo/sL52T9v/7KxsFC4HKzStme7+aUk24eync6porN8teH1JK3D5zB5Oaf4trh27Amsw9IQT0bnoMmNcXL3zcBRqdddeIpJTY89N+LH9rJcwGy+eJWRfysBatkwYeRd3hH1gMxav6w6uEF/SuD68zIYfXf1SzimVv/IrUREOOtkNERERERESU3yiKAkXJ/HpSbGzsEzPJncGQ+bm9TqeDs7NzLlVjP4qi4MMPP7S4Ly9fvozjx48/9rzZbMaSJUsyvO4phMBLL70Ed3f3HNXm5+eHoUOHFtjxOfQoIQSc9Tr0alMbpxeOxJKxL6JCiSI52r/JqUbMWH8YVftNxazfDiHFUPACHYnyG21eF0BERI+bMWMGdu7ciejoh1/Mjxs3Ds899xzKly+f3mb06NEICwtLf/zuu++ifv36uV7rf6mqioMHD+L8+fN48OABfH19UaZMGbRs2RIuLtbPdmcPCQkJ2L9/P27fvo2IiAhIKeHj44PAwEDUr18fnp6eeVJXTphMJuzduxfXr1/H/fv34ePjg0qVKqFFixbQavnnnYiIiIiIiIiIiMhRQkJCCuR1ZSKiwiY1NRXLli3L9Ebv1157DQAstnFyckK3bt3w448/Wmwzc+ZMNG/e/Ike9JgW7la6dGkMGTIEr7/+Onx8fHL0moUQ0Ov1CAwMRGBgIAYMGACTyYSoqCicOnUKf/zxB3777TeEhoZmOSg5JiYGJ06cQMuWLR95XkqJZcuWISkp6ZHnXV1dMW/ePGi12kw/H5ZmXk6TVtd/t2Hr++Lu7o7atWtDURRIKfHgwQOEhoYiNTU1wzrTZghfsWIFnJ2dMXfuXKvD/ahwE1oNfOtWQ2Lo3VzpT4FE6zvnkKB1xm/lG8CsZPx5lULB9tI14W5MRZebx6Gx88zTEkDK/Ui7bpOIiIiInkzJccmY1nUGbh67laNAMiEEnNydUKlpedR+thbKBZVFkdLecHZ3gkargVAenkdKKaGaVJhSTUiISsT9Kw9w5cBVnN9+EXfO3YUx2Wj5PFYAgc0r2XROGnkzCqaUzM+BM3pNjiIBVH7KttcipcSuOXuxduzvUE223+QshICrjwtqdqiOei/UQUDt0nD3c4NWr00P7EvbX4YkA2LD4xB64hZObTyDS3svIzEq2cJNuEBAndLZriPuQTySopOtrr1sg7JZtpNS4sT6U1g49GcYEzP5bGXcCbxKeGLE6qEoU7e01ftKSolDy0Lwy+jVNu8nIQQ8S3ii0csN0aBbHRSt4Acnd2dotA/3j2pWYUgyIuJ6BA7/ehSHfj6MhMikLLZqWfTtGOz76QDajmz1RF8HJCIiIiIiosJFq9XC3d09Pc8gIwkJCTCbzQX+3nVVVXHv3r1M2+j1euh0Opv78PDwQLVq1bIM6Pu3qKgoXL58OccTWTRt2hSVK1fGxYsXH1smpcS0adPQuHHjR65r3Lp1C+fOnctwe4qiYNiwYTm+DtKhQ4ccB9JR/pMWKvdK2zro/nQN/LrjFD5a8CfuRCbY9FmWUiIyPglvTt+AmesP44e3nkPLOuWs+l0ion8U7L/YRERPqOLFi2Pq1KkYMGAAACAxMRFDhgzB9u3bAQB//vknFixYkN6+atWq+Pjjj/Og0n+oqooZM2Zg8uTJCA9/fAY0T09P9O/fH1988QXc3NwwYMAALF68GABQtmxZ3Lhxw+41bd++HV9++SV2795tcaC/VqvFU089hQEDBqB///75/qDSZDJh8uTJmDFjBu7fv//Ycl9fX7zzzjsYM2ZMgT8xJyIiIiIiIiIiIiIiIrLVkSNHcOvWrUzbLF68GHv37s20zdGjRy0uk1Ji06ZNiImJQZEiRWyqM68IIVCqVCmLAalarRbFixdHuXLl0LBhQzRr1gwVK1aEXq93yA2zQgjodDr4+/ujQ4cOaN++Pb766ivs2rULw4YNQ2hoqMXBhlJKHD58+LEwOeDhLMoZhb199NFHWc4mHRISkuny1atXIzIy8pHvmIcOHYpmzZplup4lrVu3xrp169LD5FRVRWRkJGbNmoUvv/wSqampGa4npcSiRYvQpk0bvPLKK7yhmbJNCIFSnVsgdMNO5CgZwwoaKfHszZNI1DlhW0AtSJHxGA2zosHv5erD1ZSKtrfPQoH96hMAku5kfpMAEREREZEhxYgfe8z5f5CcbcejQggUr+6PjqPboU7nmnDxcsnynE2j1UDnrIOLlwuKlvdD9XZV0XVCZyRFJ+PSnsvY89M+XN53FeZUFfJfx8kCAuUalrGpzhvHQq1ex83PDSNWDoGrl2MmGver4Gv1OlJKHPz5MFa+tzZHAWX+lYuhy/hOqP1MTejdMr8Okra/3HzcULJ6CTR+JQiGJAMu7bmCP77ZihtHQiFVmf4ZcnJ3gk/p7F9DCjt7F1K17vOnc9HBt2zmfZhNZmyavAWbv/0TqtG69+phWF0ARqwcAs/injYFyV05cA3LRv5qdd9p/XuV9ES3z55D/efrQOeiy7AGRaNAq9eiTL0ABNQtjWfe64A149bj4JIjVr+naXVvm7YDLYY0g95Vb/X6RERERERERPmRRqOBv79/puNroqOjERcXh6JFi+ZiZfaXkJCA0NDMr4P5+fnBycnJ5j569uyJ+fPnW7XOtWvXULVqVZjNZpv7BQCdTod33nkHw4YNe+x6ppQSGzduRFRUFHx9fdOfW7NmjcX8herVq6N69eo5qkkIgTZt2nAcyxNMCAEXJx36d2qAnq1qYeHmY/hi2S7ci060MVQOOH/zATq8uwDPNK6M79/ogvIlcjbhKVFhxJQZIqJ8qn///vjll1+wZcsWAMBff/2FefPmoXfv3hgyZEh6O0VR8NNPP+Xo5CSnkpKS8Pzzz6eH3WUkLi4OP/zwA/7666/01+Qo8fHx6NevH3777bcs25pMJuzZswd79uxBt27d4O3t7dDaciIuLg5dunTJ9IaWyMhIjB07Fr/99hu2bt0KLy+vXKyQiIiIiIiIiIiIiIiIKO9JKTFz5sws26xYsSLHfSUkJOCPP/5Anz59cryt3LZt2zYEBgZaXJ4WkpYXA/KEEHByckKHDh2wc+dO1KtXD7GxsRbb3717F1LKbNWakJCAlStX5rjGO3fu4Oeff37kubZt29ocJqfT6aAoCoQQEEJAURT4+/tj4sSJqFatGvr27WtxEK+qqhg9ejQ6deqUPvCXKDuKN28IodVAGjP+bDmCTprR4+oRJGqdsL9EFUgLv7dGjRarKjaGmzEVTe9dht3+JZIS5hRDtv/NICIiIqLCRzWrWP7Wr7i6/7ptQXIC8CrhhRe/fgH1nqsLjVax+dgz7RzR3dcN9bvVRb3n6yD6Tgx2z92L3fP3Izk2BZASWmctfMvaFsB2ed8Vq19nQJ2SqNC4XL45ppZS4urha1j+1iobg+QEXIs4o8ekF9DklSBo9BqbXpsQAk5uTqj1TA3U6FANN4+H4pd31uDm0Ych+X7lfLIdRPYwdO2q1TX4BBSBs7vlAP3k+BQsHroMJ387bfV+F4pA414N0eeHl20OVIu7F495fRfAmGy0el2hCDR+JQi9vu0BZ0/nbO8jIQTcfdzQb0ZvlAgsjrXjf7cpUC4uPA5XD11HtTZVrF6XiIiIiIiIKD8SQqB27dqZTrRoNBpx5swZtGnTJhcrs79Tp04hMTEx0zbVq1eHRqOxuQ+dTpd+PS+7spoIMbuEEHjppZfw/vvvIzo6+rHliYmJWLt2LQYPHgwhBMxmM5YsWZLh9SEhBF5//fUcvRdpAgICcrwNyv+EEHB3ccIb3Zri1Y71MX31AXy3ah/iklKtvgYpIWFWJTYdvISdJ6bjnZeb492XW8DN2TEToBI9iTKe2pOIiPKFOXPmwMPDI/3xmDFjMHjwYNy8eTP9uTfffBNPPfVUXpQH4OEX1d26dXssSK5mzZoIDg7GhAkTMHz48PSbEM6dO4eXX37Z4iD3nIqOjkbTpk0fC5IrXbo0+vbti/feew/jx4/H8OHD8dRTT+VpCJ+1Bg8enB4k5+/vj/79+2P8+PF488030aBBg0faHj58GB07doTBYMiLUomIiIiIiIiIiOgJp2iUQvkDAEFBQahevTpmzJiRx3uBiIgsiYmJwebNm2274dtKUkrMnj0bqmrLjcp5y8PDAxqNxuKPtQNcHUEIgXLlyqFjx46ZtstqwG9Bljbg94033sh0fzx48ABfffVVrnzu6clgVlXsu3IPsXr7DE63hpNqQp/L+1Ev4sbDqaUtSNHo8HNgc5zyLQN7frKlueD9m01EREREuUNKiYM/H8Ghn0NsOr8SikCjlxrgo6MfoGH3+tDqbAsly2z7PgFF8MKnXfH5mQno9G476F318CnlDWcP64/tpVni5rFQ62oQAhWalLe6L0dKiEzE3D6LYLIloEwIVGhcFhMOv49mA5pA66S1yz5TNArKB5XDe3+9jb4zXoaLlzPKB5WDULK5bQlcPWh9oGG5BmUgNI/3IaVE5M0ofNvue5z47ZTV29Xqtej++XPoP7ePzUFyZqMZCwcvRdy9BKvXVbQKenzxPPrPfgUuXi427SNFUdDurdZ4euBTNq3/8N+Hw7z2QkRERERERE8MIQRatmyZ6XmylBLLly8v0OfDUkpMmzYt09cghED79u1zsSr78/T0xKuvvprh/pRSYvr06en5DqGhobhw4UKG23F1dUXPnj3tco2sIOU4UM4JIeDl5ozxr7bGxaWjEfx8Y7jodRA2TB8oIZGYYsRnS3ah9sDp+OPwRZgL4Pg8orygzesCiIjIsjJlyuCrr77CiBEjAACxsbH49ddf05dXqFABkyZNyqvyAAA//vgjtm3blv7Y19cXixYtQpcuXR5ru2LFCgwdOhQHDx7E8ePH7V6Lqqro06cPzp49m/5cmTJlMHXqVHTv3j3DdeLi4rB+/Xp89913dq/Hng4dOoTU1FQIITBx4kSMHTsWOp3ukTY7d+5E3759ERYWBuBhoNykSZPwySef5EXJRERERERERERERE+kkJAQeHp65nUZRESUiS1btiA+Pj7X+jty5Ahu3bqFsmXL5lqfBYGUEklJSXB1dc3xANOsZir+9yRtTyIhBD7++GP8+uuvuHv3boZt0oINR48ejeLFi+dyhVSQSClxNSwKb077HduPXcYgZ080S0ywYeiu7QQAV5MBAy7sRnINPc4XKQlk9O+EEEjQOWFBtVYIPrMNgbHhdqlTaDgHLxERERFlLOJGJFaOWQupWn9zrKJV0H3Sc2gb3Cp9chJHEULA3c8dL3zSBS0GN8OZzWehZBAglpWUhBRE3oq2er3KTSvleQB9GtWsYsmI5Yi7G2d9CLUA6j1fB68t6Au9i20BaVnR6DRoNqApAltWRvSt6Gy/b8ZUE8IuhlvdX+Xmj+8bKSWu7L+K2a/MR2JEEqx5o4QQcPF2xsAFr6Jmx+o273cpJQ4uP4wLOy5ZffO5olHw8jfd0XLY0zn+3CmKgm6fd8Xx308h4b71oXaX9l6G2WiGVs/b8YiIiIiIiOjJ0KpVK2i1WhiNlkP6V69ejS+//BJ+fn65WJn9HDt2DBs2bMi0jaIo6NSpU7655mULIQRGjhyJWbNmwWAwPLb83LlzOHPmDOrVq4c1a9akB8v9dxsdOnQosPua8gchBIoVcce0kV0x6qXmGDNrMzYcOA+TKq2+NiilxI3waDw/bik6BlXGDyO7onwJnwL9u0rkaBwVRURko927d0Or1Vr9c+rUKav6GT58OFq0aJHhsnnz5sHNzc0eL8cmycnJmDhxYvpjvV6PrVu3ZhgkBwC9e/fG+vXroSgKUlNT7V7P8uXLsXnz5vTHgYGBOHjwoMUgOeCflO2TJ0/Cy8vL7jXZS9r79cUXX2DixImPBckBQOvWrbF9+/ZHbpT48ssvER5u/UACIiIiIiIiIiIiIiIiooJIVVXMmDEjV2dENhqNWLZsWYGehdlRgoOD8d577+H27ds2vz9SSpw4cSLTNiVLlnziBwl6enpi/Pjxmb7OhIQETJkyhZ9FypCUErGJKfhgzmbUGTQdW49egdkscdDLH+Y8+P0RALwMyRh8fifKxUcAlj63QiBG74p51dvglruv9eEQGWxP46x/4v/NICIiIiLrmU1mLBm2HKnx1o9xVrQK+s3uhXZvtnZ4kNy/CSHgW8YHLYc2t+kYN/JmFEwpj980mmmfGoHiVfyt7ssRpJQ4tekMzmw6a/W5sBAC9bvVwcBFrzosSO7ffRWrUBSBLSpne534B/FIjk62up+y9cs88pyqqti38CCmdZmJxIhki6delrbnV8EPY3a8naMgOQBIiEjE2rG/27SfOr7bDi2H5jxILo2LpwvavdnKpu3F3otHQmSiXeogIiIiIiIiyg9KlSqFOnXqZNomLi4OEydOhKqquVSV/URERKB3794ZBqf9W926dVG+fPlcqspxypUrhzZt2mR43UNKienTp8NgMGDp0qUWr9O89dZb/D6d7EIIgfIlfLDy41dwYObraFojAIpi22dLVSU2H7mMuoN/wFcrdiM51XIAJlFhxzA5IqIcMJvNVv/Y8gXovwPb0gwePBht2rSx10uxycqVKxEd/c9sdG+//TYaNGiQ6Tpt27bFa6+9ZvdapJT48ssv0x9rtVr88ssvKFmyZLa3kd9PbOrXr48xY8Zk2qZatWqPfF4MBgN++umnTNdJTU1FXFzcIz9EREREREREREREREREBVFoaChCQkJytU8pJRYsWJDpLM2FVVJSEqZMmYLAwEC8+OKL2LdvH1JSUrL9vbmUEtu2bcPevXstthFCoH79+vYqOd8SQqBPnz4oVqyYxTZSSsydOxcPHjzIxcqoIDCZVazZ8zdqvfY9vl25/+GgWikBIXDZzRu3nd1zHtJmAwHALyUeQ8/9heJJMZkGyt138cSc6m1w38UzZ7VKwLWU5d8jIiIiIiqcpJQ4se4ULu+/CmnlEadQBLpPeh5NX2mcZ2ORbe33xvFQq1+vu68b3P3cberP3lITU/HL6NWQqvXj8ys/XREDf3oVOietg6rLuN/sunP2rtU3aOtcdPAt65P+2JRqwq+j12LZm7/CmGqy6j4GIQSqtKqMD/eORsmqJXL02ZZSYsNnfyDJynA8AKjUvCK6jO0EYeNNphkRQqBJryAoOutvqZMmicgbkXarhYiIiIiIiCivKYqCUaNGZXruL6XEvHnz8Pvv1gfF/3c7kZGRuTJBnpQSN2/eRLt27XD16tVM+1QUBRMmTIBWm3vXiRxFURR88MEHFsPk1q9fj+XLl+P8+fMZrh8QEIDGjRs7ukwqZBRFoEFgKeyaOgSrP34FFUoWse16o5RISDZg3Pw/0XDoD9h/5jpUTrhJ9JiC/9eMiKgQmDx58mPP3bt3Lw8qedSmTZvS/1sIgddffz1b6wUHB2cZcGat06dP4+zZs+mPX3jhBdSrV8+ufeS1N954A4qS9ZfWQ4cOxYQJE5Cc/PAL97Vr12LcuHEW23/xxRf45JNP7FYnERERERERERERPfmEIvL9BB32Jv4/3iAoKAgajQbBwcEIDg7O26KIiOgRUkqsWLEi01C3okWL4sMPP4SLi4tV2759+za+/vpri9u+fv06zpw5k+XkW4WRlBLJyclYs2YN1q5di1KlSuHZZ59F165dUbt2bfj5+UGv1z9ybGE0GnH37l3MmTMH33//fab71NXV1eIs2V27dkWpUqVsqnvNmjXYvn27xeW1a9fG8OHDH6m7SZMmNvWVXZ6ennj33Xfx3nvvWRzoHB8fj++++w5ffPFFoTteo8dJKXHx1gMET/0Nu0/fgJpBwEKKosEen5Loe+ci8uITIwCUSozG0HM78GOtDohycgcy+uwKgVvuvphXvQ2Cz2yDtyHJtnoF4FqCYXJERERE9ChjshFrJ/xmdSgZBNCoV0O0DW5Z4M7BpJS4vPcKrE1rLlm9BLR6jWOKsoKUEjtm7EZsmPWTaHv6u2PIkgHQOescUFnOSSlx5cBVq/eNT0AROLs7AQDiIxMwv+9CXNx9xeobtIUi0HpEC/T4/Hlo7RC2F3kzCgd+Pmx1Hc4eznhtXh9o9fa/9c2ruBeKVvBD+AXr7smQUiLqTgwq2r0iIiIi+q+Mjh3y4pg7v9RRWEkpsX//fly+fNlim86dO8Pf3z8XqyIierIIIdCtWzdUqlQp039vjUYj+vTpg+XLl+O5556z6u+hlBIJCQn4/vvvcfz4caxdu9YepVvsKzY2FkuWLMHHH3+MmJiYTK9JCCHw1FNPoXPnzg6rKbc1bdoUlStXxsWLFx9bFhcXh9dffx1ms/mxZUIIDBw4EE5OTrlRJhVCWq0Gzzevjg5BlTHrt8OYvGwnouNTYe2FUCklzodGoPWonzCgU318OawTiri78Did6P8YJkdEZKOWLVti165dDu9n/vz5+Ouvvx57fsOGDfj111/x8ssvO7wGSw4fPpz+35UrV0a5cuWytV69evXg5+eHiIgIu9Xy333Ru3dvu207v+jatWu22nl4eKBVq1bYvHkzgIdBe8nJyRZviPnwww8xevTo9MdxcXEICAjIecFERERERERERERET6CQkBB4enrmdRlERJQBo9GIBQsWWBwEKoTAkCFD8Pbbb1s9eMxsNmPHjh04ePBghsullJg1axbmzZvHgWmZkFLi9u3bmDNnDubOnQuNRgNPT08UL14cbm5u0Gg0MBqNCAsLQ0REBEwmU5aDejt27IgiRYpkuKxRo0Zo1KiRTbW6uLhkGibXtm3bx8LkHC3tM/z111/jwYMHGbaRUmL27NkYPXo0ihXLv4FZUkr+rjiQ/P9szJOW7sAPaw8h2WgCLP0uCYEQb390eBAKf0NyngXKVYy7j0Hnd2F2jXaI1zlbDJS75FUcC6q1wrCzf8HNlGp1vUKrwLUkbyojIiIion9IKRGy+jiiQmOsXrdIKW/0+q4nFE3Wk0XnN9IscfN4qNXrVXqqggOqsV5STDL+nLbDpqC0vjN7w6OYh4MqswMJXD1w3erVytYvA6ERuHshHDN6zMWDa9aN1RdCQOusxcvfdkezAU2zNQl6VqSU2PLNNpiSLU8UYKmWju+0hU8ZnxzXkOH2NQIB9UpbHSYHAFGhUQ6oiIiIKPdIKZGYmJit4yhXV1doNI4PEpZSQlVVxMbG4vr167h48SJu3LiB0NBQGAwGCCHg5uaGcuXKoXLlyggMDETp0qXh6upq1+8apJQwGo0IDw/HpUuXcP78edy8eRMxMTEwm83QarUoUaIEypYti6pVq6JixYrw8/ODRqPJcR1msxlJSUlZtnNxcYFWm/1oAFVVkZiYaHF52nv77/qzWseRFEV5bL8uWrQIP/30U4bthRDYs2cPw+SIiHLI2dkZM2bMwDPPPJNhyFiapKQkvPjiixgyZAgmTpyIokWLWvwbKKWElBL379/HkiVLMH36dISFhaF9+/Y5qjXteCHtnnkpJUwmE6KionDmzBls2rQJf/75Z5YhcsDDvyOenp5YuHAhdLr8OemALXQ6HUaPHo3hw4c/9h5IKZGammpxvVdffZVjOcihhBBwddZj9EvN0bd9PYybvxVL/zwBo0nC2lA5k1nFT38cw6aDFzFj1HPo2rQaNAXwWj2RvTFMjogoH7tz5w7efffd9Mfu7u5ITU1Nn3V95MiRaN++PXx8HPMlaWZMJhNCQ/8ZQFCrVi2r1q9VqxZ27txpt3r+/vvvRx47esb33FayZEn4+fllu33dunXTw+RMJhMuX76M2rVrZ9jWycmJKeFERERERERERERERERU4J06dQrXrl2zuFxRFPTr18+mQY+KomDgwIE4dOhQhoNNpZRYt24dpk6dCg+PfHwzcj7y7wG9UVG23YTr7OyMSZMmOWQga34dHOvp6YnRo0dj7NixFgc+x8XF4fvvv3fYe5NTUkrcOb8X3n7l4F6sTF6X88RRVRXbjl7BiKm/4ea9rAfIA0CsVo8dfgHoFXYpT8LkgIeBcjWjbqP/xT34qVorJGv0FgPlTvmWwdIqzTHgwh44m41W1awv4g29N/+dJiIiIqJ/mFJN+OPLrTaFkvX6ridcPJ0dVJljpSSkIOpWjNXrVWxaIc/PNaWUOLTsCJKik61aTwiB6u2qomaH6nn+GjJjSjXh7qVwq9YRAihZvThObTqDxUOWITk2xep+3Yu6Y/iKQajYtLzd3p/EqESErD5h5W2ggLufO1q/3sKh+6lEleI2rWdMNTEkn4iICrSjR4+ibdu22Tr+nTlzJvr16+ewWqSUCAsLw4oVK/Drr7/i7NmzSElJSV/2X2l/f4UQ8Pb2RuPGjfHaa6+hQ4cO8PT0tPnvs9lsxunTpzFv3jxs3LgRYWFhUFU1yzp0Oh0qVaqEV155Bf369UNAQIDNNZw4cQKtW7fOst2wYcPwzTffZLufyMhIBAYGwmQyZbjc09MTly9fhqura/pz165dQ7169bJXuJ2VLl0ap0+ffqICfYiICgIhBNq2bYsRI0bgxx9/zPQ4wWg0YtasWViyZAnat2+PF154ATVr1kwPJzUYDLhz5w4OHz6MzZs34+TJk0hNTbX62pslV65cQZUqVdL/FqaF1qVt35p+9Ho9VqxYgYoVK9qltvxCCIGXXnoJH374oVXjcZo0aYKyZcs6sDKifwgh4O/jjnljumPEC03x5rTfcej8Lav/rZBS4m5UPHpOXI5nG1fBrNEvoISvB6/dUaHGMDkionxs+PDhiI2NTX/89ddfIywsDJ9//jkA4P79+xg1ahQWL16c67XFxMQ88tjaQDtfX187VvPwwmYaIQSKF7fty938ytrZMf7bPjo62p7lEBEREREREREREREREeUrUkrMmTMn0wFllStXtnkAqBACXbp0gZOTU/pNLP8VHR2NHTt24Pnnn7epD7KOoij46quvUKVKlbwuJVcJITB8+HB8++23j3xP/m9SSsyaNQujR4+2atKy3CClxM0TW7Fpej94uBdFz892wLXIk/X9fl6RUiIsIg4jp2/A7wcuwPz/G92yRQjs8ymBp6PCUDolIU8D5Rrev4ZErROWBTaDQWPhZjEhcMi/EtyMqeh15SD0qjnbffjWDoSi501oRERERPSPS/uuIvKGdSHnQgiUa1gGtZ6pUWBvSou8GQVjstGqdRSNguJV/O128++/WfM+mgwm/PXjLqv7ULQCL37dDYpGsXrd3BT3IB7JVgblAcDO2XsRdy8OqsmK80E8fO9L1SqJ11cNhm+Aj90+01JKhKw+jpS4VOtWFEDr4BZw9nBcUKMQAj4B3ratbP+PPxERUa5J+/4gISEhW8d0P/74I1555RVoNBq71xEaGorx48djzZo1SElJyVY9/w6JiYqKwubNm7FlyxZ4e3vjtddew8SJE+Hp6WlVHUePHsXbb7+Nw4cPQ1VVq+owGAw4d+4cJkyYgM8++ww9evTAl19+idKlS1t9TGU2m5GYmJhl/7Nnz8bbb7+N0qVLZ2u7UkokJibCaMz42D+jfauqarZqcYTExMRc75OIiB5SFAXffPMNQkND8fvvv2f6d0BKiYSEBKxbtw7r168HgEfC3f79//YmpYTZnP3vhy1xdXXFvHnz0KlTpwJ7fS8zXl5eePXVVzFt2rRs7QshBN544w0oSv6+bkZPHiEE6geWxK5pQ7By52mMmb0F96Kzd77yb6oqseHQBewfOA3fBT+LV9rVgdbO5zFEBQXD5IiI8qlly5Zh48aN6Y9btGiB4cOHw2AwYM2aNTh//jwAYMmSJejTpw86dOiQq/Wlpj76pa5er7dqfScnJ3uWg/j4+PT/dnV1feJOVtzc3HLUPiEhwZ7lEBERERERERERUSGnaBQoT+AAmswo/x+YEBQUBI1Gg+DgYAQHB+dxVURElCYhIQHr16+3OJBMCIH+/ftDq7V9qEyxYsXQqFEj7NmzJ8PlUkrMmDEDXbt2feK+r7SVTqeDEMKug4SFENDpdJg8eTKCg4OfyEG9WfHy8sLbb7+Njz76yOJ7Gxsbi2nTpuHTTz/NN++RlBJX9v2KrXNHwGxKRbQhFKs/fxY9P9oMV69ieV1egWY0mbFo81F8MG8rYhJsm9U9UavHltKVMOjG3xAmkwOqzB4FQIuwC0jUOWNNhSCYlYwH90qhYEepGnAzpeL568egldkISxACpTu3zDe/E/mNyWSC+p8QQo1GY5cbRc1ms1U3dQghoNFo0veVvfdZTurh54eIiOjJIlWJv37YadO6z0/sAo224N6MduPYTdiSivX7J5vgWTz7AR3Z4VvGBy0GN8v2sdaNo6GICrVuom0hBKq2roLila2b4DsvhJ29C6lat2+kBGLuxFjdlxAC9Z6vjf7z+sLZ3b7j+1Wzil1z9sLaz5nORYfm/Zs4/Nhb72Td/Q9pFC2v+xERUcEVHx+f6fdp/3X8+HHcvHkTFSpUsFsNJpMJM2bMwPjx4+0SWCalRHR0NKZOnYqWLVviueeey9Z6ycnJ+PDDDzFz5kyYTKYc1SGlRGpqKlasWIFNmzbhu+++w4ABAxzyfWFSUhImTpyI+fPn81olERHZnZOTE1asWIG+ffti3bp1Voe9FgRCCPj7++OXX35BixYtnti/p0IIvPnmm5g5cyYMBkOW7X18fNCxY8dcqIwoYzqtBq+0q4vOTapi4sLtmLvxCAwm9eGFz+ySQFR8MgZ+vQZLth7DT+/3RJli3k/s7zmRJQyTIyLKh+7fv4+33nor/bGLi0v6BT4nJyfMnz8fTz/9dPoAzmHDhuHvv/+2OnAsJ7y8vB55/O8wt+yIi4uzZzmPzBqSlJQEVVWfqBs0rJ1V47/t3d3d7VkOERERERERERERUaEVEhJi1UzWRESUO3bs2IGoqCiLy7VaLXr16pWjwWGKouD111/H3r17LQ6C3bt3L8LDw1GyZEmb+3lSCCEwZ84c9O3bF4sWLUrfR1JKmwYRCyGgKAoaN26MqVOnIigoqNAO9hNCYMSIEZg6darFz31auOFbb70FPz+/XK4w43ou7F2B7XODoZoNaU8i+u5FrP7sGfScwEA5W0gpcTH0AYZOWYf9f4faPEBfoyjoFFQJU0e8jdjFa3B+6kLrBuTamQYSnUJPIVGrx+aydaGKjMd/qIqCTWXrwc2Yiva3zkCTRUCB0Cgo2bqJI0ou8EwmE9q3b49z58498vzgwYPx+eef5/jf23Xr1mUrjDsttE2r1cLX1xf+/v6oWbMmGjZsiHr16qFs2bLQ6/U5rmfDhg0YNmxYttoqigK9Xg8/Pz9Uq1YNHTt2RNu2bVGiRIlC+3eIiIjoSZIQmYhLe69YfSztU84HlZpVdFBVjielxKV9V60+7FfNKg7+fMTu9bQe0SLbbaWU2D13n039dHq3PYSSv4/hpJS4cuCqLTl/VlO0CrqM7YROY9o7JBgx4nok7l96YPV6VVpUhqe/47+HMRmzHzD9b86eTjwXICKiAuuvv/5CTExMttubzWYsXboUH330kV3+/iUkJGDAgAFYu3at3QNnrNlebGwsunTpgv3799u1DiklYmNjMWTIEJw/fx5ffPFFjia6stTHsmXL8N5776FKlSp23TYRERHwMNPgl19+wVdffYVJkyYhNdW2yczym7SJC1955RV8/fXX8PPze+LP78uVK4c2bdpg69atme5DIQR69OgBDw+PXKyO6HFCCBTxcMG0N7tgcJcgDP92HQ5fuG31v0GqKrHj5HXUHTQdXw3vhIGdGkJbgCeGIbIWw+SIiPKhN998E5GRkemPP/nkE1SuXDn98VNPPYURI0bgxx9/BADcuHED48aNw/fff59rNbq7u8PZ2RkpKSkAgJs3b1q1vrXts+Lr65v+31JK3L17F6VKlbJrH3np/v37VrW/d+/eI4+LFCliz3KIiIiIiIiIiIiIiIiI8pXDhw+jW7duFpeXKlUKAQEBOe6nU6dO6NmzJ8xmyzebnjt3LtMwOY1Gg86dOyMpKclim8DAwBwNWi1atGim7wfwcACwo7m7u+OZZ55Bp06dkJycjGvXrmH37t3YvXs3Tp06hfDwcCQmJqZPpPZfiqLAxcUF5cuXR6dOndCnTx/UqFHD7jfeZKRMmTLo3r27xeW1atWyanutW7dGuXLlLC5v3LixVdsrUqQIvvrqK2zevDnTdocPH0bnzp3zdBC0lBLn9yzDX/Pe/CdI7p+FiA5LC5TbAlevonlTZAGUYjDhu5V7MfnnXUgymGwKfxNCILC0L3546zm0rlcBGkWB6d3XcHfzbkSfv4q8HDqvlSpeuH4MiTpn7C5ZDdLCZ9ikaLCmYiO4mlLR/O5FZDbtoHfVCnAtXdwxBRdwV65cwd69ex/7+/bTTz9h7NixOZ7gMiUlxeqxL6GhoQCALVu2QAgBIQRKlCiB5557DoMHD0adOnWg0dg24NvWeo4fP47ly5fDyckJXbt2xddff42yZcs+8TeaEBERPcnO77wIU4rJqnUEBJq92gRafcG9+UyaJUKPh+Z1GQ8JgcrNKmb7mCo1MRVnt5+z+gZC71KeKB9U1pYKc5cErh68DunANDkhBPRuegyY2wf1XqjjkONZKSVObTwDac74mk9mtTXr3yRXjrGjw6KtXkcIAd8AHwdUQ0RE5HiqqmLGjBlWrSOlxOLFi/HBBx/AyckpR/0nJCSgS5cu2LNnT54G0iQkJOCZZ57BoUOHHFaHqqqYMmUKpJT4+uuvoSiZXbm2nsFgwLhx47By5Uq7b5uIiAgAdDodxo0bh+eeew5vvPEGDhw4AFVVc/S3UwgBLy8vPPvss3asNOs+AcDHxwfdu3fHO++8k+PxOAWJoih4//33sW3btizD5IKDgwvN+0L5nxACtSsUx67pQ7F063F8MHcrouKSrLtiKoHYxFS8/t3vWLXjbyz8oCdKFfXk55wKBYbJERHlM+vXr8fKlSvTHzds2BCjR49+rN0XX3yB33//PX3g5A8//IBevXqhSZPcm0G5bt26OHToEADg5MmTMJlM2Rq0n5CQ8NhMxjn134H6hw8fznRgf0Fz584dREREZHvG+FOnTqX/t1arfSSMkIiIiIiIiIiIiIiIiOhJM2nSpCzb2GMwmJeXF3799dccbUOn02HJkiU5riUztWrVwurVqx3ahzWEEHB1dUXNmjVRs2ZNjBgxAmazGcnJyYiLi0NcXBwiIyNhNBohpYRGo4GHhwf8/PxQpEgRuLq6QlGUXB3Q17JlS7Rs2TLTNtmtR1EUfPPNN1m2s+b1CSEwaNAgDBo0yK7btTcpJc7t/hk75o98PEjun0aIDruItZ8/g+4TNsPVk4FymZFS4u/r9zDgi1U4eTXctgH7QsDDVYdxfVrjje5PwUWvTf+caF2c0XzeZ9jSYRBMScl2rt46etWEXpcPIlHrhJBiFQALn+VURYvllZvBzZiK+hE3Mg7BEwKBg3pCaHhj3X9JKTFnzpwMgz3v37+PvXv3olOnTnlQ2T+klJBS4s6dO5g9ezbmzp2Lpk2bYsqUKQgKCsrVf+eklEhJScHq1auxdetWzJ8/Hz179uSgcyIiogJISokjvx61+phaaAQa9qhn89//hMhExN6LtWldSzz8POBZzCPb7VMSUhAZan2QliMIAZSunf0JxO/8fRfJ0SlW9iFQ69ma0Drl/1uojKlG3L0Y7rDtCwj4lPHGiJVDUapWSYcdx0opEbL6uNWReFpnLQKfdvy4dykl4u7G27SuV0kvO1dDRESUO+7evYv9+/dbffx78+ZNnDx50upJcf7NZDJhwIAB2QqSE0JAURRUqFABTZs2Rc2aNeHm5gaz2Yzw8HCcPXsWJ06cwJ07d6wOtTGbzRgyZEi2guSEEAgICECrVq1Qp04dODk5ISYmBocPH8a+ffsQExOT6TaklJg6dSqqVauGgQMH2vW4S0qJ33//HSdPnkT9+vXttl0iIiq4HHF+L4RA7dq1sXPnTpw4cQLTpk3DH3/8gejoh9eUsvM3WAgBvV6P2rVrY8iQIejZsye8vb2zrNfa1yOEgEajgVarhbu7O4oWLYoqVaqgcePGaNWqFWrUqAE3Nze7vE+ZbcOW7adNquWoazRPPfUUKlWqhEuXLllsU7VqVVSrVs2m7TuydiK9VoOBnRuiS9OqGD1jE1bu+htmCxOWWiKlxF8nr6Hu4OmYNrIrerWuDQ3HjdATLv9/E0JEVIhER0fj9ddfT3+s0+mwYMGCDGfRdXd3x5w5c/DMM88AeDhjxeDBg3H8+HHo9fpcqbdZs2bpYXKxsbHYvHkzunbtmuV6q1evhslk3Sx+WWnVqtUjj5cvX/5EhckBwMaNGzFgwIAs28XHx2Pnzp3pj2vXrg0XFxcHVkZERERERERERESFjUajQFPIBoBo/j/4KSgoCBqNBsHBwQgODs7jqoiIKE1uDUx09MBSe8nvAzWFENBqtfDw8ICHhwdKlcr+TeO5xd7voaMGb+dnUkqc3bkEOxe8BdVszKoxIu9cwJrPnkEPBspZlJRiwOSlO/Dd6gNINZqtD70QAooQeK5ZVXz/RheULuqV4efIq3olBE0ehUOjv4S0ciCuPQkALmYD+l/cg2StHn/7lM44UE4IJGn1WFS1JVzOGlAtOuyxQDm9twfKdmuf739v8kJSUhJWrFiR/nlKe4/SAtx++OEHdOjQAYpivwHVer0egwYNgre392PLzGYzEhMTcePGDVy/fh2hoaFITEx8pCaz2Yz9+/ejefPmePPNNzF58mQ4OTnlqJ7XXnsNPj4+jy0zmUy4ceMGjhw5glu3bqXXIKVEXFwc+vbtCxcXFzz77LP8fBERERUwxhQjrh+5afV63iW94Fv28eOG7JBS4vdPNmLP/AM2rW/JgJ/6oknvoGy3j7wZBVOKfcdy28rZwwneJbIXziWlxJktZ63uQ0qJ+i/ULRDHa/H345EU7ZhgbyEEKjQph9d/GQz3ou4OfT+SY1MQfuGe1esVr+IPV+/cGfduS2if0AoUyebnlYiIKD+RUmLlypVITU21ad2ZM2eiUaNGNh0/SCkxY8YMrF27Nstr2hqNBj169MDEiRNRuXJlaLUPb4H/9zVLADAajbh16xaWL1+OefPm4fbt21luW0qJVatWYeXKlZm2FUKgdOnS+OGHH9CpUyfo9frH+o+Li8PcuXPx6aefIjEx0eL2VFXFqFGj0K5dO5QtWzbT+qxlMpkwZswYbNu2LcN7T3OiZMmS+O2337L1HURkZCRGjhyJhIQEi23q1auHCRMmZKtOFxcXu78eIqInXZcuXXD06FGLyxVFgYdH9ich+C+NRoOGDRtiyZIlSExMxIULF7B//34cPXoUV69exf3792EwGNL7cnd3R0BAAOrXr4/mzZujfv368PPzy/YEgt988w3Gjx+faZu0ADNFUaDRaODu7g4XFxfo9Xo4OztDp9Olf7dpr+sfGo0Gv/32W6bHU35+flZvt2jRojhy5Eimf3fLly9v9XbT6HQ6vPPOOxg+fHiGfQghEBwcnH7cZa3x48dj+PDhFpcHBgbatF2iNEII+Pt4YOm4lzHo2SAM+XYtbtyNtm4SCykRFZ+M/l+swqpdZzD3nW4o6m2fgEmi/IhhckRE+cioUaMQHv7Pl5Jjx45FrVq1LLbv1KkT+vXrh6VLlwIAzp49iy+++AITJ050eK0A8Oqrr2LKlCnpjz/++GM8++yzmQ4eTUlJweeff273WmrVqoVatWrhzJkzAID169fjxIkTqFevnt37yis//vgj+vfvn+WB6bx585Cc/M8ggictVI+IiIiIiIiIiIgoL4WEhMDT0zOvyyAiIiLK16SU+HvnIuxaMCrrILl/VkLUnQtY+3lndB+/Ga6e1g+0flJJKXH62l30/ewXnAuNtDpEDng4wLZ8CW/MHt0NbepVyHRshxACFfo9j4hTF3F50VrAhv7sRQDwMKZg0Pld+LFWB1z1LGYxUC5W74L51VrjzTNbUS4+4p9AOSFQZejL0Hm652LlBcfu3btx//799McBAQGIjo5GfHw8AGDnzp0IDw9HyZIl7dant7c3vvvuuywD4FRVRWJiIs6fP4/FixdjxYoViI2NTQ9zMxqNmDp1Ks6fP4/Vq1fD1dXVpno8PT0xdepUODs7W2xjNBpx8OBBvPHGGzh79mz676HBYMDAgQNx/vx5+Pr62tQ/ERER5Y2o0GgkRiVavV7l5hWh0dkWbiBViatHbth0TG+JUATK1C1t1To3j4fCurvtHKdYxaLQueiy1VZKiXPbz1v9/ulcdAionf+C7DNy59xdqKrjdk6DHvUcHiQHAOEX78GYnM3rAf9SvW1VCMXxN3GqZhX3rtzPuuF/uHm7wtXbtvMOIiKivGQymTB//nyLx1EajQZCCJhMjwcOSymxYcMGxMXFwcvL+lDVsLAwfPTRR1kGuHl6emLJkiXo0qWLxevXaccwer0eFStWxPjx4/HOO+9g6dKlmDBhQqZ1JCUlYcyYMVAzmUBFCIEGDRpg8+bN8PX1feyYKe2xl5cX3n33XbRp0wadOnVCRESExW0mJCRg1KhRWL16tV0n7JBSYvfu3Th48CCaN29ut+0CgLu7O7p27ZqttgaDAePGjcs0TK5+/fp44YUXcjWsI21SkqSkJBiND49LnZ2d4eLikh5ARET0pPD19c2V76iEEHB3d0fDhg3RsGHD9O/rVFV95O+8oig2B7kJIVCxYkW71m0vQgjUqFHD7tvV6/UOzUIQQqB169ZQFAVms/mx5S4uLujZs6dNfxuFEChTpgzKlCljj1KJMqUoAq3rVcDJ+W/isyU7MX3tARhMqlXjWVRVYsOBC6h7fjrmvtsNnRtXhZIL1yKJcpv9zjyJiChHtm7disWLF6c/rlGjBsaOHZvlelOnTkWxYsXSH0+ePBnnzp1zSI3/Vbt2bbRt2zb98fHjx/H6669bvKhqMpnQr18/XL161SH1fPDBB+n/bTab0atXL9y9ezfb69tzYIYjHDt27JHwvoxcvHgRn3zySfpjnU6HQYMGObo0IiIiIiIiIiIiIiIiIiIiAP8PkvtrgXVBcv+sjMjb57H282eQHGf55qvCJMVgwqSfd6Jp8GybguQEBFz0WnzYpyVOzh+JtvUrZuuGNaEoaPjlOyjRunHG4W25SADwSU3AkHM7UCox2vJgYCEQ4eyBudXbItzVKz0bw8nXG1WH9eLNYRlQVRU//PBD+udKCIFp06Y9MtlhSkoKVq5cafdxNWk37GX2o9Fo4OnpicaNG2PGjBm4fPky3nrrLej1+vTtSCmxZcsWvPbaaxne7GqvevR6PVq2bIkDBw6gefPmj3yeIiIi8OOPP+b7sUdERET0qJsnbAlUE6jaKtDmY0tDsgER1yNtWtcSvZseRUoXyXZ7KSUu7b0CmU/S5Co0Kpft99OYZMS9yw+s7sM3oAhcPfN/AJiUElf2X3NYoLeUEr99vAmhJ2859NhVSolL+65Yf/4qgCotK+fKuVtqQiriwuOtXq9YxaLQ6rUOqIiIiMixzp8/j4sXL2a4LC1gZNCgQRb/DsfExGD79u1W9yulxKRJk9InrrDExcUFf/zxB7p27WpV4JoQAq6urhg6dCjOnTuH6tWrW6zjl19+wZ07dzLdnr+/PzZs2AA/P78sj0mEEKhfvz5WrVr1yPXKjPreuHGjQ+6nVFUVb7/9dnpYGj18vy9cuIDRo0ejevXqKFmyJEqWLIlSpUqhVKlSaNasGebMmZM+aQkREdlOCAFFUaDVaqHT6dJ/0kJq+d1s/iClxK5duzLMfhBCoEOHDihatGgeVEZkPSEEPFyd8dWwTtj3wzDULl/M6n9rpJQIj0pAt/HLEPz9esQnpTqoWqK8wzA5IqJ8ID4+HkOHDk1/rNFosGDBgkwvJKbx9fXF9OnT0x8bDAYMGjQo01ky7GnWrFlwcXFJfzx37lw89dRTWLduHSIjI6GqKu7fv49ffvkFDRo0wOrVq+Hk5IT69evbvZbevXvj2WefTX986dIlNGnSBOvXr7e4TkJCAn7++WfUq1cPsbGxdq/JXtJmYn7vvffw+eefZzj4dc+ePWjXrh3i4uLSn/vwww9RvHjxXKuTiIiIiIiIiIiIiIiIiIgKLyklTm+fh50LR1sfJPfPRh4Gyk3qjOR4+4YsFCRSSpy7cQ/N35iNiQv/QkqqyYYb8QWerl0Wx+a9gc8Gtoe7i5NVA2k1eh1aLPkKRZvUyReBciWSYjD03A74pcRnGih3x60I5lZviygnd0AI1JsYDL23R67WW1CEh4dj165d6Y+LFi2K9u3bY/DgwemfFSkl5syZk6OgNnsQQsDPzw/fffcdfvvtN3h4eDxS4+rVq7F48WKH3wDo4eGB5cuXw93dPf05KSUWL17MmzaJiIgKECklLu+7ZlPYVZl6ATb3G307BqkJ9r05rWg5Xzi5Zj3mPI00S9w8fsuuNeREpeaVsn2eEnM31qb3L6BuAISmANzALIErB685tAtDogGzXpqP+AfWB6lZ49oh61+HUBQUr+LvgGoed+/qA5hSrDvHEUKgcvNKD09QiYiIChApJebPn5/pvYZvvPEGRo0aZTHITUqJH374wer7FSMjI7F06dJMj7uFEJgyZQqaNm1qc+hM2rXDSpUqZbjcbDZj6tSpWdYxffp0+Ptn/3hECIGWLVtiwIABmdZuMpkwe/Zsu1+7lFLixIkT+PPPPxmMhof36A4ZMgS1a9fGtGnTcPnyZSQmJsJgMCA1NRWxsbE4dOgQRowYgapVq2LNmjW5dg8uERFRXklOTsa3336b4bGCEAKjRo1i8B8VOEIINKxSGodmBePT19rBRa+DsPKinVlVMXfjUTQePhMnLofxeJqeKAyTIyKy0e7du6HVam36uXnz5iPbev/99xEaGpr++O2330ajRo2yXcvLL7+Mrl27pj8+dOgQfvzxx5y/yGyoXLkyli1bBp1Ol/7c4cOH0b17d/j5+UGj0cDf3x+9e/fG6dOnAQDffvstatWqld5eq7XP7FxCCCxZsgQ1atRIfy40NBTdunVDQEAAXn31VXz44Yf46KOPEBwcjBYtWqBYsWLo168fTp48aZcaHKVJkybo2bMnpJSYMGECAgICMHDgQEyYMAFvvfUWGjVqhJYtW+L27dvp6zRu3Bjjxo3Lw6qJiIiIiIiIiIjoSaUoChRNIfv5/6DpoKAgVK9eHTNmzMjjvUBERESUv0hVxemNP2D3ojGQag5Dp6TEg1vnsPbzZwtloJzRZMaP6w6i0fCZOH7F+kGrAgJF3J0xe9Rz+HPKIFQtY/1szGl0bq5o8+v3KNq4dr4IlCsX/wCDz++ElyE500C5q57FsKB6K7i1boYKvZ7lAPgMSCmxYsUKpKY+DOMQQuDFF1+Eq6srqlevjsDAwPS2ly5dSh/3k9eEEOjYsSNWrVr1yHglVVXxwQcfICYmxuE1lCpVCj169Hjkudu3byMiIsLhfRMREZGdSOD26TtWr6boFHiV8LK521snb0Oq9r0prVxQOQgl+8e7KQkpiAyNsmsNthKKQEDtUtluf//KA8CGt690rZIF4pzAmGpC+KV7Du1DSomYO7GY88oCGFMcE4asmlXcPW/963By18Pd180BFT3qYZjkFVj7YZKQqNqqcoH4LBERkX1IKZ+IQIGkpCT8+uuvFl+Lh4cHWrZsiYoVK6JKlSoWt3Po0CGEhYVZ1fe2bduQmJiYaZs6depg4MCBDv0bGxoaigsXLmTapkKFCnjuueesrkMIgQ8//PCRa5X/lTYZRk4mo3B1dYWb2+PHSqqq4r333oPBYLB520+CBw8e4Omnn8aCBQtgNBoz/d2VUiI8PBy9evXCJ598wkA5IiJ6IqQdu6b9mM1m3L17F3379sXly5czXKd69epo0qRJLldKZD/Oei3G9m2FI7NHoH5gCauP5aWUuHg7As3fnIMpK/fBaDI7qFKi3MUwOSKiHDCbzTb9/Pti1J49ezB79uz0x5UqVcJnn31mdS2zZs2Cp6dn+uNx48Y9FlrnKN26dcOGDRsQEJD5THsuLi5YuHAh3njjDSQlJaU//++6c8rHxwf79+/HM88888jzt2/fxtKlS/Hll1/is88+w8yZM7F3714kJyfbrW9H++mnn9C8eXMAD2eGXrhwIT7//HNMnz4dISEhj7Rt1KgRtmzZAr0++zMNEhEREREREREREVHWQkJCcO7cOQQHB+d1KURERET5hlRVhKz9ErtXjM95kNz/CSkRcetvrP28c6EJlJNS4tb9GHR45ye8/eMmJKUarQ5KUBSBbi2q48yitzG4SyPotJoc16XzcEObVdNRvGUQrJzM2e4EgGrRYRhwYTdcTYZMA+X+9imNfW2eA5ScvwdPIqPRiDlz5qSPYxJCYMiQIRBCQKvVYujQoemDraWUmD17dr65eVYIgQ4dOmDUqFGPDAiPjIzETz/95PA6hRB44YUXHunbZDIhPDzcof0SERGR/ZhNZkTdjrZ6PWd3Jzi52TY2958AKzsSQOXmFa26SS7yZhRMKfY5b8spZw8neJfMfjjfXRuC1oQQKFrBz+r18kL8g3gkRzt+bLuUElcPXMeqD9Y7JDjDmGpC/IN4q9dz93WH1slyCIvdSOD0xr8tnk5aonXSonSd7IcfEhFRwSGlhCHJgPvXHuDc9gvYPX8ftn3/F7ZM2Y5tU//Crtl7cWbzWdy9GI7k+BRItWCFzO3duxcPHjywuLxt27bw8vKCRqNBv379LB5bGgwGrFy5MtuvXUqJZcuWZdpeCIFx48Y5/P63PXv2ZHrcI4RA3759ba6jTJkyCAoKyrTNnTt3cPv2bZu2DwD16tXDe++9l+H+OXfunFX75knz119/oV27djh9+rRV74HZbMbnn3+OH3/8sdC+d0RE9OTYv38/WrVqhVatWqFFixaoW7cuKlasiPXr12f4d05RFEyePDnTQFyigkAIgRrl/bHvh+H4fGA7uOh1Vo1rkVIiOdWI9+dsQecPFiE8Kp7HhlTgafO6ACKiwiw5ORmDBw9+ZFDmvHnz4OLiYvW2SpUqha+//hrDhw8HACQkJGDYsGHYsmWLXWu2pGPHjjh37hwWL16MtWvX4sKFC4iIiICPjw/KlCmDrl27YsCAAShdujQAPDITr5eX7TP0ZcTLywt//PEHNm3ahC+++AIHDx60eMFXp9Ph6aefxsCBA+0aaucInp6e2LlzJz799FPMmjUrw9mMfX19MXr0aIwZM4YncERERERERERERERERERE5HBSVXF47WQcWfsVpLTzTehSIuLWWayd1AXdx22Ei4evfbefj6iqxNq9f2P4d78hOj7Z6sGpQggUL+KOmaOeR9enqkJR7DvPrM7dFa1XfIcj736Fays2QTogcCC7BIB6ETfwyuX9WFrlaaQqWiCDG+gkBKbvvoEyZc5hZJfqVs9CbYt/77d/78F/95wbdWTHmTNncPXq1fTHgYGBqF69OoCHNb700ksYO3YsUlJSIKXEmjVrMGXKlHwzvkYIgQ8++ADz589HZOTDwMm00LuRI0c6/AbUkiVLPvbc/fv3HdonERER2Y8x1YSUuBSr13Ny10NjY2CzVCWuHbHvROFCCJStl/lk4P9142io5VBmC5zcndBpTHu4eDhbtV5WXLxdoHfJ3nGblBL3r9y3+lxJAvAu6W19cXngztkwSHPu3KgopcSeeftQulYJPD2wmV3PU1LiUmBIMlq9nndJLygax58vJcYk4ebxW1avV7pmSbj5uDmgIiIiygtSSiRGJ+HstvM4tPwIbh4LRVJMMiD/f13vX8cc6X8nxcPjolI1SqJhj3qo93xteJf0hlDyx/W+jKRdL7NECIHBgwenv8YXX3wREyZMgMn0ePiwlBLz5s3Dm2++ma171gwGA44fP55pGzc3N7Rr1y7LbeWElBK7du3K8jiya9euNh8TCSHQvXt3HDhwwGI/qqrixIkTqFChgk19ODs7Y+TIkVi8eDGuXbv2yDIpJT766CN0794dbm6F73hl8uTJMBqNNoV+qKqK999/H61bt0atWrUcUB0REVHuiIyMxJ49e7LVVgiBNm3a4Jlnnsk3310T5ZSTXosP+rTCc82q49XJK3HyarhVx4dSSuw4fg31Bv+ARR/0RIegyvz9oAKLYXJERNlUrlw5u6fIuri44NKlS3bb3rBhwzBs2DC7bc9a7u7uCA4ORnBwcKbtpJQ4efJk+uPAwMAs29vi2WefxbPPPouoqCjs27cPd+/eRWRkJLRaLXx8fBAYGIj69evD3d3dpu3bYsCAARgwYEC22u7ateux57RaLT799FNMmDABe/bswY0bN3D//n0UKVIElStXRsuWLaHV8s87ERERERERERERERERERE5nlRVHFozGSHrHBAkl96JREToGayb1BXdx22Es4ePY/rJI1JKxCWlYvSMTViy9QTMNoS0aRQFL7euhWlvdoGPp6vDBrRqnJ3QZNo4+NSohGOfzIBqMFodQmEvCoBmdy8hSeuElZWawCQyDvQwqxLvLTuCEr6ueLFpObu+N1JKGM0qIuNTceN+PC6ExeFSWAzCIpMQFpMMg9EMVQUUBdDrNCjt4wp/bxdUKemFKiW9UMHfAz4eTtBplFwfhCylxMyZMx+ZAHPQoEGPjDkpUaIEmjZtip07dwIAYmJisHXrVrz44ou5WmtmvLy80Lt3b8yYMSP9tdy4cQM3btzIcjxSTmW0zzK6yZaIiIjyJ1OKEUaD9WFXTm5ONoeFGJIMeHDj8Ymkc0LvqkeRUt7Zbi+lxKX9V2DtUXzZ+gF45t32eR6UkhiZZNN6WifbAgBzk5QSVw5cg7Ry76TtEVvOzKQqsXLMOpSuUQrlG9vvfCklPgVStb4iJzcnu/SflYu7LsGYbN3vv4BAUM8GvIGUiOgJIKXEg2sR2PLtdhxbcwKpCalZ3reWvlw+DE29evAarh68jrXjfkPVNlXw3EedUbp2aSj5MFQuIiIC27dvt/gafXx80KJFi/THZcuWRfXq1XH69OkM21+6dAlnz55F3bp1s+w7JiYmfRIIS6pWrerwySuklDhz5kymbZydnVGxYkWb+xBCoEmTJlnWcfr0aXTv3t3mYwpPT0989tln6Nu372P79ObNm5g/fz5GjhxZqI5ZpJQwGAwAHu4HDw8PtGjRAoGBgTAYDDhy5AhOnDgBk8lk8fcgNTUVw4YNw+7du7MVlEhERFSQCSFQsmRJLF68mHkE9MQRQqBGeX/snzEcXy7bjS9X7IbRpGY7q0RKiXvRCXhu7BKMeqk5PhnQDk56/p5QwcNPLRER5bpdu3YhOjo6/XFQUJBD+/Px8cFzzz3n0D5ym06nQ9u2bfO6DCIiIiIiIiIiIiqkFI2SLwdCO5IiHg4mCAoKgkajydbkKkRERERPMqmacXDVZzj62xTHBcmldybxIPQ01k7qgm7jNsLlCQmUk1Li6MU76P3pClwPj7F6sj0hBEr4uGPOO93wTOMquXKMLjQaBA7vDb9GtbF3yHgk3AjLs0A5DSTa3f4biTonbChbH6qiZNjOaJIY8OMe+Hro0aZmSZtvpJNSQpUS4dHJ2H4mDL+H3ETIlUiExyTB/P+ghOy8FWndaxUBfy8XNAkshueCyqBVzRIoUcQVisg4qMyeYmNjsW7duvTPnF6vx8svv/xIv4qi4I033sCuXbsgpYSUEtOnT0ePHj2gWHivc5sQAv369cOMGTPSn1NVFXv27HF4mNy/xz6l8ff3d2ifREREZD9mk2pT2JUN2c/pUhMNqNWxusVjxuS4ZFzYcQmqOfudFC3nC72rPtvtpVki9HhottunqdSs4j+pZXko/n681esI+fB153sSuHbwutWrVWxWERq9gku7rtg0gbspxYTZvedj7P4x8C7pbfX6GUmMSrI6FA8A3Hzc7NJ/ZlRVxa45e60//9YJ1H2hdqEKZiEietJIKZESl4LfPt2EvT/thznVbFMY67+2CGOqCX9vOYezf55H7Wdq4uUpPVCktHe++XshpcT69euRlJRxIK8QAp07d4ab2z9/gzUaDQYPHoy33norw7+XUkrMnTsXM2bMyPJ1RkREZDn5Qr169Rz+fpnNZty/fz/TNj4+PnB1dc1RP6VLl4aiKDCbzRbbXLlyJUd9CCHQo0cPTJo0CefOnXtkmZQSX3zxBQYMGAAvL68c9VMQCSHQvXt3zJ49G76+vunPq6qKM2fOoGfPnrh27ZrFz/Xhw4dx8ODBR8IViYiInjRCCFSuXBkbNmxAiRIl8rocIodx1uswcUBbPNu0CvpNXonLtyOtGtZiNKv45pe92HPyOlZ81Atl/PPPeR5RduSPET1ERFRomM1mfPTRR+mPNRoNOnXqlIcVERERERERERERERFlX0hICM6dO8cgOSIiIirUVNWMAys/zZ0guTRS4sHN01g3uStSEqJyp08HMprM+OaX3Wj51lxcv2t9kJxGUdCrTW2cWvAWOjfJnSC5NEII+NSvgWd3L0OVwS9C0Wv/SUjLZVqpouuN42gbdg4KLH8WUwxm9PxmJ45djbD6vZZS4n5sMmZsPY9G729AxeBVGDhjD9YdDsXtyESYzBJSZj9TL62t0SxxOyoJqw/dwKs/7EGl4FVo8sHvmL3tIh7EptgUBJFdW7ZsQUxMTPrjpk2bolSpUo+1a9euHby9vdMfHzlyBKGh1oePOFLVqlXh7u6e/lhKiT179jj0/ZNS4s8//3zkOUVRULRoUYf1SURERPaVmpgKW9JDkmOSYDbZdg7k6e+BwUsGYMjSjH9em9cXGq11t/iUa1QOwopzgZSEFESGPh6KmxkhgEpNK+T9zXISMBktB3NYXE1IxNkQQpfbjKkm3L0YbvV6TXo1xPDlg1CsUlEIGxL/pJSIDY/H7F4/wZBksHr9jJhNZptqSUlIsUv/mYm4HokrB69ZvV75hmXhE1DEARUREVFukFLi2qHr+LTRl9g1ay9MOQ6Se3TbqknFqY1n8GnQFwj59ZhNocWOYDabMWvWrEyvkw0aNOiR4zwhBF544QXodLoM20spsWbNGosBdf+WVYAbAJQrV87hx5kGgwFxcXGZtnF3d4dGo8lRP56enhbftzTh4dYf7/2XXq/HlClTMpzw4/79+5g+fbpDr43mR0IItG/fHsuWLYOfnx+EEOk/Go0GdevWxZ9//vnIte7/SptMpbC9d0RE9GT799/DMmXK4NNPP8XRo0cRGBiY99f6iBxMCIGgqgEImf0GhnZpBK2VE+ZJKXHowi00GDoDGw9c5HEiFSjavC6AiIgKvgMHDqBMmTIoXbp0pu0MBgOGDh2Kffv2pT/XtWvXLNcjIiIiIiIiIiIiIiIiIiKi/EFVzTjwy8c4vvH73AuSSyfx4OYprJvcFd3GboSze8G7kVtKiXvRCeg/eSW2H79m9YBTIQT8i7hh1ugX0LVptVwNkftvHToPNzT86l1UfKULDo/5+n/s3Xd4FNXfBfBzZ7alh/RG70GaEDpYQFSkCIrKDyn2ktdesWHBhr2AiCgoHUQUFUSlF4HQey8JJCSQXjbb5r5/hESQlN20Tcj5PE/E7LTv7sxOptx7Bmnb90Nq1b1NAAapITbIjFbdG2DS5jPQSuismWW2YuA7f2LDO4PQJNSnzAbymiaxLyEdH/yyB4s3n0Ke1X5RYFzlf+5Wu4Ztx1Ox/cRGPP/DFtzatSHGDW2PVpF+lbqeHQ7HJZ3ihBB45JFHiu186OPjg0GDBmHmzJmQUsJms2HmzJl45ZVXakwHAy8vL9SvXx/79+8veu3QoYLG3FVVY1ZWVtFnUig4OBihoaFVsjwiIiKqfEZPQ7mmM2flw2a2Qm90vStOWccmqfHpsFudD0sTQqB5r6YuHfOkxqfBnm93enwAEIqC8NZhLk1TJQTK9blDAueOna/8eipZdko28tLNLk0jhEDDmAbw9PfEoz8+gPev/djleRQ6uTUec55YgNFT/gdFda1jZWUxZ5WvdmdJKbFs4l/QXAyEFELg+v+7pthzJiIiqvmkJrF+xj+Y//SPsFsdVRYAIKWEOTMf0++fhVM7EjDs7cFQdRULJ6uoEydOYM+ePSUODwkJQUxMzGWvR0REoEOHDtiyZUux0507dw5r167FzTffXOrybTZbmTVWx/U0h8MBh6P042x/f/8KX0tUVbXMQDpnPpOyCCHQt29fXH311di2bdsl27SUEh999BEeeughhISEVHhZtYVer8fkyZNhNBpLHKdRo0Z48cUX8eKLLxa7H5BSYvXq1TCbzfD09KzKcomuSP/9XkkJSBQ8jMnu0ODQJDQJODQNeRYHHJoGh5TQNAmHBpitduRZ7NAkLtznKxi/kBCAgIAQgKIImPQqvE16KBd+Vy/8mPQq9DoFiij4XacqUFAYqHVpzTXlPhdRZenSpQt++umnS14rfBhWZGQkwsPDodfrue1TnePjacSkp4ZgYPdWuP+DRUjJyHP+vFACadl5uH38LDx9Ry+8fk8/GPWM6aKaj1spERFV2NKlSzFx4kQMGDAAt956Kzp37oxGjRrB29sbOTk5OHHiBFasWIEvvvgCx4//+yQvLy8vTJw40Y2VExERERERERERERERERERkbM0zYENc1/Fjt+/cEOQ3AVSIuXkLix+Z2CtC5STUmLljmMY9fZCJKflQMK1jouKIjCwW0t889wwBPl51YiG3kIIBHRojRuXfYPEvzZg+xtfIvtIfEHj26p+MrMQ0Pv7oOOrj6LZ3UNwi6pA/T4Ony3dV+yipQRSsi244Y0/sHbCLYgK9Cp2tlJK7I3PwLjZcfhr1xnYNA2Q1fdZSwnkWuyYve4Y5m84jn7tIvDhmC5oHVnxDo0AcOrUKWzdurXodz8/P/Tv37/YcYUQePTRRzFr1ixIKSGlxLRp0/D888+X2jGvOimKclmY3NmzZ+FwOKok7MFut+OZZ55BcnJy0WtCCAwZMgQmk6nSl0dERERVQ9XroOgUOFwIbwMAe74dqafS4Olf+eECJ7acBFw5RxBAww71XVrGyW3xLp+HeAV6wSfI26VpqopPqE+5pjv6zzFc/3/X1IhzqJKc2ZcIWUIwdkn0HnoENgwEAIQ2D8E9347ClDunwWFz/XxdSolNc+IQ1T4KfWMr9lnpDOXrqpaWkA7NoVVZ8M75E6mIW7DVpa8ZAPhF+KLdTVdVSU1ERFS1pCax/JMV+GX8b9Ac1XM9W3NoWPHFamSdy8LYqXe7LVBOSonp06eXGKImhMCwYcPg4eFx2TBFUXDvvfciLi6uxJCFL7/8EjfeeGOp19+cOZ6oqnA/V9ntrgUuu5ter8dnn32GPn36XLaOs7Ky8P777+PDDz+s0ce/lalLly5o3LhxqeMIITBq1Ci89tprsFgsxY6Tnp6OhIQEtGzZsirKJKp1CvfREoBDk7A7NORbHcjJtyEzz4rzWRak5ViQmJ6Hc1n5SM+x4ExaHvIsdqRk5iPPYoPFpiHbbIPVoV0IjpOwa9qFsLn/Lue/fxP+uw+TF16TEBcNE+Lf31QF0KnqhSA5AW+jHia9CqNBRaifCV4mPUL8PBDqZ4K/lwFRgV7w9zIg3N8TPh56+HgWjG/QqdAp/wbQ1ZX9KdVu4eHhGDp0qLvLIKqRFCEwoFtL7Jz2OO59/0f8EXfUpXMRm0PDxHnrsGHvKcx77S6EB/rybwPVaAyTIyKiSmGz2fDLL7/gl19+cWp8o9GIGTNmoHnz5lVcGRERERERERERERFVtoKnddatG+GFbzcmJgaqqiI2NhaxsbHuLYqIiIioGmkOO9bPeQU7ln1Z9SFhZSkKlBuEoS//BpOXv3vrcYLV5sA7s1fhvTlrYLW5FlgBIeDnYcCnjw/C3f06QFUrPyCrohSdDpE39UFEvx5IXr8dez/6FilbdkPatUrfXiQAg683Wtw7DK3/bxSMAX5F5ycTR8cgKd2MBf8cL36xEjh5PgcD3/kTq98cAH+vfwPRpJRIzbbgpdlb8cOao7A6HBdC5Nx07iMBu0Ni+Y4zWLnnF4y5phneHtkZgT7Gcp+PSSnx/fffF3WOFEJg4MCB8PPzK3GaDh06oGHDhjhx4gQA4PTp04iLi0OvXr3KVUNViIqKuuR3s9kMh8MBvV5facuQUuLs2bN4+umnMX/+/Esal5tMJjzzzDN17jyZiIioNtN76KE36V0Ok4ME9v65H1HtIiv1b7+UEkc2HHPp0NnoZUS9KH/XlrHuqMtBWuGtQ6Ez1oyuRz7B5Qu1O7zhGGz5Nhg8DJVcUeWQUuLoP8fLHvE//KP8YPIuOKcRQqDtTW0w6NUB+GX87+UKZpGaxOJXlqB+2wi06NO83Nu4T5BXYf9+l2QnZ8OSY6mSsEbNoWH+sz/Clu/ad14IgRsevw56j8o7tyAiouohpUTcgm3VGiR3ybLnbYdfmB9umzAEQqn+a0YWiwUzZ84s8ZhACIGxY8cW+/deCIFBgwbhiSeeKDZ0S0qJ1atX49y5cwgNDS2xBoOh7GOvM2fOQEpZpdfVVFWFTlf68Wx2dnaFg+1sNluZoXTOfCbO6tq1K6677jqsWLHiktqllPj666/x+OOPo2HDhpW2vJpKCIEBAwY4tQ0FBwejQYMGOHLkSLHDpZQ4dOgQw+SoTrgkKM4hYbE5kGW2IjkzH2fS8nAqJQdHz2Yh4XwOEtPykJhhRq7ZihyLHXaHhENKCOnyaV8Zyvoei6J/L17uxftAhwZYL9oXp+dYi/5/b0lzFQCkhFAU6BQBk0GFr0mPEH8PBPuY0DjUG03D/BAV4IWGIV4Ir+cBP08DPAw66FWFgXNERLWEEAKhAT74+e3R+GrJZoybuhz5VrvT5wFSSqzfG4/OD03CvNfuQu92jbnvpxqrZtzRISKiKnPq1Ck0bdq0Uud57NixSy4menq6dsO2bdu2mDJlCnr06FGpdVWWZs2a4eTJk5U2v++++w6jR4+utPkRERERERERERERkfvExcXB19fX3WUQERERVSvNbsParx/Drg2z3B8kV0hKpJzcicVvD8TQl36Dydvf3RUVS0qJ5PQcjHxrPlbvOuFyhzQhBHpd1QA/vHQHGoT61+jGqEIICL0e4dd1Rdg1MchJSMKJBctwYuEfyDl5usLBckKnwrdFI7S493Y0GnYDDP6XP+1ZpyqY/n+9kGW24o8dp4vvxCKBPfHpGPjOX1j+6o3wMunh0CR+2nQSj327EeeyLBfKLP9nXdxqKu9blwCsdg3TVh7Gkq3x+PzeHhjWvSHUcnRCtVgsmD59etF2KITAww8/XOp2ZTAYcM8992D8+PGQUkJKic8//xw9e/asMdvjfzthOhyuBUTk5uZiwYIFCAkJuWyYzWbDiRMnsGLFCqxcuRK5ubmXfI8VRcGrr76KZs2ala94IiIicgu9SQffYG/kZ+W7NJ2ExMYfNuOGJ66HzlB53XE0u4b4nQkuTRPUKAAGT+fDKKRD4tT2eJeWISDQvEfNOM4RQqB+uyiUJ6Us91wujm06gdbX1dBQCAkc23jc5fPFRp0bQLkobFwIgf5P9UPCrjPY/tPOcgWi2C12fD1yOl5a/yyCGgW6PD0AmHw9oOgUl8MarXk2nNmbiOa9Knebk1Ji84Kt2Lv8AFzddvwj/dDr3ppz7kNERM5L2n8WM/9vXrUHyRWSUmLF56vRuHNDXD20Q7X/LdmyZQvOnDlT4vCIiAi0a9euxOFhYWHo2rUr1q5dW+xws9mMxYsX46GHHirxvZUWNFeo8AEWVclgMMDX1xeZmZkljpOVlQW73Q5VVcu9nKysLNhstlLH+e9DMSpCVVVMnDgRXbt2vWy5eXl5GD9+PKZPn15py6vJ2rZt69R3TFVVNG3atNQwuWPHjlV2eURuVXBfp+A+U/aFsLjjyVnYn5CBA2cycCQpC2fScpGRY0We1QFNSkBKSCfukdWQO8YVVnhPUGoSVk3CateQlWfD6bS8S8a7ODTOqFPh56lHmJ8HGof5IDrKH9FR/mgS6oPIQC/U8zLAqFeL7uXxnJKIqObQ61Q8NrQ7ru3QBHe+PgeHT6c6fx1VSiSn56L/c9Px9v398cRtPaGrgQ+DJGKYHBHRFU5K6XIDSWfmebGXXnoJAwcOxB9//IFNmzbh8OHDSExMRE5ODoQQqFevHiIiItCzZ0/cfPPNuPnmm2v0ya/dbq/Uz0zT3HPjgYiIiIiIiIiIiIiIiIiIqKIcditWTI3FgQ1za06QXCEpkXxyJxa/OwhDX/oVJi9/d1d0CSkl1u85ibvemIektBy40q1CCAGjXsWro67HM3f2gkFfu5r6CUWBT8NItH32Plz11FiYk84heeMOJK7chLQdB5CXlAJHXj40TStoQ1K4bYl/O1UIvQ4ewfXg37YFom7sjfA+MfCsHwahqqW2OzEZdJj71HXo98YybDt+vtjNVkrgn8MpGPXZWnw0tgvGzd6KHzedhKa5vo3rVIEwPw+0ivLDVQ0C0DLcF2EBnqjnZYQiAE0Cqdn5SM7Mx5HETOyJT8fBMxlIzjDD7tCc6pBTWHNyZj5GfLYKI7Y2xqQHesDXhfAQANi8eTNOnz5d9HtUVBQ6depU6jRCCNx999148803YbcXPJl72bJlSEtLQ2Bg+cIlqpqr7ZLMZjPGjh1b6jjFNSBXVRVPP/00nn/++RrdFoqIiIgup6gK6neoj5Rj512e9tyx89izfB86DGxXaccA5qx8ZCaWHG5RnMZdGkG4EDCcn5OP1Ph01woTQNMejWvMsU6DDlG4+PTBWVJK/DZhGVr0bgZVV/6AkKpis9hx9nCyS9MIIdC8V7PL1o2qUzB6yggkHTqLxH1J5erdn5eWi8nDv8Hzq56Eydvk8vQePiZ4B3gh82yWS9NJKbH2u41o1rNppW5zyYdTsODpRZAunu8JITD0zUEwerl23kVERO5nt9jx3X0zYcsrPdirqmkODbOfWICWfZrDO8i72pYrpcTkyZNLDEQQQuC2226D0WgscR5CCNx///1Yt25dsfORUmLKlCm4//77odMVf+06KCgIer0eVqu1xOVs374dmqZVKMStLKqqIjw8HAkJJYc3p6enIycnp9TPpCwnTpT9MJkWLVqUe/7Fad++PYYOHYqFCxdesmwpJebNm4eXXnoJ/v7+lbrMmsjHx8fpccPDw0sdnpeXBylljTkHInJG4fffoUmYrQ6cyzLjSFIW9sanY8eJVBw8k4mE8znIzLPB5tAgIQFZ2jbO7b84hbtZKSXMVjvMVjvOZpix81QaFm8+deEWp4QiFHibdAj180CLSD+0bxSAjo0C0TLSD2H+nvD10EOnMmSOiMidhBBo1yQMm6c8isc/W4JZf+9yuq2IlBJWmwMvfP0H4g6cxtRnh8HXq/znEURVoXa1MCMiohqrXbt2pT6RhIiIiIiIiIiIiIiIiIiIiGoXh92Kv79+BAc3zK95QXIXCCmRcmIHfn5nMIa+tATGGhIoZ3c48MnC9Xj1uxWw2RxwNUiuSXg9zHntLnRuEVmrOxIIISB0OnjVD0eTO8PR+I6bIR0aHPn5sKRnwZqeBXNyKhwWCwBAUVUYAv1grOcPU5A/9N6eEDqdy5+Br6cey17pjz6vLsWB0xnFjiMl8MvWU1i2IwFWh+bUJl5YRj0vA65rG447ezRB1+YhCPUzQa9Tit5zSQo79NgcGlIy87HpcArmrT+OlXsTkZlnc6oGTZOYs/44th9Pxc8v9kOzMF+nPh8pJb788suiGoQQGDVqlFMdJBs0aIDOnTtj06ZNAIDc3Fz88ssvuOeee2rE9pmfn3/J72oZgYPFcfZp44XzbdasGT7++GMMGDAAisKnjRMREdU2Qgi0uaEVtv+00+njgEJSSix49ie07N0cnv6elVLPuePnYbe68CDsEoLESpManwZ7vt2luoQqENGq9LCF6hTcOAgGbwMs2RaXpz226QS2LdqBmDs6VekxrJQSGYmZMHoZnN4+slOykZdudnlZTWIaF/u6yduERxc8gPeu+Ri553PKEb4HJO5Lwg+PzMV908dA1bl2vKsaVIS2CnE5TA4Advy8C2mvD0RggwCXpy1OVko2Jt02FXmZrn2+Qgg06dYYnW+/ukac8xARkfOklNi2eAdO7z7j8nFeVchLzcOSCUsx4pPh1fY3JSMjA0uXLi11nCNHjmDKlCml1hQXF1fqPPbu3YujR4+iVatWxQ739fVFWFgY4uPjS60jLS0NwcHBpS6rIoQQ6NixI7Zs2VLiOFarFQcPHkTPnj3LtQwpJdatW1dmHe3bt6/U7UBRFEyYMAG//PILLJZLj5GtViuee+45fPzxx5W2vJrKleuzZQUXappW0XKIqpSUEhKAxebAuUwLjiVnYduxc9h05Bz2J6TjTJoZuRY7NKmVEhjHc5yqUHDYIeCQEpl5NmTm2XA4KQu/bU0our9o0KkI9TehVaQ/OjUOROdmQbiqQT2E+nnCy6RCEYLnoERE1cjX04TvXhiOGzo3R+xnS5CVZ3G6aY+mSSxcvRf7Tibj5wmj0CQigPtwqjEYJkdEdIVr1KhRjbj4XZucPHnS3SUQERERERERERER1WiKqkBR6tZNb0UUXGuPiYmBqqqIjY1FbGysm6siIiIiqjoOuxV/TXkYhzYuqLFBckWkRPKJ7Vj87hAMHfeL2wPl0rPNuH/iIvy8Yb/LH52iCNzdrwM+f2IwfDwMV1xj04JwORWKtxf03l5A/aoLpgj0MWHpy/3R55XfkJCWV2yjXykBi73szmFCAIoQ6N4iBE8PboO+bSPhbXI95K5wfINORVSgF27v3hi3dWuEbLMNf+9JxMdL9mLzkXNwSFlqI2UpgYNnMtDtxV+x4Jm+uL5tWJm1nD9/HsuWLSv6XafTYezYsU69B1VV8eijj2Lz5s0FHZUuBNONHj0aOp37m6H+t0Oqp6enSx0IRSmdc4QQUBQFHh4eCAsLQ+/evXHXXXehd+/eMBiuvO8oERFRXdLq+pYQOgFpc/18Jz0hAzPun40HZ98DnbHix0Mn407BtQBqoEGHKJeWcWpbvEvLAACvAC94B3u5NE1VMvmY0KRLIxxYccjlaaUmMfeJBYiIDkfkVRGVfhwnpUReeh7+/mI1Nsz4B2/ufsXpaRP3J0G6GFph8DQgsEG9EocHNQrEgzPvwRdDvoLN4lqIIFDwfrYv2onINuEY8MKNLn9e7W65CkdWH3VxiwNs+XbMe/pHPDLvfiguhthdrDDU79NBk3Du+HlXN30YvAwYO3UkVH3pYSNERFTz2K12/PrW0hrTl05KiX9mbsGAF26Ef7hftSxz6dKlyMnJKbWmpUuXlhk4VxZN0zB9+nS89957xR4r6PV6dO/evdQwOYvFgp9++gkPPvhglV1nE0Lguuuuw9SpU0vdLhYuXIgePXqUqw6Hw4FFixaVOn+dTocOHTq4PO+yNGvWDKNHj8a0adMuWb6UEsuWLUPbtm0rfZk1zX+D9EqTlVV64LHRaOQ1X6oxCu7HAHlWOxLT87DzeBo2HUnG5iPncSQxE+m5VmgXvveX7364HdckhevHYnMg/lwu4s/l4q9dZwAU/J3y1KuoH+yNzk0D0bt1GK5uEoTGId7w8dBDVRgwR0RUlRRF4H/9OqBzqygMf30O9p1Icfp8UkJi36lz6ProZMx8+Q7cFNOC+2yqEfg4RCIiIiIiIiIiIiIiIiIiJ8XFxWH//v0MkiMiIqIrmsNmxZ+TH7gQJFfwmgQgIQpSC4SAFBceaC+Eyz/ywo+48LsQyr8/ECjo4KAAuPCaogJKwb9CUSFUHRRVD1Vn+PdHNeD8qT1Y8uEdsJpL7wxUVaSU2HciGd0enYyfNxxwKUhOQMDXy4jvnrsN375wG3w92WGpMjQI8sLyV29CgJexXNMLAaiKwKDO9RH33hCsenMAbu3SCD4e+kpbP0II+HoaMKxrI6x5awA2vzcYN3WIhFpGgLeEQHquFYPf+xOLN58qtUGzlBI//fQT8vLyil5r3749Gjdu7HSdt9xyC7y9vYt+3717N44ePer09FXF4XBc1iE1MjISqup86IOPjw+2bt2KhISEYn9Onz6NM2fOYN++ffjmm2/Qr18/diokIiK6AtSL8EfD9vXLNa2UEruX7sXUkdORl2GuUFiJlBKH1x916fzB6GVEvUh/15ax7pjLYdeR0WHQGdwfHlxIKAK97y1fwAcAmLMs+PSWL3FqW3ylBcxIKZGVnIVfJyzDq23fwrL3/4R3sDcMnganpz+y0fV1E9CgHoylnOcIIdCiTzMMnTAYopwPB5JS4rd3/sCeZftc+ryEEOgwoB1EecLgpMSeZfuw9P3l0FwM2Pt3FhJHNxzHu70+QPJB5zt/FhKKwJ0fDENw06ByLZ+IiNzrxJZTOH8izd1lXMKaZ8XWH7dXS8Cdw+HA5MmTq2VZUkrMnj0b+fn5xQ4XQmDkyJGlHrtJKfHuu+9ect2yKvTu3Rt6vb7UOubNm4fc3Nxyzf/QoUPYt29fqeM0adIEISEh5Zp/aYQQGD9+PLy8Lg+Bttvt+PDDD2G3ux4uXJucOHHCqfGklDh27Fip49SvX75zRKKKKnyQT77NgVPncrAk7hSe/X4Luo1bggYPzUP0Ez9hxKer8Olv+/HPoRScz7bAoRWEzdWQ/FRyUeG60zSJHIsdB05nYOaaY3jo6w2IeeEX1H9oPto9vRhjv1yL71Ycxv6EdGSbbdA0WWNCc4mIrhRCCLSsH4yNXz6CsTdd7drD1qVEWpYZQ1+ZhQ/mroXdUb5rmkSVqebc1SEiIiIiIiIiIiIiIiIiIiIiIiK3klIiJz0Jra+5G9HXjLrw4HpREPJ2UQAcIC5kwwkIRYXBwxeA+Hf8C/8WvlYQEoeCeVz498LIF3Umu2hccfmwwnmqeiOEovt3cUXzvjBcV77gsIrQpMSi1btx/4eLkZ1nQ1EKnxOEEOjQLAzzx49A04hABlRVIiEEWkb64ZcX+2HAhOXIzne+05wQQLv6AZj0UA90ax7sWoPhclIVBVc3CcRv4/pj0+EUPPrNRuyJTy+1I5DZ6sDIT1dj1hPXYli3hsVuP8V1IpVS4scff4SiOBfwkJOTA5PJhOzsbACApmmYNm0aPvjgA7dus9nZ2UhMTLzktTZt2rhUk8FgQOvWrWEymSq7PCIiIqrBFFVB/6f7YurI6eXqhFsYKDeh2/sY8clwtO7XEqpOdfnYyGHTEL8zwaVpAhsFOB1WBgBSkzi1Pb7sEf/DJ8QXuam55Q4jK4vB0wC9qeRQj+JcdWM0fEK8kZWc7fLypJTIPp+HD/t/jlvG3YjrHrkGRi+Dy+tMSglbvh2ndpzC6inrsPv3fbCZbUXbUZOYRs5/ZhI4tvG4q28FjTo3LHMZQghc90gfnNoRjy1zt5VrO9dsGr67bybGrX0Goc2dDz8JbBiAhh3r4+RW14P7pFYQYpebmoshbw5yeh0VBPtl49e3l2LjD5uh2RwuhyoIIdBjdDd0H9WV5+ZERLWQlBKb58a5u4xibZ4Th+sfvQZCrdq/LwkJCdi6dWuVLuNiiYmJ2Lx5M6699tpih19zzTUIDAzE+fPnS5xHfHw83nnnHUyYMKHCf3+llMXOIzQ0FJ06dcI///xT4rQpKSmYOnUqnnrqKZfqcDgceOmll0oNwi0M1tPpqqZLf0REBB566CF8/PHHl10DtlgsVbLMmkJKiVWrVuH+++8vc73l5uaW+oAUIQSaNGlS2SUSFUtKCU0COWYrDidlYc3+JPy9KxG7T6XjXFY+NCkZEldHFa73PIsdB89k4uCZTMxaewxCCHibdGgV6Yc+0WHoe1Uk2jeuhyAfE1RF8ByWiKgSeHsY8M2zw9DrqgZ44svfkZNvczq11Wpz4KVv/8TuE2fx9TND4WVy/vo9UWVjmBwREREREREREREREREREREREREBKOgs4xfSEH4hDd1dSq1htdnxyrd/4ZOFG+AopcNYcVRFwYMDO+ODRwbAk41Jq4QQAj1ahmDOU9di6MQVsDtKb+wrAHgYdHjzro6IvTkaRr1aLXVeTFEEerQKxaZ3B+Pz3/fjjYXbkV9KEILFruHeyetwdZNANA71uWz4oUOHsG/fvkte27ZtG+66665y1yilxKxZs/DGG2/Ay8ur3POpqD179iAvL6/odyEEevfuzU4zRERE5JS2A65CaPNgnD2cUq7ppZRIPZWGSbdPRXDzYPS5pwfa3twG9aLqQW/UFR/2JQGH3QFzVj5O7zmDzXPikBaf7tJym3RxIawMQH52PlIT0lxaBgBs/XE7tv+80+XpnBW76CG06dfKpWkMngbc9OwNWPj84nKFo0FK2Mw2/DL+d6yavAY9RndHp+EdENw4GAaTvtjPVTokbBYbslKyEb/zNHb+sgsHVx9GdkrOhVn+W4cA0LxXU6ePR20WO84eTnbpLQgh0MLJZSiqgru/vAvJR87hZNwpl5ZTyJyZj8m3T8ULa56Gp7+nU9MoqoJbxt2ESbdNLdcypSaxaso67Px9D/o/3RcdBrWDb4gPFFW5bDxLrhWJ+xKxfvo/2LZ4B6w5NkgXAt4LCSHQ8tpmGPHJ7Zcth4iIagfNruHQ6sPlO0aoYkmHkmHONMMroOquo0kpMXv2bNhstipbRnG++uorXHPNNcUem/j4+OD//u//8MYbb5S4XqSU+OCDD9C2bVvceeed5bquJ6XEgQMHYDab0alTp8uGq6qK5557DrfddlupdYwfPx79+/d3+mEVUkrMmzcPv/32W6nbnclkwr333ltl1yyFEHjxxRcxffp0pKW5ftxf2/3111/IycmBj8/l18YvtmHDBmRlZZU43GQyoVmzZpVdHhGAgv2FQ5NIy7Fgb3w6/tp1Bn/tTsShM5nIs9ovjOPmIqnGkvJCgHqeDVuOnMeWI+fx4ZK9MKgKGgR5oXvLEAy4uj66tQhBeD0P6FWF98mIiMpJUQTG3twZnVrVx+2vzcbRM6lOT6tpEnNX7MaR06n46c2RiAjy5f6Y3IJhckRERERERERERERERETkEkUVUJW61ZFGEQWhIDExMVBVFbGxsYiNjXVzVURERETkbmlZufjfW/Pw17bjLnZSFPDzMmLqM7fitmvaQnEhCILKJ8DbeKGhbtnr6alBbfDUoKvc3rDXZFDx3K1XoX+HCAz/aCWOJ2cX25nIZFDx9UM90CjE+7JhUkp8/fXX0FwMOnRGSkoK1q1bh5tuuqnS5+0MKSVmzJhxyWuqqqJnz55uqYeIiIhqH51BxZ0f344vhkyB5ij/8ZLUJFIOpWDRuJ+x6KVfYPIzoV6EPwKi6sHD3wOqqsDh0GDJtSAzMRNZKdnIOZ8Du8VRML0L5xICQPOezoeVAUDqqTTYzXZX3xakJuGwOlyezhmKqiC8VajL0wkh0Pu+HljzzXqkHDlX7rAYKSUyz2bjjw/+wh8f/gWTrwkBUfUQ2CgAJh8TBACHXUPW2SxkJmciKzkblhwrpEMr/YxCEWjQsb7TdWSfy0Zeutnl+ht2auD0uAYPAx6Zdz/e7jkR2ck5rn9mUiL5yDl8e8/3eGTBg9A5Gbgd3a8V6neMQsKOhHKFIkgpkRafjnlP/YiFzy9GQJQ/ghoHwSfYBxBAfk4+zh87j7SEDFhyLEXTlIcA0KBjFB6acx/0Jn255kFERO6Xn2NBxtmSg6LcyZ5vR9rpjCoNk7PZbJgxY0a1hulJKbFs2TKkp6cjICDgsuFCCDz++OOYMmUKzp49W+J8bDYbxo4di4SEBDzxxBMwGJx78IqUEomJiZg4cSKmTp2K+fPnFxsmBwADBgxA69atceDAgRI/o9zcXNxyyy1YunQpoqOjSz3m1jQNixcvxgMPPFDqtVchBEaPHo2IiAin3lN5BQYG4plnnsErr7xSIwMVq1JqaiqmTJmCZ599tsR1ZrFY8PLLL5f62bRp0wb+/v5VVCXVNVJKaBLIyLVg+/FULN1+Gn/tOo1jydmw2rUL47i5SKrdJGC1azh6NhtHz2Zj1tpjUIRARD1P9GodikExDdCjRQjCGC5HROQyIQTaNQnD5q8ewT3vL8KvGw86fYwtpUTcoTPo8vAk/Pz2KHRuGcV9MFU7hskRERERERERERERERERETkpLi4Ovr6+7i6DiIiIiNxMSomDp85h0EszcOJspmvhD0KgfdMwLHj9f2gaEcCGo9XgbIYZt3+wCja7cwEhX/91ECN7N0XLSD+3rx8hBDo0DsSmdwfjro9WYeW+xKIORkIAfh4GLHzuOlx/VUSxtebm5mL+/PlF26gQAoqiQClnQLjD4YCUsujns88+Q//+/cs9v4o4d+4cFi1adMn3r3Xr1qhf3/nwDiIiIqrbhBBofV1LdPtfZ/wzK67CoQtSFvzHnG6GOd2MxH1JlVPoxVwMKwOAk9viIZ0IVa5OXgGe8Am6PAzZGQYPA+75dhQ+6vcZbBbXQ/IuJqUEJGDOMONMhhln9iZWaH5GLyPqRfo7PX7iviRIzbV1Y/A0IKB+PZem8Qv3xUNz78MnA74sX7CglNi3/CB+fWspbn1joFPnSapexagv78LE6z6p2HqSgMPqwLnjqTh3PLX88ymBEAJNujbC/y1+CJ7+npU+fyIiqj7mrDzYK3hsUJUykzJRv11klc1/9+7dOH78eInDVVXF0KFDiw19K43FYsGPP/6I3NzcYofn5OTgt99+w+jRo4sd7u/vjy+++AJ33XUXHI6Sg4otFgteeOEFzJ49G6+//jquvfZa+Pj4XHbd0eFwIDMzE9u2bcM333yD33//Hfn5+WUeyxuNRnz11Vfo168fbDZbseNIKZGQkIBu3bph3LhxeOCBBxAQEHBJDXa7HadOncKECRMwa9asUt+TEAKhoaF4++23q/w6sxACsbGx+OKLL0oN7rsSSSnx2muvoXnz5hg8ePBl24zFYsETTzyBHTt2lDgPIQQeeOABt1znpiuHlBI5+XbsP52OX7cmYNn2BBw8k4V8m/3CcDcXSFc0KQGHlEhIzcXc9ccxb8NxqIpAZIAX+rWLwJCYBujSPBiBPkYoQrj9/icRUW1Qz8cTP77xP0ycuxZvfr8SVruTD16REmfTc3HdU9Mw/YXbcfs17n+QIdUtDJMjIiIiIiIiIiIiIiIiIiIiIiIicpKUEks27MfY9xYhMy8frmQyKIrAqBs64ssnB8PLZKi6IqmI1e7AyE9XIykjF0DZDXQlgPPZFtzwxh/Y+O5A1C9nwEVlC/QxYslL/TD6s3X4acsJSAChfh74ddwNuLpJYImNj1euXIlz584V/R4QEIDVq1cjODi4XHXs3LkTt9xyS1EnyTVr1iA5ORnh4eHlml95aZqGl156CdnZ2UWvCSHw+OOPQ1XVaq2FiIiIajehCNz58e04uSMBSXvP1rjQtf9yNaxMSokj64+6dN5SHcJahUJnKH+XpkadGmDo24Ox8PnFLoexVaWgxoEweDp3rielxNGNx1xeRmDDejB6GV2aRgiBpl0bY8THwzErdl65PjMpJf78eAUadIjC1UM7ONUBsn6HKAx4+UYsGb+0wmGNVUEIgXaDrsL908c4vd6IiKjmys+21KjjgktICbu16oLupJSYMmUKNK3kh2m0b98ec+fOLfe1sx9++KHYv+dSSnz99dcYOXJksfMWQmDYsGF45JFHMGnSpFKPCaSU2LVrF4YNGwYvLy80b94cLVq0gK+vL+x2OxITE3HixAkkJCQ4FSD3X71798bTTz+NiRMnljitlBI5OTl45ZVX8Pbbb6Nly5aIjo6Gp6cnUlNTsWfPHpw8eRJ2u73M5ev1esyYMcPlAL/y8vX1xRtvvIGHH364Rh57VaX8/HzccccdGDJkCO6//340bdoUNpsNW7Zswaeffopdu3aV+pkEBwfjzjvvZMgHuURKCZtD4nRqLv7efQY/bTqJzUfOIctsuzDczQVSnSYlYHdInDqXg29XHMZ3Kw/BqFPRKtIfg2MaYHBMA7SK9IeHQeW+j4ioFDpVxbiR16JDszCMfncR0rLznLrWLqVEXr4NIyfMx8GEcxj3v2uhUxlcTNWDYXJERERERERERERERERERERERERETnA4NHwwfx3GT/8LNnvJHfP+SwgBT6MOnz02CGNuuhqqwkai1UFKic9/3481+8/CmSC5fycEEtNzcfOEP7H6rQEI8jFVWY2u8DDoMPvJa3DPFzpsPnYWf42/CY2CvUvs5KFpGr744ouiTnJCCAwcOBBt2rQpd8eQvn37Ijo6Gnv27AFQ0Elv3rx5ePLJJ6uts4mUEosWLcKMGTMu6QAYFRXFDn9ERERULiZvE578NRYf3vAZzh07X6ODF4IaBbgUeiU1iVPbE6qwItcJIdCsR1OXDtGLm8d1j/RBdko2/vjw7xoTHNO4S0MIxck3JoFjm064vL01inFhGRcRQqDH6K5I2Hkaa6auL9d2rjk0zHhoNkKaBSOqbWSZx95CCNz4VD+cPZiCLXO31qjvlmpQcfPzN2DA8zdC1TOQmojoSqDT6yCEqFF/bwpJAQi16q5Z5eTk4Oeffy5xuBACDz74IFS1fIEx9913H3744YcSh8fFxSEhIQGNGjUqdriiKPjoo4+Qnp6OOXPmlLmOCgPdduzYgR07drhcb0mEEHjzzTdx8uRJLFiwoMxgu7y8vHLXoNPp8Omnn6J///7Vdr1SCIG7774b7777Lk6ePFkty3S3iz9bm82GRYsWYdGiRZfsC8ra3oQQGD9+PHx9fau0VroySCmRk2/H7lNpWLDhOH7fnoBT53LhkLLGhbgTXUxKgXybhp0n07DzVBomLNqFQC8D+rQJwx09m+Ca6DAE+pigCPA+GxHRfwghcHPXVoib8ihufWUW9p5Idvq80+7Q8Pr0FTh2JhVfPXUrPIz6Kq6WCGCLNCIiIiIiIiIiIiIiIiJyiaIqdfIHAGJiYhAdHY1Jkya5eS0QERERUXXLs9hw78Qf8cq3f7ocJNcgxA9rP3sI99zciUFy1ehQYiZeX7AD5ek/KiGw/3Q6bp6wHDlmW+UXV04GvYLvHuuBuImD0TjEp9QOHUlJSVi/fn3R70IIPPzwwxXqBKKqKu6///6ieUgpMXXqVNjt9nLP0xWapmHOnDkYM2YMHA7HJXV99tln8Pb2rpY6iIiI6MrjF+aLZ/96AlFtI2p0p9mm3Rq7FCSWn52P1Pi0KqyofJp1b1Lhz1lRFAx+7RYMfOkmKDr3n2cJCLTo2czp92Wz2JF08KyLywCa93J+Gf+lKApuf/9WNOvZpFzTA4At14av7piG3NRcp8ZXdSpGfzUCMXd2qhHfLSEEgpsE4uk/HsPAl25mkBwR0RXE09+jSgPbKkbA08+zyua+cuVKpKWVfMxnMBgwePDgcv8t7ty5MyIiIkocbrfbMXPmzFIDFQwGA6ZPn47nnnsOOp2uXHVUBoPBgO+//x5jxoyBUkXX6k0mEyZPnlzha7Hl4eHhgYkTJ9aI467q0rdvX3h4eAAouF4tpYSmaUX/XxohBG688UY88MADdeozI+cVbE8SyRlmLNx4AkPe/RsNH5qPPq/+ji+WHcDx5Bw4NAbJlYcQF/0ogFDEhR9c9G9xPxeG/Wce5AIJaJrEuWwLFm06hbs+XoVGD89Htxd/xUdL9uJIYhZsdq1GBvQSEbmLEAKNwwOw/osHMbRXa5eOHaWUmPnnTvR/9lukZuVVYZVEBdx3xk1EREREREREREREREREVMvExcXxacREREREdYyUEucycjHs1Vn4Z3+CSw3nhRC4oVNTzHrlTgT6erIzUjWyOzQ8NGUjzNbiQ86EAJqF+sHDoGBPQnoJgXMC24+n4s6PV2HRc9fDZKgZTS6NehXGMgIPpJSYPXs2LBZL0WsRERHo0KFDhZYthMCwYcPw/PPPF8378OHD2Lt3Lzp27FiheZdGSonTp0/j5Zdfxpw5cy4JkhNC4NFHH8WQIUP4HSMiIqIK8QvzxbMrnsScJxZgy7ytkJr7O80WHt8ENKiHax7shd739nTpmCc1Pg32/OoJ/nWWUATCo8MqZV6KquCWcTchvE0YZsfOR1662S2dnYUQgADqd4hyeprslGzkpZtdXRAadqzvYnWX0hv1eHDOvXi394dIjy/pXKhkUkqkxafjm1HT8djPj0BnLPs8SWfQYezUkQhsWA/LP14BaS872KOyCQjovfS45uHeGPjiTTB6GXj+QER0hTH5muDpZ0JOas3rnK8oAoENAqpk3pqm4csvvyz1b2uXLl0QGhpa7mWYTCYMGzasxOVIKTF9+nQ8//zzMBqNJc5Hr9fj3XffRZ8+ffDwww/jzJkzlXZMIIRwOhzOaDRi2rRp6NatG1544QVkZWVVSh1CCDRp0gQ//PADunfv7pZjDSEEhgwZgjZt2mDfvn11Igjo+eefR2ZmJkaNGoX8/HynpxNCoF27dpg7dy70en0VVki1jZQSmpQ4nZqHJXGnMGftCew6dR4Wu1auhxddSQp2axKAKNjvCsCoK7hn5e+lh0mvQ5CPCQHeBnh76BER4Akvow6eRh3C63nCQ6/CoFeg1ymo52WEUa9CpwgooiBITrkwz3+XVUAW5vVJCU0CmpTQtIJ/7Q4NaTkW5NscsNk1WGwOnM0wI9tsQ77VgaSMPKTnWpGbb8PZDDPMVjsyc60wWx2w2DXYNXkhfBIQQkLKuneeKCWQb9Ow7fh5bDtxDuNmb0WjYB/c3qMRhndrjOj6fjDqVZ5DExEB8PE0Yd74EXh31hpMmLXS6QdRSimxcV8Cev3fFPz+3lg0Dq/H/SpVmZrRsomIiIiIiIiIiIiIiIiIiIiIiIiohpFSYv/JZAx66QecTM6EK71EVEXB03f0wlv33gBDGcFfVLmklPhlazw2HEoucZXV8zLij9f6w9/TgN4v/479Z9IBXN5YV0rgj52n8eCUDfgutjd0qnMdEt3NZrNh2rRpRZ0FhRC44447Su3M6ayIiAh07doVa9euBVDweU+ePBlTp051usGzpmmldmSUUsJmsyE9PR1bt27F9OnTsWzZMuTn518ynRACd999Nz788EOnO4sSERERlcbkbcTYb0ai87COmP/cIqSeTHNbOJnJx4irboxGnwd6oXGXhtAZdC53MDu1LQE1ra+7V4AnfIN9Km1+QhG4ekgHNO3aBAueXYQdS3ZDOqoprEwIKIpAVPtIDHihP0Kbhzg96Zn9idAcrtVo8DQgoH49V6u8jE+QNx5d8CA+6PsJLDlWl6eXUuLQmqP48aXFuPOD2yGUsrdLVa9iyPiBaHNDNGY+MhcpR89VyzoSAtAZ9bh6aHsMem0AghoGsqMmEdEVSm/UI6JNBA6vPeruUi7jE+wFnxDvKpn32bNnsW7duhKHCyHwyCOPVOjamRACY8aMwaRJk0r8+33q1Cns3r0bMTExpc5LURQMGDAA+/btw+TJk/HFF18gKSmpXMcFQhSEGbVu3RrPPfccbrjhBqenVVUVDz74IAYOHIgJEyZg5syZyMvLK3cdoaGheOKJJ/DYY4/B09O9D5YxGAx4//33MWjQoDoRJufh4YF+/fqhXr16uPvuu5GcnFzm+y7cDmfNmgU/P79qqpRqMiklHJrEibO5WLTpFOb/cwT7EzIuBI25u7rqIURBYJsiBPSqgLdJj0BvI4L9TGgS6oOoQC80CPJGVKAXAn2MCPEzwcOgg7dJD50qoFeVooC5y+dd/fvE4vYDmix43a4VhNCZLXbkWe04n2VBZp4VJ8/lIDnDjBPJ2Yg/n4uk9DykZJmRbbbDYnPAITUIiCt7m5ACDilxLDkLExfvxge/7EGEnwdu7dYQI/s0RbuGATBVY7CcpknsPJmKo0lZ6NwsCI1DfHhOT0Rup1NVvDL6OkQ3CsK9E39Cdp5z11ellDiUcB49/+8rLJ5wN7q2bsB9GlUJhskRERERERERERERERERERERERER/YeUEn/FHcaICfORkWNxOkhOCAEvDwO+fW4obrumLRQ2/qx2+TYHXvwhDppW/DpTFIGvHuiJRsHeEEJg+Ws3os+rv+PkuWxIWXyg3Ox1x1DP24iPx3aF6kRQgrtt374dx44dK/pdURTcc889ldIYWVEUPPDAA1i3bh2kLAjp+Omnn/DRRx/B19e3zOnT09MxevToYse12+3IzMxEYmIiTp8+jdTUVNhstss6/Qgh4OHhgbfeeguPP/44dDo2hyUiIqLKoygK2g5og5bXtcDmeXH465OVOHf8PCCL74xcOQSEAngHeaN135boemdnNO3WGCZfU7mP4aSUOLzuqEuh2NUhrGUodIbKPX4TQsA/3A8PzLwH8btO47e3l+HAioOwWxxVss6EIuAb6oNOwzqg9709EdYiFIrO+YAWKSWObjwOuBj1F9iwHoxeFQ+IFkIgqm0ERn55J2bcNxuaQ3N5HlJKrPl6Axq0b4Duo7o4tZ0KIdCsRxO8svkFbJq1Gcs/+RupJ9MLPoVKXk9CCPiF+6LHqK7o80Av+Ef4sXMmEdGVTgBd7uiMI+uO1agALSEEWlzfvNKPfwqtWbMGTZo0KXG40WjEjTfeWOHlXHXVVejVqxfOnTtX4ji///47OnfuXObfXCEEfH198cILL+Dxxx/HunXrMHv2bKxZswZnz56FzWYrGvfih2UU/uvh4YGmTZti0KBBGD58OKKjo6HTuR68LIRAZGQkJk+ejDfeeAOLFy/G3LlzsXPnTmRnZxct+781AAXnDMHBwejZsyfGjBmD6667Dl5eXuU+3vDw8ECrVq1KHF6/fn2X5te/f3/ceuutOHDgQKnj+fhULJRHCIFmzZrB39+/xHHCwsLKPX8ACA8PR+vWrUscXhjed/3112PPnj147733MHPmzKJtVUpZ9B5VVUWbNm3w+uuvY+DAgbyuXMdJKaFJifjzuZi/7iRmrzuKQ0kZcFyhAXIXf9X1qgJfDwMiAzzRJMwbLSL8EB3pj6bhvgjx80A9LwO8jAUhcYoiih7FVJvOqYqrVRUAIKBTAehVeJv0AICGFwXeX3wMoUnA7tCQb3MgM9eKlKx8nErJxuGkLBxKzMTRpCzEn89BarYFFrt20d+Nqnxn1UcCkJrE6fQ8TPrjACb9cQBh/h4Y2rUhRl/THO0aBsCoV6psu5BSYsKinXhr4U44NAkPg4qfnu+L/u0ja9W2SERXJiEEhvVpi2aRQRjyyiwkpGQ6fR6akpGHG5+bge/HDceQnq25T6NKJ2RNuipCREREbpeVlQU/Pz+8CKDiTQ1qlwh3F+BGzXxM7i7BLbzrebm7BLfw8a+b7xsAVKPe3SW4hTNP/bwSBbdr5u4S3Eb1UN1dgltIF5/Ue6WQJXSEqwvq6jqvcY8MpyqnORzuLoGqmbS73lCfqDaqa/u3bJsdTResR2ZmplMd62uqwuuHa5sEw7sCT62ujXI0DX2On0OLFi2gqipiY2MRGxvr7rKIiIiIqIpoUmLqks14evJS5FvtTk8nINAg1A8/T7gb7ZqGs9GnG0gpMWfdMYz+Ym2xnTWEAIbENMTCZ68vCoWTUuLo2Sz0fPk3pGZbSuzkoSgCb9/VCc8NrdkhgVJKjB07FjNnzixqsNyiRQvs3bsXen3l3DNNTU1Fo0aNkJOTA6CgsfTChQtx2223XTburFmzMGrUqEpZrhACer0et9xyC95//300a9bM5e/ZvHnzMGLEiKLfg4KCkJCQAJOpbradICIiotJJKeGwOZCw6ww2/rAJe//cj4zETEi7hCznDfyCvssCil6Bb4gPGnSsj+i+rdCidzMENwmCzuh6EEZxNIeGD/p+iuQjKRWeV2W6/tFrcMtLN1Xp+ZLUJDLPZmLb4p3Y+uN2nN6dCJvZBsD1UICi4AmTitCmIWh/y1XoMKQdIlqHl3tdSSnx5W1f48Tmky5NFzO8E+765PZK++ykJvHLm79h7Tcbyj0Pg4cB//fzw4i6yrWW2FJK2K12HN98Euu/3YgDqw4hJzUX0ODyd6toHekVBDYMQJsbWiPmjk6o3z6q0r5PRERUO2SlZOOV6DdgybW6u5QiQhF49u8n0Kx7yYFvFaFpWpmhBYpS8aCXwodKlLYsIQSUcrYlkVLCZrMhPT0dSUlJSEpKwunTp2E2mwEAvr6+iIyMRFRUFMLDw+Hj41Mp7+u/NUgpkZeXh+TkZMTHxyMlJQUpKSnQNA16vR7h4eEIDQ1Fw4YNERgYCKPRWCk1SCmhaSW3GyzPZ+vMtgFUbPsoq26gYtsFUPb7+G/9UkqYzWYcOXIEhw4dwtmzZ6HT6dC4cWNER0cjMjISqqryGLEO0zSJM2l5+HnLKcxYdRh749Nhv4IC5IQAIAvuaXkb9YgK8kR0lD/aNQpAu4YBaBLqgxBfD/h46KHXKSjscsbvhPOKQuMA2B0SeRY7UrPzcSIlBwdOZ2DXyVTsiU9HwvlcpOVYYLsQ4H6lbGMQgCIEIvw9MLxHE9xzfXO0jPCDThWVuh3l5NsQ9eA8ZOX9GzLbrmE9bJ04BDq1brVdJaKaS0qJlIxc3PrS99hyKNGlYHODXsXHj96Mhwd3h1JH+4BT1WCYHBEREV2CYXJ1E8Pk6haGydU9DJOrexgmV7cwTK4OqqNvuy6ra2FLxDA5qjvq2v6NYXK1X2GYXG1fh0RERERUNrtDw2vf/YUP5q2Do4xOUBcTQqBPu0ZYMP5/CPL3ZKcLN7HaHWj/9GIcSswqdrivhx57Px2GqMBL75tKKbHteCpueOMPZOaV3NlUVQS+vL8bHryh1WXrWEqJlKx8BHgZode575wpIyMDjRo1QmZmJoCCbfPdd9/F888/X3mhE1JiyJAh+PXXX4te6927N1avXn1Zx8CKhskVBsg1bdoUd955J0aPHo2GDRuWuwMiw+SIiIiovAqD5TLPZuHMnkSc2pGAM/sTkR6fjqxz2XBY7LBZ7XDYNEAAepMeOp0CnUkP31Bf+IX5IrhJECKiwxHeMhQBDQLg6e8BVV81QQZSStgt9hrXWVnVK1B11dO+qTAQxJyZj+QjKYjfmYCEXaeRfPQcMpMyYc2xwG7XLgTNATqTDnq9CpOPCb7hfghrHoL67aNQv0MUgpsEwtPPE0KpeOfowiA16eKtYVWnQNVX7mcnNQmbxfkQ9eIoOgW6CtQlpYQt3460hHTE70jAya0nkXjgLNLPZCA/Kx92mwP2fBukBPQeBd8rvUkP33A/BDcOQlTbCDToEIXwVmHwCvSColZusAwREdUeUkrMeGA2Ns+Jc6kTf1URQiCqfSTGrX2m0v+GExGR86SUyMi1YtmOBHz952FsPpICm12r9c3yhSj4W+Nt0qF+oBfaNghATNNgXN00AI2CfRDka4LJoBQ9IInnSdVDyoKIdJtdQ2aeFfHncrE3Pg1bj5/H9uOpOJqUhfRcKzStvI8pqDkKt8GmoT4Ye11zjOjVBPWDvIse6FUR2WYbIu+fi5yLrlnoVYEDn9+GJqFsv0lENUtevhUPfPgT5q3c49K5qKooeOnua/Hq6OsZlEmVRufuAoiIiIiIiIiIiIiIiIiIiIiIiIhqArPFhgc/+glz/t7tUgNPRRG4f0BnfPrYIJgMbJbnTv8cTsGRs9nFDhMCePKWqxAZ4FnMMIFOTQIx76nrMOyDv2G2Fh+C7tAknvxuM8LreWJQ5waXdLz5Ne407vh4BUb2boJpj/Z2W6ecjRs3Ijg4GMHBwQAAVVVx1113VWo9Qgg89thjOHDgQNFr58+fR0pKCsLCwi4Z18fHB82alf0gKEVRoKoqjEYjwsPDERUVhTZt2qBDhw5o2bIlgoKCoKoVD1rx9va+pB5/f392oCIiIiKnCCGgM+gQ2CAAgQ0C0HZAm4IBEtA0DVKT0BwSmsMBIURBYJooeBiooiiA+Hc+1VWv3lQ3H8BaSIiC4Devep5o0qURmnRpVHSuJzUJTZOAJuGwOyBlQVibUBUoiih6iGtVrC8hBPQ15OG4QhEweLi3FiEKaghrEYKwFiGIueNqAAXrqPCncB3p9IXfq4L1BMFABCIi+pcQAoPHD8COX3bBkmNxdzkQisBt7w5hkBwRkRtIKZFvc2DT4XOYvPwAlu84jdwaGLjujMJTHkUI1PM0oGWkP7q3DEbPVqFoU78ewup5wMOgQhEVDz+nihNCQAAw6lWE+HkgxM8DnZsFYcx1zSEBWGwOpOVYcCQxC3HHzmP9gbPYdTINZzPMsDkKUudry3YqZcF37UhSFl6esxWvz9+Ozk2D8cANLTC4cwPU8zaWe5v0MukQ0zwYq/YmFb1m1yTmrD2Bl29vx22diGoUT5MB34+7A80iAvDO7LVOP7jSoWmYMHMVElIyMOXpW2HQs70RVZyQNSFen4iIiGqMrKws+Pn54UUARncXU80i3F2AGzXzqZtP1/au51X2SFcgH/+6+b4BQK0hja+qm6iEp3nURsHtyu6QcqVSPepmgwPpqJuXOKRWN983UHfXea1//BS5THMU32mVrlzS7uLj54lqqbq2f8u22dF0wXpkZmbC17f2PhWx8Prh2ibB8Fbq1hPQcjQNfY6fq/XrkIiIiIhKlp6dh+Gvz8GqHSdcCpIz6FVMfOgmxA7tDrWOHSfXNFJK3DZxBX6Jiy/2Umo9LwOOTh6Oel4lt4iQUmLhxpO4+/PVsJdyHdrToOLXcf1x7VVhEEJASokXZ8fhg5/3oluLYKx/eyAUN3Vi0DQN2n8aJ1dGCNt/SSnh+M/5fXHLKa6ekhSGfRTOoyo6gvy3HiEKwl3Y6YSIiIiIiIiIqPaTUmLFF6vx44s/u3Sdt7IJIdDlrk64Z9qoOtuen4jIHRyaxPHkbExfeQiz1hzDmfQ8ALUnnAsAICQUocDHqEPLSH/0iQ5F7+gwtG1QD6H+HjDqVQgwWPtKUHisYndIpOdacCQpCxsPJmPV3rPYfSoNKVlmODQNUtaudS2EhLfJgJs7RCH25mjENAuCUe/6vbg5645h1OdrLvn+tojwxZ6Ph0Gv4315Iqp5NCkxc/k2PPrJrzBbbU5PJ4TAoG6tMOvVO+HtYajCCqkuYCQhEREREREREREREREREblEVRSoat1qjFMY2x0TEwNVVREbG4vY2Fi31kRERERElUNKicTzmRjw4g/YeyLZ+Q6GQsDfy4i5r41A/87N2GGjBkjLseCv3YklPpPj/r4t4e9ZesNbIQSG92iEpIyuePb7zXCU8FCXPKsDwz9agRWv34x2DQMAANl5dgAo6sTjLoqiQKmGYEMhBHS6spuhVlc9zqpp9RARERERERERUeURQuC6R/rg4Joj2LN0r5tqAEJahGDEp8MZJEdEVA2klMgy2/D7tgR8/vt+7DhxHnZN1ooAucLbi3pVQWSAJ7q3CEG/9pHo1jwEUUGe8DToIASD465UhetVrxMI8fNAiJ8HerQMwbND2sJq15CcacauE2lYuTcJa/Yl4khSNvKsBfcja/L2LaVAttmGhZtO4MfNJxEV4IUH+jXHmOtaICLA0+kHcvVvHwkPvYo8678Ptzp2NhtHkjIRXb9eVZVPRFRuihAYfWMnNAyth+Gvz0VattmpNkhSSvy66SAGvDADS94ZBX9vj2qolq5UDJMjIiIiIiIiIiIiIiIiInJSXFwcfH193V0GEREREVUSKSUOJZxH/2e/xZnz2U4HyQkh0CDUD8vevwct6wexA0cN8feupKIOFP9l0Cl49ObWTq0rIQQeG9Aa6dkWTPhpJ7QSAuXSc6246a3l+OfdQWgY7I18W0FHBqOOQWVERERERERERETuoupV3PfdKHx44xc4s/uM8w8QqQRCCHgHeeOxxQ/Cw5cBAEREVcmhaTh4JhOTlh3A/I3HkZFrrdEBW4WEAIx6HZqE+OD6tuG4+epIdGwchEBvI3Sq4H3HOq5w/Rv1KhoEeaNBkDcGdq4PTQLZZisOJ2Zh1b4k/LH9NHaeTEWW2QZIlPiwLXeSsuB+fPz5HLw2fwfeXrQLvVqF49khV6FPdGjBA7pK2d4DfIy4rm04ft92uug1h9QwZ/1xvHXX1fyuEFGNJITANR2aYM1nD2DQSz/gxNkMpxJApZTYsPcU+jw2Fcs/vBfhgT5VXyxdkRgmR0RERERERERERERERERERERERHVOYUPMYa/OwvlMM5xtYi+EQEzLKCx5ZxSC/b3YSL2GkFJi3oZjJbbB7dY8BPUDvZyenyIEXhneHsmZZkz9+2Cx85USSM7Kx01vLcef429CRq4FABDm71met0BERERERERERESVxMPPA0/9HotPB03C6Z3VFSgn4B/ph8d/eQRBjYKqYXlERHWPlBI5+Tb8ti0Bn/y2DzuOp0KTskaHyAkhYdCpaBrqi37tIzDw6gbo0DgA9bwMUBSGx1HZhBBQBeDvZUSX5sGIaRaE54a0RV6+HUfOZuHvXYn4fVsCdp5KRbbZViO/D1IC+TYNf+85gxV7zyAq0AsP9muJsdc3R0Q9z2K/B4oQeKBfSyzdfvrf9yQF5q07jteGd4BBp1bvmyAicpIQAq0bhmD9Fw9h8Es/YPuRJKfOSaWU2HsyBdc+ORV/fXgf6of48TiBXMYwOSIiIiIiIiIiIiIiIiIiIiIiIqpTpJRYtvkQ7nxjHvIsNrgSJHd7n2hMf3E4PE2Gqi2SXGK2OrDhYHKxwwSAh/q3gqK41shWpyr4/L5uSMnMx89xJ4vveCGBI2ez0Pf1Zcg22wAA3h5smklERERERERERORu3oFeePbPx/HDI3Ox/aedkFrVJasIIdAopgEenns//MJ92eGfiKiSaVIi/lwOJi8/gBkrjyA1x1IjA7MAQIiCEKyoIC9c3yYct3ZphK4tghDobWR4HFUKIQQEAG8PPTo2DkSHRgF4ZshVyM23Y19COpZuT8CvW+Nx6EwmLHatxn1XpAQSzufitfnb8c5Pu3FTxwg8O6QdOjcJgk699DtyTXQ4fDz0yMqzFb128nwO9sdnokOTAHeUT0TkFCEEwgN9sfKTB3D7a7Pw9/bjToacSxw5k4YesZOx/IP7EN0ohMcO5BK2WCIiIiIiIiIiIiIiIiIilwhFQLgYwlDbCVnwfmNiYqCqKmJjYxEbG+vmqoiIiIioPKSUmPnnDjz08WJYrA6np1MUgadu74V3HugPPZ9yXuPEn89BWq612GF6nYJr24SVa756nYKZT/TB4PcsWLU3qdjOFlICx89mF0USmvTcPoiIiIiIiIiIiGoCk7cJ988Yg039W+PHcb8gLzUP0smHizhFCOhNOtz4TD/c9Ew/6E36yps3EVEdJ6WEzaFh0+FzeG/xLqzckwSrQ3P2GVHVSgjAx0OPrs2CMbRrY9zQPhz1g7ygVxUGwFCVKwyX8/HQo1uLEHRtHozxd3REapYFm46cw6J/TmDl3iQkZZghpawx4XJSAmarHT9viccvcQloFeGHZ4e0xW3dGsHbpIMQAr6eevS9KgKLt5y6aDoN3689gvaNu/D7RUQ1no+nEUveGYOHPl6MmX/udC5QTkokpeXi+qemYfmH96B903Du78hpDJMjIiIiIiIiIiIiIiIiInJSXFwcfH193V0GEREREZWTpkl8vmg9Xpi6HDa75tQ0QgjoVQUfPjoAjw7pBqWOBSvXFnFHUiG14hvdNgn1QbCfqdzz9jTqsOi5vrj2td+x+1R68YFyF/2/v6eRDXmJiIiIiIiIiIhqCEVV0P3urmh701X4/b3l2PjDP7Dm2pzrxF8CIQQUvYK2N0Vj2DtDENIkmNcEiYgqiZQSOfl2LNxwEh/9thuHErNqVABWIUURqB/ohZs7RmJ49ybo1DQQ3h56KPx7QG4mhIAqBEL8PTA4pgEGda4Pi03DsbNZ+HVrPBb+cwL7EjJgc2g14nslZcH3fv/pDNw3eR1enLkF9/VriUf6t0ZkoCfu7dscP8edKqpVSoFFG0/g3ZGd+ZAvIqoVjAYdpj13GyKDfPH+3LXQSmjbcjEpJc5n5aHf099i6ftjEdMqiuec5BSGyREREREREREREREREREREREREdEVz6FpePP7FXh71mqnGmYW8jTpMeflOzGwRys2zKyhpJTYdCQZJa3Va9qEQa1gCKCfpwF/vHoT+rz8O44mZ5XasWLHyfNwaBpURanQMomIiIiIiIiIiKhyCCHgE+yNOz8chlte7I/Nc7di3fSNSDl2DppdQ4kXFy+eBwSgAPWi6iHmjqvR576eCGwQAMEHkBARVQopJZIzzZj8x0F8/edBnM/OrxFBV4WEAPSqgujIerijZyPc2rUhmoT6QK8qvIdINZoQAiaDijYN6iG6vj+eu7UdUjLNWL03CT+sOYqNh1KQk2+rEd83KYGULAveW7wbn/y2D4M7NcD17SJg0Cmw2P59WFxihhl7TqUhplmwG6slInKeTlXw1r39EeBjwkvT/nLqAZhSSqRlm3HDM99i4ev/ww0xzXnMQWVimBwRERERERERERERERERERERERFd0ex2B16athwfL9zgdJCcEAKBvh5Y8vYodI1uwAaZNZgEsOtkWrHDhAB6tw6r0PqTUkIIgVA/D/z+8o3o8+qvSM4suQPT2TQzbHYNqoFhckRERERERERERDVJQaicD/o+di2ue7QPMhIzcXTjcRxacwRJ+5OQcTYT+Tn50GwaFJ0Kg5cBPoHeCI8OQ7NuTdCiTzMENwmCqld5zZiIqJJoUuJoUhYm/rwb8zacgNlqrxGhVgAAIeGh1+PqxoG4+5qmuKVTfYTX84QiwL8DVCsJIaAKILyeJ0b0boo7ezVBVq4Nm46kYMaqI/h7dyLScy1u/w5KCeRbHVi46QR+3HwS8j8FSU3i+9VH0blpEL+LRFRrKIrA03f0QUg9Xzz40U+wWB1OTZdttmLYa7Ox4I3/4eYuLbjfo1IxTI6IiIiIiIiIiIiIiIiIXKKoChS1boUiFL7bmJgYqKqK2NhYxMbGurUmIiIiInKO1ebAwx//hO//2AFn27wLIdAo1B+/vz8GLesHsyFmDedwSJxOyy1xeHSUf7nmq0mJnzafwuEzGTAZVIT5e8KkV3F1kyAs3Z4A4PLtQgjg/wZEw2Rg80wiIiIiIiIiIqKaSggBVacisEEAAhsEoMudnQAJaA4NDrsDmkNCUURBaJwqIITgdWIiokrm0DRsPZaKt3/ciT93nYHV7kBx916qmxCASa9Dt+ZBuKdvC/RvH4kgHxMEA+ToCqQIAX9vA27qGIUbO0QiJ9+O7cfP44fVR/Dr1gSk5lgAwG3hclLisiC5wtcXbzqJiaNi4GnkfVkiqj2EELj7hg6o523E/yYsQG6+rdj93H+ZrXYMHz8HC8aPwIBuLXlMQiXiX0UiIiIiIiIiIiIiIiIiIifFxcXB19fX3WUQERERkZMsNjvGvvcjFqza41KQXJvGwVg+8V6EBfiwAWYtYHVoyMi1FjtMpwiE1fMo13xTMs0Y+elq2OxaMUNL2C4kEB7gWa7lkWvOnDmDJUuWODWuqqowGo2IjIxEo0aNEBERAQ8PD36/iYiIiIiIiIgIwIVwIAGoigpVr7q7HCKiK5rNrmHjoWS8MncbNh0+B4dWeBfPfdfsBQCDTkH3liG4tzBAztcEhfcRqA4RQsDHQ49r2oSjT3QY8iwObD6agml/H8KfOxORnmtxW6hccc5mmrHhYDL6tYvgPT8iqlWEELile2ssfW8sBr30A7LyLGUGykkpYbbYMPyNOZj76l0Y3KM1931ULIbJERERERERERERERERERERERER0RXHYrVj9DsL8OPafU49xRcoaLDZu21D/Pz2KPh7ly+AjKqf2WJHvtVR7DC9qsDDUL6mkoE+Joy9thk2HEyG1a7BYteQk2+D3SHh0CRsdgfs2r/blgQAKeHBzqbV4tChQ4iNjXX6+w0UfMeFEAgICED//v3xxBNPoHPnzlAUpQorJSIiIiIiIiIiIiKq26SUsDk0LN95Bq/N24498enQNDenUgkJnaKgXcMA3Nu3BYZ0boDwep5QFAazEAkh4GXS4fqrInBdm3Dk5Nuw7kAypiw/iFV7k5Bntbs3WE4AUgJ3f7YGL9/WHmOuaw5fDz2DlYio1hBCoGfbhvjrw3sw6KWZSM7IgTNPycy32DHirXmY88pdGNKTgXJ0OSFdaUVDREREV7ysrCz4+fnhRQBGdxdTzSLcXYAbNfMxubsEt/Cu5+XuEtzCx79uvm8AUI16d5fgFqKO3sQIbtfM3SW4jepRNzsoSUfdvMQh3X0D1Y3q6jp35sIwXVk0R/GdYOnKJe2au0sgqhZ1bf+WbbOj6YL1yMzMhK+vr7vLKbfC64ebWkfAW61bHe1zHBq6HUis9euQiIiIqK7IMVtw1+uzsSzumEtBcjd3aYH540fAy8NQxRVSZUpKy0ODh+fDUcw182BfE+K/vhPGcga8SSmhyYJ/JQC7Q4OmSWhSIs/iwI+bTiAr146BnesjO98Kq13D1Y0D4evJbaiqrVy5Ev369XMpTO5iQggoioLbb78dU6ZMgb+/f+UWSERERERERERERERUxxWGyC3bfhqvzN2G/aczLrzunnqEKLg/0DDIC2OubY7/9W6CxqE+UC48jIaISielRFqOBb9uS8A3fx7C1mPnYNOkW/u5CAEEeBvx+IDWiL0pGvW8jfw+E1GtIaXEkTOp6PvUNJw5n+X0dCajDrNfvhO39ormPo8uUb7HbRIRERERERERERERERFRnaWoCpQ6FiZX+G5jYmKgqipiY2MRGxvr1pqIiIiIqHjZeRYMefkHrNl1wumOKEIIjOrfAV8/MxRGPZvV1TaOUla0ThGoSLtZIQRUAQAFM9FfdC7k4wE8emProvHIvYQQMBqNUNXLgwOllLBYLNA0reh3KSUcDgcWLFiAvXv3YvXq1QgKCqrusomIiIiIiIiIiIiIrjiFIXK/b0vAK3O34+CZDLcFyAEFYVN+HgYM6dIAj9zYGu0bBcCgU3h/h8hFQggE+pgw5ppmGNWnGeLP5eD7NUfw/aqjSEjNvXAPrnprkhJIzbbg9QU78dnvB/D4gGj834Bo1PMy8DtORDWeEALNIwOx+rMH0PfpaUhIyXLqgXr5FjtGvj0fc165C0N6tub+joqw1RsRERERERERERERERERkZPi4uLg6+vr7jKIiIiIqATZeRYMGvc91u055XQjdUURuH9AZ3zxxGDodZeHUFHNV1qTWImq7a1wpTbILa5xck1/ryaTCdu2bUNkZORlw6SUyM7Oxp49e/Dll19i+fLlcDgcRcP279+Pu+66C3/88Qd0OjatJSIiIiIiIiIiIiIqL6vdgeU7zuDlOVux73QGALglSE4IQFUErm4UiCcGtsGATvXh66Gv8fc7iGqDwgdyNQ71wfjhHfHi0PbYeuwcvvjtAJbtTECuxe6WULm0HAveWLgDXyzdjycHReOxAW3gY+L3nohqNiEEmoQHYPWnD6D/09Nw7GymU4FyFqsD/5swD3NfvQuDezBQjgqwxQsRERERERERERERERERERERERHVetl5Fgwc9z3W7znlVKNKoCBI7uW7r8Vro/tCVZUqrpCqiqIIlNQm1uaQ0LTqredKsGfPHjz99NNFv0+ePBktWrRwY0VlE0IgMjKyxABwPz8/REVF4cYbb8Tvv/+OUaNGITMzE0BBoNzKlSvx22+/4dZbb63GqomIiIiIiIiIiIiIrgx2h4Y/d53BuNlbsTc+3S0BckBBiFywrxGjr2mOB25oiSahvlAVhqsQVRUhBEx6Fb1ahaFny1CkZluwaNNJfLF0Pw4mFgQiVef+QEogNceC1+btwKRlB/Di0Pa4t28LeBl1DFoiohpLCIFGYQFYP+lR9HvmW+w7mVJm2ycpJfItdox4ax7mvzYCA7u34n6OwNZvREREREREREREREREREREREREVKtl51lwy4szXAqSUxUFb95zA8aP6ccguVrOpFehK6ETUK7Fjnybo5orqv0yMjKwcuVKrFixAitXrkRWVpa7S6o0iqJg4MCBmDFjBlRVLXpdSokPP/wQGtMHiYiIiIiIiIiIiIic5tAkNhxMxrWvLcXg9/7GnlNuCJITgF4V6NkqBAufuR5HJ92B90fFoHm4H4PkiKqREAJBviY8eENL7PjwVmx6ZyBG9GwCb5MOQlTvjkFK4GxGPp6asRnRjy/CtBWHkG+1O92egIjIHULqeWPlx/ejbeNQp4Ph8i123PXmPCzbfJj7OGKYHBERERERERERERERERG5RlHVOvkDADExMYiOjsakSZPcvBaIiIiIqFBhkNyGvfFON4rUqwree7A/XvzfNVDYgaTWM+lVmAxqscNsdg3ZZls1V0Q1nRACgwYNQvfu3S95ffv27UhLSytxOinlJT9ERERERERERERERHWVJiX2xqfjprf+wLWvLcU/h1OgadV77VwIwM/DgIdvaIVdHw/D6jdvwbBujeBt0jsdwEJElU8IAb1OQedmwZj5xDU4Omk4PhzdFY1DvFHdX00pgdNpeXj4643o8OzP+GnzSdjsfLgUEdVcQf5e+Pvj+9C+SZjTxzNmiw13vDEHf209yrYMdZzO3QUQEREREREREREREREREdUWcXFx8PX1dXcZRERERHRBjtmCQeO+dy1ITqdg6jNDMfrGq9mJ5Aqh1ykI9DEhI/fy0DiHJpGUnof6QV5uqIxqMlVVcd9992HDhg1F+4/8/HwkJCQgKCjosvFPnDiBjz/+uGjcbt26YeTIkdyPEBERERERERERVbLCa3BSSmiaBk3TIKWEqqoQQkBRFACo8mtzhXUULt9ut19SgxCC1wepTpJSIv58Ll6cvQU//XMKdk2iOjNLhCj4/jcP98HTA6/C7d0bw9/LwO8jUQ0lhECInweeHNgGj97UGusOJOO9n3Zh3YGzsDmqb+chJXA4MQt3fLQKnZoE4cMxXdCzVShUPnyOiGqgID8v/PXxfej/zLfYeeysU22i8vJtuG38bPz69ihc06EJj43qKIbJERERERERERERERERERERERERUa2TY7ZgyMszsW7PKaeD5Ax6FdOeG4aR/Tqw0eQVRFUEGod449jZ7MuGCQHsOJ6GLs2D3VAZ1XTt27e/7LXTp0+jY8eOl72enJyMSZMmFe1vcnNzMXLkyCqvkYiIiIiIiIiIqLJJKbFhwwbk5uaWOW7Tpk3RtGnTagluy87Oxp49e7B+/XrExcXhxIkTyMrKgtlsht1uh6enJzw9PdGwYUN06NABffr0QceOHREUFFQpwW6F4XVJSUn4559/sGbNGuzfvx9JSUkwm83Izc2Fh4cHTCYTAgIC0LJlS/Ts2RM9e/ZEs2bNYDBULMzKZrNhzZo1cDgcpY4XGRmJNm3aOL0sKSU2bdqErKysEsfp2bMnvL29i353OBxYs2YNbLbLH+JSHXr16gUvLz4kpqaRUiI9x4J3F+/GV38chNlmr9YQOQhArwhc0yYc44a1R89WIdCrCu/5EdUSQggY9Sr6tYvA9VeF40hSFj75bQ/mrT+B7Hxbte1PpAS2HT+Pvq8vww3tI/HhqC5oXd+P+xIiqnECfT3x50cuBsqZrbjttTn4++P70KFZOPdtdRDD5IiIiIiIiIiIiIiIiIiIiIiIiKhWMVtsGPbqTKzeecKlILnpL9yOu65vx8aSV6C2DQPx9+6ky16XElh3MAkP9m/B9U6X8fX1hRCiaD8ipYTVanVzVURERERERERERFUrOTkZN9xwA/Lz88sct3Pnzti0aRNUVa2SWmw2GzZt2oRPP/0Uf//9N7Kzs8u87r9v3z4sXboU7777LkwmEzp16oQHH3wQgwcPLrrm5wopJfLy8rBw4UJ8+eWX2L17N+x2e5l1bN68GTNnzoQQAlFRURgzZgweeughRERElOt6dG5uLgYPHgyz2VzqeKGhoTh48CD8/f2dmq+UEo899hi2bdtW7HAhBPbt24fWrVsXvWa1WnHbbbchIyPD2fIrjaIoOHjwIJo3b17ty6biSSmRb3Vg+qrDeH3+DqTmWKo1RE4IwEOvw529GuP5IW3RPNwPisJ7PkS1maIItIz0w1cP9sSEEZ3x2dJ9+PDnPbDYtWpZvpSAQ0os33kaq/cmYtQ1zfDmXZ0Q4mfiPWUiqlECfT3x54f34YZnvsWu42UHykkAGbn5uPG577Dyk/vRplEo92t1jOLuAoiIiIiIiIiIiIiIiIiodhGqUid/ACAmJgbR0dGYNGmSm9cCERERUd1lsdkx9r0fsWK7a0Fy348bziC5K5QQAr1ahaCkNbvhUApsjuK3FSklNCmd3pboypKXl3fJuhdCQK/Xu7EiIiIiIiIiIiKqCaSUkLJ6wkyqm5QSCxcuhMVicWr8Xbt24cSJE1VSx+bNm9GjRw9ce+21WLx4MbKysly6ViulhNlsxvr16zFmzBg0bdoUM2bMcGkeDocDCxcuRHR0NO69915s27YNNpvN6XlIKaFpGuLj4zFhwgS0aNECr776KnJzc52uwVUpKSn45JNPeF2bqoXDoeGPHWfQ8bmf8di0TTifXX1BckIAoX4mvHp7RxydPBzTHumFlpH+DJIjukJIKZFvc+BQYiaOnc2GO468pATybRqmrTiMVo/9iA9+2YNci/PHAURE1SHQzxN/fXwf2jVxLhhOSonzmWbc8Ox3OHrmPPdpdQzD5IiIiIiIiIiIiIiIiIiInBQXF4f9+/cjNjbW3aUQERER1UlWuwP3TVyEH9fsdbqxo9Gg4vsXh+OOa9sySO4KdnXjwBI7D50+n4vTqZd33Mu3OvDU9M3o+dKvmLX2KBvQ1kH79u277LWwsDA3VEJERERERERERDWF1DTsWPge/lnwFqTmcHc5lc5ut+Prr792+nqo3W53OaCtLBaLBc8++yx69+6NrVu3QtO0Cs9fSonU1FQsW7bM6Wmys7MxcuRIjBgxAvHx8ZVSQ15eHt555x106dIFhw4dqpLrzlJKfP755zh//nylz5uokJQSB89k4Ka3/sSg9/7C4cQsVNddFCGAJiE++OL+7jj05e0Yf0cHhPl78D4f0RWgMEAu7ug5PP7tJjSPXYg+r/6O+RuOw2b/N05OXvipvrqATLMN42bHof3Ti/Fr3GnYHVdmsDAR1U6Bvp7466P70KZRiJPHRBIp6Tm4+YXvkZSWXeX1Uc2hc3cBRERERERERERERERERERERERERGWx2R146KOfMHfFbpeC5H548Q7cfu1V7GByhQsP8EREgCcSzl8eGqdJiV+3xuPxAdFF24GUElOWH8QXy/ZDSmD78fXo0TIMTcN8qrt0chNN0/DDDz9c8pper0ejRo2KHb9p06b47rvvin5v1qxZVZZHREREVGNIqVVd711R9J9/X3LzuVuF368Q1fYeKmXdVHG9UsqCHtkVVY2fKxER1W1Sc2DTj28j7ucPCl9B9+GvQiiqW+uqTIcOHcLBgwedHl9KiZkzZ+LVV1+F0Wis8PKzsrIwdOhQrFq1yq0P+EhPT0f//v2xbdu2Sq9DSokDBw6gZ8+eWLJkCbp3717pxzKZmZl466238Nlnn/E4iSqVlBIZuVa8sWAHpv51EPl2ByCrfhsr3Ixbhvth/B0dMaRLAxj1KrdvoiuEzaHh0JlMzFpzFHPXH8OZdDOklGVeMhDi32tFmpRVnjAnpcDx5BwM+/BvXBMdhi/u74bWkf7cFxFRjRDk54UVH9+P6578BgdOpZS5S5QSOJ6Yjhue/harP3sQwf5e1VInuRfD5IiIiIiIiIiIiIiIiIiIiIiIiKhGc2ganp/yO35YvtPpTl0GvYrpLwxnkFwdoVcV9G0bju9XH72s04GUwIyVRxB7U2vo1H+3hcT03KJx7Q4Ne+PTGCZXR0gpsXnzZvz999+X7FNat26NgICAYqcJDg7G2LFjq6lCIiIioppBSon1s19G/J5VVTJ/vckLOoMndHojTL5B8AtphMCoaAREtIRPUBR0Rs9qPZ9z2K34/eMRyE49U+55dBzwf4i+5u5KrKp4Ukqs+u5pJB3eVKH5NI8ZjJjbxlXZ55xyYidWfPMopFb+3t56oyeGvvwb9EbPSqyMiIjoclJz4J8fJ2Drzx8WhLYC2PrLR4AEut9xZQTKSSkxbdo0aJrm0nSnT5/Gtm3b0KNHjwotPzc3FwMGDMDGjRudvtZfeJxy8YNCLv63vHXcfPPNTgfJXVyDs8uXUiItLQ2DBw/Ghg0b0LJly3LXW9L8v/32Wzz77LNo0KBBpc6b6i6r3YE5a49j3OytSMkyX7iHUrXnZIVBUR0aBWDCiE64/qpw6HUK7+0RXUHiz+VgyHt/Y09CulOBcEIAihCIrOeB23s0xtjrmsOhSbz14w78vvU0rI4qfPDBBZomsWpvEjo99wse7t8a4+/oAD9PA/dNROR2wf5eWP7BWPR67GucSs5C2TtEiYMJ53Hryz9g+Qf3wtuz4gHhVLMxTI6IiIiIiIiIiIiIiIiIXCKEgFDqVqMYoRW835iYGKiqitjYWMTGxrq5KiIiIqK6QdMkJvywEl8s3uxSkNx3L9yOO69rywbddYQQAiP7NMP3q48WO3xvQjr2JWSgfaOAovFbRvpDiIKwOQmB/afTMTimAbeZK5yUErt27cLw4cNht9uLXhdC4LHHHoOq1v5OwURERESVRWoOHN+2FBlniz/OrhICEFBg8PRHeIsuaHPtaDRo2xd6k1eVHqtLKXF4/QKc2LkclyVUuyB+z0q07jOyys8rNIcNJ3YsQ07q6QrNR0qJzsNegBCVfxxst1nw15SHkHp6f4U+08AGbaHq2cmRiIiqltQc2LjgLWxb8lFRkBwASKlh65KPAKGg2/CXodTyQDmz2YwFCxa4HMQmpcRXX32F7t27l/s4x+Fw4IEHHnAqSE4IAX9/f9xyyy0YPHgwWrRoUfQQiPT0dJw5cwb//PMPli1bht27d8Nmszn9njRNw5NPPoktW7aUOo0QAnq9Hn379sXdd9+Ndu3aoV69esjLy8PJkyexePFizJs3DxkZGSXOpzBQbujQodiyZQu8vb2dqtFZZrMZr732GqZPn14lx58eHh6wWCxOjWuxWEoNKRRCwGAwQFGUMuclhOB1+mompcSuk+l4eOp6xB09X5HDd5coikDHxgF453+dce1V4dCrZW8fRFS7aFLi/q/WY1d8mlMhcmF+HhjWrRHGXNscbRvWg+GicMmFz/TF8eRsvLVgBxZsPAGrQ6vy/ZXFpuHzpfuwcONxfDS2K27r1gg67quIyM0ig/2x4uP7cd2T3+D0+Wyngq43HTiNoa/Nwq9vj4bJqK+mSskdGCZHREREREREREREREREROSkuLg4+Pr6ursMIiIiojpDSolPFqzFWzNXQdOcawmu1ymY9twwjLi+HTsb1TFdmwcjwNuI1OzLO7dpUuLDX/bgh8f7FG0XrSIvPbbfcyq9WuqkqiGlRFZWFjw8PIodbrFYcOrUKXz99deYNm0a8vPzixpVCyEQHR2N//3vf9xvEBEREV3Eas5GTtqZ6l2oBCQ0WHLTcHLHHzi5czlMPoHocNOj6HhzLPTGqgmVs+ZlYcO81yoUegYA5+P3QkqtSsLZLpafk468jJQKzycz+Tis5myYvPwrXtRFpJTY+cfkCgfJAUB4s84Qgh21iYio6miaAxvnvY7tv316SZBcISk1bP3lA0AFug97BcKJMKyaauPGjTh79qzL00kp8euvvyIzMxP+/v7lmn7evHmYP39+mQFuRqMRzz//PJ588kn4+/tfduxXv359tGvXDjfddBPGjx+PxMREzJgxA5MmTUJKStnHR2vXrsX06dPLrCM6Ohpz5szBVVdddVkAWvPmzdGvXz+8+eabePLJJzF37twSg9SklDh48CDeeOMNTJw4sVKPZaWUmDt3Lp5//nlER0dX2nwBwGQy4cCBA06F9GmahnvuuQdLliwpcZzIyEj8888/Tgfq+fj4OF0rlZ+UEhl5Voyftx1T/zoEq0MrM+ypooQo+I5d3TgQ797dGddEhzGYiegK5tAkDiVmFrtvKfyTGOhtxMDO9XHP9S3QqUkQPAxqsX8vFSHQLMwXMx7rg/F3dMSbC3diwcbjyLdX7b5LSiAx3YyRn63B5D8OYMpDPdAq8vJjFCKi6tQ4PAB/f3w/rn1iKpLSc8rcD0opsXLbcTz40WJ8+/xt0Otqd1A6lYxhckRERERERERERERERERERERERFTjSCkx668deOnbv1wKkvv6maEY2a8DG2/XQV5GHYZ1a4hpfx++LKtASuCnzScx4XwnNAwu6KwWFegNRQg4Lox8ODETUv7bceFKIaVEfn6+S9MkJiZe9rvZbHZ6+sJOn9X5PczPz0eXLl1gMBguG6ZpGjIzM5GTkwMp5SUdIIUQCAoKwk8//VRiEB2AYjtNcj9DREREV7qslJOw21w7lqx0UiI/6zw2LXwLB9bMws2Pz0BI46srPYRj+5KPkZtV8XC2nPOn4bDmQzF5VUJlJUtPOgqHw4aKfgp2mxkZZ48irGnnSqmrUNa5U9jy0/sVDpIDBCJb9eKxNxERVRlNc2DD3New4/fPiw2SKySlhq0/fQBFCnS97eVaGSgnpcSkSZNKDAdTFAXh4eFITEwsdpysrCwsX74cd955p8vLzsrKwtNPP11i4BpQcK3N398fS5YsQc+ePcv8+y+EgKqqqF+/Pl555RU89thjePfdd5GUlFTiNFarFY8//jgcDkep8+3UqRP+/vtv+Pn5lTpeUFAQZsyYgeDgYHz22WclfrZSSkyePBlPPPEEoqKiSn1frrLZbBg3bhwWL158WehdRQghSn3/F5NSIiQkpNRxTCYTwsLCoNM5F21QeB3X4XDAYrEgOzsb58+fR05ODs6ePVv0sBCdTgd/f38EBwcjPDwc/v7+MBgMPH50gt2hYUlcPB7/bhOS0vMqfujuBEUR6NAoAO+M7IzrrgqHniFyRFc8nSJwQ/sITF95pGg/IwTga9JjQKf6uK9vC3RtHgxPo87pfbcQAk3CfDH9/3rj1eEd8PKsbfh566kqD8TUNIn1B5PR+fkleHLgVRg3rB28XKibiKgyCSHQLDIQf0y8B9c++Q0yci1lBkFLSMz+exf8vT3w6f/dUqnnD1RzMEyOiIiIiIiIiIiIiIiIiIiIiIiIahQpJX7dsB8PfrQYNnvJncsupioK3rmvP8bcWLmhAlR7CCHwfze1wXcrjhQFxF3MbHXgjQU78O2jBUEEAd5GeBpUZOfbAQCn03JhtWswGa6sJzCbzWa0atUKOTk5Tk/z3/C5ESNGwGg0Oj19YGAg9u7d69I0FSWlLLWjaHEKO4bOnz8fjRs3LnXcEydO4N133y36vUePHhg7diz3N0RERHRFO3tsGyRQ4cCySiElMlNO4Mc3bkT/R6ehWZchlXYslnMuHtuXf1UJwWeA1ZKDvKxz8KvCMDkpJc4e2QxRST2kEw9uQGiTTpX2eWoOO1Z+8xhsFufPQUoihEBw4w4VL4qIiKgYmubA+jmvYOfSL0sNkiskpYYtiz+AUe+JDkOeghC1q+P9+fPn8eeff5Y4vEmTJpg6dSpuvPFG2Gy2y4YXBqINHz7cpdABKSW+/fZbnDt3rtTxTCYTfv31V/To0cPl45LCILr33nsPqampJY63adMm7Nu3r9R5+fn54ccff3Q6SE2n0+H999/Hjh07sHbt2hIDHMxmMz799FN88MEHlR6MvHTpUmzbtg0xMTGVNl930TQN27dvx8aNG7Fu3Trs3bsXSUlJyM3NvSQE8L8PDAEK1kVISAh69+6Ne++9F3369GGwXDGklDiZkoNHpm7A33uSnH6gU0UIAbSK8McHY2JwQ/tIhsgR1SFCCHwythsMqopVexPROsof91zfAn2iw+Droa/QPloIgaZhvpjzzLXYn5COl2Zvwx87TsMhZZUFZEpZcM/5vcW7sGDDcUx5uCeuaxMOReHfGiKqfkIIXNUkDEveHY0BL3yPHLO17EA5KTH5l02IDPbF83f14bHyFYhhckRERERERERERERERETkEqEqUNQrK1yhLOLCvfWYmBioqorY2FjExsa6tygiIiKiK5SUEhv3ncLd7yyExeooewIAiiLw+j198dQdvdnQsY6Lru+P7i2Dsf5ASrHD56w7hscGRKNj40B4GFQE+3ogOz8bAJCRa0O22QqTwaM6S64WmZmZyMrKKte0Ukrk5eUhLy/P6Wl0uprbPFUIAb1ej3bt2uHpp5/GbbfdBoPBUOZ0KSkp+Pbbb4saX9vtdowdO7aKqyUiIiJyHyklzhxcD1FVvW/LQ0rYrWYsn3QvPH1/RUQr14NGLp+lhnVzX4HNklspoXlS05CZfAx+IY0qYW4lO3NgQ+XMSEqc2r0KHQc8Xkmzkziy5Rck7FtdKeF8OpMXfAIjK14YERHRf2gOB9bNHoedf0x26W+WlA6sW/gmYDSiw02P1ppAOSklfv31V5jN5mKHCyEwcuRI9OrVC23atMHOnTuLHW/Tpk04ffo0GjRo4PSyrVYrvvzyy1JDDYQQePXVV8sVJPff+QQFBRU7TEqJSZMmlVnHiy++6NL7AwCDwYAvv/wSnTp1gtVqLXH5s2fPxltvvQUPj/Jdgy78bP77HhwOB5599lmsXLkSai1vz5OZmYnevXvDYrGUGYRRqHA8m82GM2fOYP78+Zg/fz6aN2+OyZMn4/rrr+e9owssNge+XLYfb8zfgVyrvcrClgoJATQO8cGEEVdjaNdGMOgUrguiOsjHQ49JD3SHJiUUISp9P6AIgasaBODnF/ph58k0PD19M9YfSoas4lC54ynZuOmt5bi9eyN8fm83BPmauI8jomonhECPNg2x+K27MfilH5BnuTwY/L80TeK1b/9C/WA/jOjbnvuuK0zNba1DRERERERERERERERERFTDxMXFwdfX191lEBEREV2xpJQ4EH8Og1+aidz8shs4AgVBci/ffR3G/e9aPvGboCoCb93VGX3fWAZNu7x3gM2h4cGv1mP92wNh0CloHOqN4ynZRcOSM/MR7HflhcnVBaqqYujQoQgJCblsmKIoMJlMaNCgAaKjo9GmTRsEBwdDUdhxjYiIiKgkUnMg5fgOd5dRLIfdij8+H4O7P9wKo5d/heaVfHQrjm75pVKC5ApIpJzYhfpXVV1gheaw4Xz83kqb37mTO+GwWaAzmCo8L0tuOtZ+/yyk1CqhMsA3uBF0Rs9KmRcREVEhzW7D2h9ewK6/p5Yr/FRqdqyb9TKEUND+xodrRaCcpmn46quvSgznUhQFI0aMgE6nw5gxY7Br165ix7XZbJg3bx6ee+45p491Dh48iFOnTpU6TkREBB577LEqvVaXl5eHFStWlBpQ5uXlhfvvv79cdURHR6Nnz55YtWpVieMkJydj37596Ny5s8vzBwoe4tGqVSvs3bv3kvchpcT69euxbt06XHvtteWad01SEP5T/vSfwmkPHz6Mm2++GW+//TaeeeYZKErN/65WFSkldp1Kw71frMeu+NRqCZEL9jXh5WEdcF+/FvAwqLwWT1THCSGgVvF+QFEErm4SiBVv3IS1+5PxzPQtVbrPkxJwSIkFG49j5Z5EfHJPV9zRowl0at39e0NE7iGEwHUdm+CbZ4finomLYLWV/eBOm0PDfR/8hLAAH1zXsQmP1a4gDJMjIiIiIiIiIiIiIiIiIiIiIiKiGiEhJRM3PTcdGTn5TnUUEkLg4UFd8dqY6xkkR0V6tgpBt+bB+OdQCv67FUkJbD+Rio+W7MW4Ye0QXd8fK/YkFQ08djYLVzWoV+01VyVVVTFixAiYzWanpzlw4AC2bt0KKSWEEOjatStatGjh9PQ+Pj7V3jHPaDRi2rRppQaAswE0ERERkfOs5mzkpJ1xdxnFkxI5mWex/fcv0G34K+U+znPYbVg1/elKCz4rlHx8W6XO778suRnIy0yptPnl56QhNz0JfqGNKzQfKTVsmPsa8rLOVVJlQHjzzrUioIeIiGoPh92KVV8/in3r5wGXXT10ntTsWDtzHABxIVCuZl93OnHiBHbu3Fni8BYtWqBJk4IAgWHDhuGFF16A1Wq9bDwpJb755hs8+eSTMBgMZS5XSok//vij1Ov9Qgjce++98PLycuq9lNexY8eQnp5e6jjXXHMNAgICyjV/RVFw3333YfXq1aW+37/++gudOnUq1zaj1+vx2Wef4fbbb0daWtolwzRNw1NPPYXNmzc7tW7qCpvNhnHjxqFFixYYPHhwjf+uVoU8ix3v/bQLHy7ZA4tdq9ogOQH4GHV4/JY2eHZIW/h66OvkZ05E7qUqCq67Khyb3h+InzfH44WZcYhPzanCUDmBc1kWjP5iHeasPY6vHuqBqEAv7v+IqFoJIXBX3/bIMVvx6KdL4NDKvuZtsdoxfPxsrPr0AbRtEsb91hWCYXJERERERERERERERERERERERETkdmnZebhl3Pc4fS4LznTiE0Jg1A0d8Mn/3QK1mkOrqGbTqQo+GtMVvV/9DXbH5duSlMBbC3egd3QYWkf6Q6Bgi5MQ2J+QjsExDa6oRrJGoxFfffWVS9OsWrUK/fr1K/r9008/RZcuXVyahzs+QyHEFbXuiIiIiNwpM+Uk7LZ8d5dRMimx++9v0GnwUzCYvMsxucSRzYuRcnInKrs3cWrCAUipQQi1UudbKD3pCDTNVmnzk1JD8vFtFQ6TSzqyBfvXzKq8z1MIRLTqxWN8IiKqNA67FX9NeRiHNi5ARYLkChUEyr0IAYF2Nz5UY/9mSSkxY8YMOByOYocLITB69GjodAVdzqOiotCpUyf8888/xY5//Phx7Nu3Dx07dnRq+StXriwzTG748OFV/vlt3769zDpuueWWCtXRs2dPKIpS4mctpcT69evLPX8hBNq3b4/nn38e48aNu+z97Nq1C8uWLbuiQtOEEFAUBT4+PqhXrx5CQ0Oh1+shpUR2djaSkpKQmpoKTdNKXL8OhwOxsbG4/vrr4ePjU83vwH2klNh0+Bzu/XI9Dp/NqNIQOSEAvapgRK8mmDCiEyICPK+YbZCIai+DTsXwHo1wS6f6mLL8IN5dvAvpuZYq2x9qmsTSnQlo99RivD+qC+7p2xx6le0YiKj6CCFw3y0xOH0+C2/PWgVNK3uHl5FrwaCXfsDGSY8gMqjkB/dR7cG/PERERERERERERERERETkEqFT6+QPAMTExCA6OhqTJk1y81ogIiIiurKYLTYMfXkm9p9MgbNBckN6tMLUZ4dCr6uazvlUu8U0D8Lw7o1QUl8lq0PDXR+tRNyx85AXbXN7E9KrqcLqVRiy5uxPYcfRQqqqujwPIiIiIqrdko9thashK0LRwTekMfzDmpfw0ww+QQ2h9/CBEApkBQ8b83PScO7krnJNa8vPwfrZL1d6kBwA5KQlwm4xV/p8gYJAiKQjWyoh/uaSmSJ+T+khL2WxW8z4e8qj0DR7pZUlIBDSqEOlzY+IiOo2h82KP796sCBIrhL//kvNjjUzX8DuP7+u0N/SqmS1WjFz5swS69PpdLjrrruKrukJIXDPPfeUeI1PSomvv3bu/drtdhw5cqTUcXx8fNC4ccVCbcsipcT+/fvLrLlTp04VWk5ISAh8fUsPYDh69GiJYXPOUFUVjz76KCIiIoodPm7cOFgslnLPv6bw9vbG4MGDMXfuXBw+fBinT5/G4cOHsWHDBqxZswZr1qzBtm3bcOrUKRw4cADPPPMMjEZjifNLTEzE4sWLq/EduFduvg3P/bAF141fWuVBcooi0KtVGLa8NxjfxfZGZKAX7xEQUY0hhICXSYenB7fBgc+H4eH+rWDSK6iyvZQUyDTb8Mg3G3DTW8txMiW7xh4jEtGVSVEEXhtzPR4ZFOPUMZmUEgnnMjHkpe+RlVuDHy5DTtOVPQoREREREREREREREREREQFAXFxcmQ1/iYiIiMg1NrsDo96ej/V7451qSC2EQK+2DTDz5Tth0LMJHBVPEQKf3tMNK/YkISXz8gavUgJnM834ftWlHRkPncmElCgxhI6IiIiIqC6QUuLMgfWuZskhsnUv3PriYgillNBvKeGwWZCddgaHNi7AruVTYMnNKF+oiwRO71+HiJY9XAorkFJi26+fIDcjyfVlOsFuyUVeZjIMHt5VMv8zB9ZDVHJH5MRDmyA1B4Tq+nm2lBLbfv8M6WcPV2o4j87kBZ/AyEqbHxER1V0OmwXLJ9+PI5sXV0mQrNTsWPPDCwAE2vV/sMaFKG3duhWnT58ucXjbtm1Rv379ot+FEBg4cCCMRiPy84u7tiqxaNEifPjhh/D2Lv14x2q1IjU1tdRxQkNDYTKZyngXFXfs2LFShwshEBISUqFlGI1GhISEID295IeWpKenw+FwXPZAD1d4e3vj1VdfxSOPPHLJfRUpJQ4ePIg5c+aUGghYk+l0Orz33nsYNWoUAgICSnwPha+rqormzZtj4sSJ6NatG+644w5omnbZ+FJKfP/997j77ruhKEqVvgd3klJiy+FUjP1yDQ6fzazSEDkhgGahvvj4nq64sUMkdOqV+7kSUe0nhECwrwe+vL87Hr+lDZ749h/8vScJmlY1O0opgdV7k9Dx2Z/x/t0xuLdvC+4niajaqIqCj2IHITkjD4vW7iu7LZYEdhw9i/+9NQ+L3robRrbFqtX414aIiIiIiIiIiIiIiIiIiIiIiIjcQtM0jPtmORavP+DcE7mFQNvGofjl7dHw8jBUfYFUqwX5mvDVgz2gKsV3NpMSKOgf8O/w02m5sDou72hGRERERFSXSM2BlBM7XZtICERF94ai6qEoask/qg56kxcCIlqg220vY+R7mxAY1bqcic4SqQn7XJ4qJ/U0diyb5HyYjBAu1SelRHriIZfrcobmsOF8/N5Kn2/WuVOw5maWa9rM5OPYuuTjSg/n8Q1pBJ3Rs1LnSUREdY/DZsHySfdVWZBcoYJAueex+69vnLvWXU2klJg0aVKJNQkhMGbMmMvCtUJDQxETE1PifFNTU7Fq1aoyl+9wOGCz2UodJzg4uFrCvXJzc0sdrtPpKvxwQSEEvLy8Sh0nPz+/zM/EmeWMHj0azZo1u2yYlBKvv/56me+3pvL29sYTTzyBwMBAl8LwhBAYMmQIWrZsWeI4O3fuLDYg8UqRb7Vj/PztuGb8b1UbJCcAP0893r27M3Z8dCsGXB3FgCQiqjWEEGgZ4YffX+6PX1+8Ac3DfarsIWMSQGaeDY9O+we3vvc3EtPyatRxIhFd2Qx6FTNevB09rmrg1HG1lBLLthzBc5N/LzacmWoPHpkTERERERERERERERERERERERFRtZNSYsqSzfj0xw1ONZoWAmgc5o+lE8fC39ujGiqk2k4IgSExDXHv9c2d7gSQkWtDjrliHfmIiIiIiGq7/2fvvuOjqPY3jj9ndjedhN6LFOkqxShgL2BXrAiKHVvsYu96LVdsVwV7QxGvYkURCwJSFEMVKdIUJJRQ09vunN8fXPIzhmR3k01CyOf9cl+ys+fMfLdkZ3b3nGcK87KUvT0t7H4tO/cLO/ChXqPWOv22CfJFlx+6UZacHZvCam+tqxnv36OighDDNYxRXGITeXwxoW/ESJv/WFglE4Tzs3coNyM94usNFBVoewUC8NyAX1Neu07+wtyI19Si08EyhqlvAICK8xcVaPKLl2rlL59VaZDcbtb1a/o7t2nx96/vNUEhGRkZmjRpUpn1+Hw+nXnmmaWO4RzH0WWXXVbmsZ21Vi+//HLQkAFrbdDHwufzhXUMWVGBQKDc2z0eT0RC7aKiyj8RTlFRkfx+f6W3ExMTo8cee2yPj9369ev1yiuv7DWvw3AYY4K+Hna/rv558Xg86tmzZ5n9MjIytGPHjkiXXOOstVqetlMD7vlSj368SIV+t0re8oyRvB6j8w9rryXPnaXbTj9AcdHeavn7BYBI8ziOTuzdSgueOlOPX5CsxDhflYXKua7V1wvXq9etn2rCz3/KdWvf/hlA7RQXE6UvHr1IXVo3DOlkKdZajfniF730+Zxa+VkCu3hrugAAAAAAAAAAAAAAtYvjceTUsTPKOv/7UTw5OVkej0cpKSlKSUmp4aoAAABqL2utJs35XbeMmRTSYGljjBonxumbUZepRcN61VAh9hWOY/Tspf204I/tmrt6a9D2RQFXm3bmqXFiGEERAAAAwD4mI/1P+Yvyw+rjOF41aNWlQtur17itOvc/V0umvhV2X2tdSVZSaDN+N62ap9W/fBFyoIzjeHX81S9rzoRHtXn1vFCLUvrqBaG1DdPOjavkBsIIwN49STDY/TXSX0t/VIsu/UMOg7DWasXsCUpbNrP89Ydaw9/XLaNWXQ8jmAIAUGH+onx9/fwlWjPvy2oJktvNun5Ne3ukZIwOOO7yGt+Xffvtt8rMzCzz9oMPPlitWrXa422nnHKKYmNjlZu759DYqVOnKj09Xc2bNy9z/aEEgwULeYuUYEFxoQTfhaKoqPxjNcdxIhJaZ4zR6aefrh49emjJkiUlarfW6oknntBll12mBg0aVHpbNWX3c5Kdna3169drzZo12rBhg/766y/l5uaWeu0EAgHNnDmzzPW5rqusrKyqLrta+QOuxnyzTPeMm6fcQn+Vvd0ZI/Vo00AvXdlf/bs0k8NxOoB9gDFGcdFe3XZGT11wZAfd+vYcffzzWgWqIOzNWmlrVoGGPjtN5w9orxdH9FdSXFSNHysC2Pc1qBerr0ddpgHXvqRNO3KCfuZxXatbX5qkDq0a6sTkzrxP1UKEyQEAAAAAAAAAAABAiFJTU5WYmFjTZQAAANRq1lotWrVRwx75QEV+N6Q+8TE+ffXExerYsiEDFRG2uGivPrvjeA24e6L+2pYTdDLVms2Z6tm29k6wAwAAACpr85p5u/LZwhBTr7FiExpWaHvGGO3Xe5B+m/a2TJjpB76YBIUaJBfwF2n6O7fKdf0htbcy2q/XCWp34PFa+dPHoYfJSdqWtkzWDch4Ijd1y1qrjSvnhBWIY4yj+PotlLV9ffmPkrX6a8k0HXLm7Qr18czP2qof37vzf4F+ZfN4oxQd31C5OzeGXLdjjJrs1yvk9gA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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#create 2 plots: supplements and main text\n", - "fontsize = 28\n", - "width = 50\n", - "height = 15\n", - "# select colormap\n", - "cmap = None#plt.cm.get_cmap('magma')\n", - "\n", - "# gridspec inside gridspec\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs0 = gridspec.GridSpec(1, 2, figure=fig, width_ratios = [3,4])\n", - "gs_pam = gs0[0]\n", - "gs_figure = gs0[1]\n", - "\n", - "#adjust labels for better readibility\n", - "x_csc_label_pam = adjust_heatmap_labels(x_csc_nonzero_pam)\n", - "x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam)\n", - "\n", - "fig_pam = make_heatmap_subfigure(results = results_pam, csc_matrix=csc_nonzero_pam_t, esc_matrix =esc_top5_pam, \n", - " ylabels = True, xlabels = True, x_csc=x_csc_label_pam, x_esc=x_esc_label_pam, \n", - " yaxis = glc_uptake_rates, fig = fig, grdspc=gs_pam, \n", - " annotate = 'A', phenotype_data = pt_data, fontsize = fontsize, cmap = cmap)\n", - "fig_pam.subplots_adjust(left=0.3)\n", - "#set common x axis title\n", - "# fig_pam.xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - "\n", - "#add image\n", - "\n", - "ax_fig = fig.add_subplot(gs_figure)\n", - "ax_fig.imshow(sensitivities_mapped)\n", - "ax_fig.annotate('B', xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.30), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize*1.5, weight = 'bold')\n", - "ax_fig.axis('off')\n", - "ax_fig.set_xticks([])\n", - "ax_fig.set_yticks([])\n", - "\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/Figure2_sensitivities_pam.png', dpi =275,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "1a2a55bd-67a7-4bda-9dd0-2716e5d0deb6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_74560/2464939843.py:20: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 28\n", - "width = 20\n", - "height = 15\n", - "\n", - "#supplemental figure (GECKO)\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs = gridspec.GridSpec(1, 1, figure=fig)\n", - "#adjust labels for better readibility\n", - "x_csc_label_gecko = adjust_heatmap_labels(x_csc_nonzero_gecko)\n", - "x_esc_label_gecko = adjust_heatmap_labels(x_esc_top5_gecko)\n", - "\n", - "fig_gecko = make_heatmap_subfigure(results = results_gecko, csc_matrix=csc_nonzero_gecko_t, esc_matrix =esc_top5_gecko,\n", - " cbar =True, ylabels = True, xlabels = True,x_csc=x_csc_label_gecko,\n", - " x_esc=x_esc_label_gecko, yaxis = glc_uptake_rates, fig = fig, grdspc = gs[0],\n", - " phenotype_data = pt_data, fontsize = fontsize, cmap=cmap\n", - " )\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/SuppFigure2_sensitivities_gecko.png', dpi =300,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ecfd9d55", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAModelpy", - "language": "python", - "name": "pamodelpy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/Figure2_sensitivities_pam.png b/Figures/Figure2_sensitivities_pam.png deleted file mode 100644 index 50c1b6f..0000000 Binary files a/Figures/Figure2_sensitivities_pam.png and /dev/null differ diff --git a/Figures/Figure3_sensitivities_protein-overproduction.ipynb b/Figures/Figure3_sensitivities_protein-overproduction.ipynb deleted file mode 100644 index 7d4c609..0000000 --- a/Figures/Figure3_sensitivities_protein-overproduction.ipynb +++ /dev/null @@ -1,1613 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "6d2a8cae-9e89-4faa-9b23-2c97b6186037", - "metadata": {}, - "source": [ - "# Code to generate figure 3 in the publication\n", - "Analysis of sensitive enzymes and reactions in the model simulations of protein overexpression" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "3e02abee-b41a-4257-ae81-9f22b71d17b3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading PAModelpy modules version 0.0.4.1\n", - "Loading PAModelpy modules version 0.0.4.1\n" - ] - } - ], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "import matplotlib.colors as mcolors\n", - "\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "from optlang.symbolics import Zero\n", - "\n", - "from PAModelpy.configuration import Config\n", - "from PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector, CustomSector\n", - "from PAModelpy.Enzyme import Enzyme\n", - "\n", - "if os.path.split(os.getcwd())[1] == 'Figures':\n", - " os.chdir(os.path.split(os.getcwd())[0])\n", - " \n", - "from Scripts.pam_generation import set_up_ecoli_pam\n", - "\n", - "DATA_DIR = 'Data'\n", - "eGFP_MW = 2.8*1e4 #g/mol\n", - "eGFP_RANGE = np.arange(0,0.15,0.01)\n", - "GLC_CONC = 9.81 #mmol_glc/gcdw/h\n", - "eGFP_BEINICK_DATA_PATH = os.path.join(DATA_DIR, 'eGFP_expression_Bienick2014.xls')\n", - "eGFP_SEQ_PATH = os.path.join(DATA_DIR, 'eGFP_protein_sequence.txt')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "eb3e6732-331c-4e03-8b71-cae05360439a", - "metadata": {}, - "outputs": [], - "source": [ - "BIOMASS_RXNID = 'BIOMASS_Ec_iML1515_core_75p37M'" - ] - }, - { - "cell_type": "markdown", - "id": "47bbc17e-1d0f-4375-922a-4f630333f787", - "metadata": {}, - "source": [ - "## sensitivities of protein overexpression in the PAM of E.coli" - ] - }, - { - "cell_type": "markdown", - "id": "fd5ff0a1-bc1e-42fe-bc4c-7d93571a5c27", - "metadata": {}, - "source": [ - "### 1. Usefull functions" - ] - }, - { - "cell_type": "markdown", - "id": "75ddd447-76d1-49d1-b93f-6af5e4357b0f", - "metadata": {}, - "source": [ - "#### 1.1 Sensitivity analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "69b86edc-c37a-420f-9c88-2fa9fd186b59", - "metadata": {}, - "outputs": [], - "source": [ - "def calculate_sensitivities(pamodel):\n", - " #initialize objects for storing information\n", - " results_df = pd.DataFrame(columns = ['eGFP', 'mu', 'mu_normalized'])\n", - " Ccsc = [] #capacity sensitivity coefficients\n", - " Cesc = [] #variable sensitivity coefficients\n", - " x_axis_esc = []\n", - " x_axis_csc = []\n", - " y_axis = []\n", - " fluxes = []\n", - " \n", - " # #set glucose uptake rate\n", - " # pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " # lower_bound = -100, upper_bound = 0)\n", - " \n", - " #set glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -GLC_CONC, upper_bound = -GLC_CONC)\n", - " \n", - " \n", - " for conc in eGFP_RANGE:\n", - " print('Running simulations with the following eGFP concentration: ', conc, 'mmol/g_cdw/h')\n", - " with pamodel:\n", - " #change eGFP concentration\n", - " pamodel.constraints['eGFP_min'].ub = -conc*1e3\n", - " sol_pam = pamodel.optimize()\n", - " #check if simulation is optimal\n", - " if pamodel.solver.status == 'optimal': \n", - " y_axis += [conc]\n", - " \n", - " # save data\n", - " fluxes.append(sol_pam.fluxes) # flux distributions\n", - " \n", - " # calculate normalized growth rate\n", - " mu = pamodel.reactions.get_by_id(BIOMASS_RXNID).flux\n", - " if conc == 0:\n", - " mu_normalized = 1\n", - " mu_wt = mu\n", - " else:\n", - " mu_normalized = mu/mu_wt\n", - " results_df.loc[len(results_df)] = [conc,mu, mu_normalized]\n", - " \n", - " #save sensitivities\n", - " Ccsc_new = list()\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if csc == 'EC_min_f':\n", - " Ccsc_new += [-coef for coef in capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()]\n", - " else:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - " \n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of variable sensitivity coefficients: \\t \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6), '\\n')\n", - "\n", - " return {'Ccsc':Ccsc, 'Cesc':Cesc, 'y_axis':y_axis, 'fluxes':fluxes, 'capacity coefficients':capacity_coeff, \n", - " 'enzyme coefficients':enzyme_coeff, 'results': results_df}\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f0b9ed5c-3082-4af5-8e58-5f56e30e5f79", - "metadata": {}, - "outputs": [], - "source": [ - "def find_nonzero_sensitivities(Cv, x_axis):\n", - " indices = []\n", - " for row in Cv:\n", - " for index, coeff in enumerate(row):\n", - " if abs(coeff)>0.09 and index not in indices:\n", - " indices.append(index)\n", - " \n", - " coeff_nonzero = []\n", - " for row in Cv:\n", - " coeff_nonzero.append([coeff for i, coeff in enumerate(row) if i in indices])\n", - " x_coeff_nonzero = [coeff for i, coeff in enumerate(x_axis) if i in indices]\n", - "\n", - " return coeff_nonzero, x_coeff_nonzero" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ec341893-b52f-4b88-a488-2581b93ee22b", - "metadata": {}, - "outputs": [], - "source": [ - "def find_top5_sensitivities(Cv, x_axis, yaxis):\n", - " #top 5 enzymes per simulation\n", - " Cv_df = pd.DataFrame(Cv, columns = x_axis, index =yaxis)\n", - " largest = list()\n", - " for i, row in Cv_df.iterrows():\n", - " top5 = abs(row).nlargest() \n", - " if top5.iloc[0]:\n", - " largest += [index for index, value in top5.items() if abs(value)>0.05]\n", - " print([index for index, value in top5.items() if abs(value)>0.05])\n", - " \n", - " #remove duplicates\n", - " largest_list = list(set(largest))\n", - "\n", - " #extract non duplicate top5 enzymes\n", - " top5_df = Cv_df[largest_list].T.drop_duplicates().sort_index()\n", - " largest_list = top5_df.index.values\n", - "\n", - " top5_matrix = [list(row) for i, row in top5_df.iterrows()]\n", - " return top5_matrix, largest_list\n" - ] - }, - { - "cell_type": "markdown", - "id": "b8633232-45b6-4af2-8099-2fb4841ef988", - "metadata": {}, - "source": [ - "#### 1.2 Plotting" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "e47c6d59-277f-41bc-89a6-647ecc476120", - "metadata": {}, - "outputs": [], - "source": [ - "def parse_x_axis_heatmap(capacity_coeff, enzyme_coeff):\n", - " x_axis_csc = []\n", - " \n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if csc == 'flux_ub' or csc == 'flux_lb':\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[capacity_coeff['constraint'] == csc].rxn_id.to_list()]\n", - " else:\n", - " x_axis_csc += [coef+'_'+ csc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == csc].enzyme_id.to_list()]\n", - " \n", - " x_axis_esc = enzyme_coeff.enzyme_id.to_list()\n", - " return x_axis_csc, x_axis_esc" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cf73a888-2270-4ea9-9cc8-4c9e0f5eceaa", - "metadata": {}, - "outputs": [], - "source": [ - "def make_heatmap_subfigure(results, csc_matrix, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc, ylabels = True,\n", - " xlabels=False, cbar =True, title = None, fontsize = 16, vmin = -1.5, vmax = 1.5,\n", - " annotate = None, phenotype_data = None,cmap = None):\n", - " # fig = plt.figure()\n", - " if cmap is None:\n", - " # Create separate colormaps for positive and negative values and a color for zero\n", - " colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128))\n", - " colors_pos = plt.cm.OrRd(np.linspace(0.1,1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128))\n", - "\n", - " colors_zero = np.array([[1,1,1, 1]]) # gray for zero\n", - "\n", - " # Combine them into a single colormap\n", - " colors = np.vstack((colors_neg, colors_zero, colors_pos))\n", - " combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap')\n", - "\n", - " # Create a norm that handles the zero color properly\n", - " bounds = np.linspace(vmin, vmax, len(colors))\n", - " norm = mcolors.BoundaryNorm(bounds, combined_cmap.N)\n", - "\n", - " if cbar:\n", - " gs = gridspec.GridSpecFromSubplotSpec(2, 2, width_ratios=[len(yaxis), 1], \n", - " height_ratios=[len(x_csc), len(x_esc)], hspace =0, subplot_spec=grdspc)\n", - " else:\n", - " gs = gridspec.GridSpecFromSubplotSpec(2, 1, width_ratios=[len(yaxis)], \n", - " height_ratios=[len(x_csc), len(x_esc)], hspace =0, subplot_spec=grdspc)\n", - " \n", - " esc_ax = fig.add_subplot(gs[1,0]) #ESC heatmap\n", - " csc_ax = fig.add_subplot(gs[0,0],sharex = esc_ax) #CSC heatmap\n", - " if cbar:\n", - " cbar_ax = fig.add_subplot(gs[0:,1]) #colorbar\n", - "\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " csc_ax.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.1), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize+5, weight = 'bold')\n", - "\n", - " #CAC heatmap\n", - " im_csc = csc_ax.imshow(csc_matrix, aspect=\"auto\", vmin = vmin, vmax =vmax,cmap = combined_cmap)\n", - " if title is not None: csc_ax.set_title(title, fontsize = fontsize*1.5)\n", - " csc_ax.set_yticks(np.arange(len(x_csc)), labels=x_csc, fontsize =fontsize)\n", - " csc_ax.xaxis.set_visible(False)\n", - " if ylabels:\n", - " csc_ax.set_ylabel('CSC', fontsize = fontsize*1.5)\n", - "\n", - " #Make line between CSC and ESC data more clear\n", - " axis = 'bottom'\n", - " csc_ax.spines[axis].set_linewidth(10)\n", - " csc_ax.spines[axis].set_color(\"black\")\n", - " csc_ax.spines[axis].set_zorder(0)\n", - " \n", - " #ESC heatmap\n", - " im_esc = esc_ax.imshow(esc_matrix, aspect=\"auto\", vmin = vmin, vmax =vmax,cmap = combined_cmap)\n", - " esc_ax.set_yticks(np.arange(len(x_esc)), labels=x_esc, fontsize =fontsize)\n", - " esc_ax.set_xticks(np.arange(len(yaxis)),labels = yaxis, fontsize =fontsize, rotation=45, ha='right')\n", - " if ylabels:\n", - " esc_ax.set_ylabel('ESC', fontsize = fontsize*1.25)\n", - " if xlabels:\n", - " esc_ax.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - " \n", - " #colorbar\n", - " if cbar:\n", - " cbar_ax.xaxis.set_visible(False)\n", - " make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm,\n", - " vmin = vmin, vmax=vmax, fontsize=fontsize*1.25)\n", - "\n", - " return fig" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f1fc7967-5869-4911-8ed4-2cee897adbd7", - "metadata": {}, - "outputs": [], - "source": [ - "def make_scaled_colorbar(ax, fig, cmap, norm,vmin, vmax,\n", - " fontsize=16, cbarlabel='Sensitivity Coefficient'):\n", - " sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)\n", - " sm.set_array([])\n", - " \n", - " cbar = fig.colorbar(sm, ax=ax, cax=ax, shrink=1, fraction=1)\n", - " \n", - " # Adjust the tick intervals\n", - " tick_locations = np.linspace(vmin, vmax, num=5) # Adjust num to the desired number of ticks\n", - " cbar.set_ticks(tick_locations)\n", - " cbar.set_ticklabels([f\"{tick:.1f}\" for tick in tick_locations]) # Optional: customize tick labels\n", - "\n", - " # Setting the fontsize of the colorbar\n", - " cbar.set_label(cbarlabel, fontsize=fontsize)\n", - " cbar.ax.tick_params(labelsize=fontsize)\n", - " cbar.ax.yaxis.get_offset_text().set(size=fontsize)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ba775506-b0e9-4fc3-a403-7c68515059e7", - "metadata": {}, - "outputs": [], - "source": [ - "#adjust labels for better readibility\n", - "def adjust_heatmap_labels(labels):\n", - " new_labels = labels.copy()\n", - "\n", - " for i, label in enumerate(labels):\n", - " if 'EX_glc__D_e' in label or label[:-3] == 'EX_glc__D_e':\n", - " if label[-1] == 'B': new_labels[i] = 'EX_glc_'+label[-2:]\n", - " else: new_labels[i] = 'EX_glc_lb'\n", - " if label == 'TotalProteinConstraint_proteome':\n", - " new_labels[i] = 'Protein pool'\n", - " if label == 'eGFP_enzyme_min':\n", - " new_labels[i] = 'eGFP_min'\n", - " if label[0].isdigit(): #all enzyme ids start with a digit\n", - " if label == '2.7.3.9':\n", - " new_labels[i] = 'Glucose\\ntransport'\n", - " else:\n", - " rxn_ids = pamodel.get_reactions_with_enzyme_id(label)\n", - " rxn_name = pamodel.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0]\n", - " new_labels[i] = '\\n'.join([part for part in rxn_name.split(' ')])\n", - " return new_labels" - ] - }, - { - "cell_type": "markdown", - "id": "3fcf2695-9f1e-47f6-a20e-b39b9f00bad5", - "metadata": {}, - "source": [ - "## 1.3 Determining aminoacid content of eGFP" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "5335c49f-82fd-44ca-add8-7f95aaf4fbe7", - "metadata": {}, - "outputs": [], - "source": [ - "def check_freq(x, total):\n", - " return {c: x.count(c)/total for c in set(x)}" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "3eedb11a-a294-4c85-ab17-16cf36683efa", - "metadata": {}, - "outputs": [], - "source": [ - "#aminoacid lookup table\n", - "aa_lookup ={'V':'VAL', 'I':'ILE', 'L':'LEU', 'E':'GLU', 'Q':'GLN', \\\n", - "'D':'ASP', 'N':'ASN', 'H':'HIS', 'W':'TRP', 'F':'PHE', 'Y':'TYR', \\\n", - "'R':'ARG', 'K':'LYS', 'S':'SER', 'T':'THR', 'M':'MET', 'A':'ALA', \\\n", - "'G':'GLY', 'P':'PRO', 'C':'CYS'}" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "743d4dbe-7d07-48ba-ba91-25aa86598269", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MRKGEELFTGVVPILVELDGDVNGHKFSVSGEGEGDATYGKLTLKFICTTGKLPVPWPTLVTTFGYGVQCFARYPDHMKQHDFFKSAMPEGYVQERTIFFKDDGNYKTRAEVKFEGDTLVNRIELKGIDFKEDGNILGHKLEYNYNSHNVYIMADKQKNGIKVNFKIRHNIEDGSVQLADHYQQNTPIGDGPVLLPDNHYLSTQSALSKDPNEKRDHMVLLEFVTAAGITHGMDELYKLEHHHHHH\n" - ] - } - ], - "source": [ - "#read amino acid sequence\n", - "with open(eGFP_SEQ_PATH) as f:\n", - " lines = f.readlines()\n", - "#need to remove document start ('\\ufeff') and end ('\\n') to only yield the amino acid sequence\n", - "aa_seq = lines[0].strip().replace('\\ufeff', '')\n", - "\n", - "print(aa_seq)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "84f51474-b4ef-45a4-85c2-9cdae265fb5f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'tyr__L_c': 0.044715447154471545,\n", - " 'thr__L_c': 0.06097560975609756,\n", - " 'lys__L_c': 0.08130081300813008,\n", - " 'ala__L_c': 0.036585365853658534,\n", - " 'glu__L_c': 0.06910569105691057,\n", - " 'ser__L_c': 0.032520325203252036,\n", - " 'cys__L_c': 0.008130081300813009,\n", - " 'trp__L_c': 0.0040650406504065045,\n", - " 'asp__L_c': 0.07317073170731707,\n", - " 'his__L_c': 0.06504065040650407,\n", - " 'gln__L_c': 0.032520325203252036,\n", - " 'asn__L_c': 0.052845528455284556,\n", - " 'ile__L_c': 0.04878048780487805,\n", - " 'gly_c': 0.09349593495934959,\n", - " 'phe__L_c': 0.052845528455284556,\n", - " 'met__L_c': 0.024390243902439025,\n", - " 'val__L_c': 0.06910569105691057,\n", - " 'pro__L_c': 0.04065040650406504,\n", - " 'arg__L_c': 0.028455284552845527,\n", - " 'leu__L_c': 0.08130081300813008}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#determine amino acid composition\n", - "aa_freq = check_freq(aa_seq, len(aa_seq))\n", - "\n", - "#match to model identifiers\n", - "aa_biggid_freq = dict()\n", - "for aa, freq in aa_freq.items():\n", - " threeletter = aa_lookup[aa].lower()\n", - " if threeletter != 'gly':\n", - " bigg_id = f'{threeletter}__L_c'\n", - " else: \n", - " bigg_id = f'{threeletter}_c'\n", - " aa_biggid_freq[bigg_id] = freq\n", - " \n", - "aa_biggid_freq" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "313f2b25-b355-4ad3-8205-f5b5c7f991be", - "metadata": {}, - "outputs": [], - "source": [ - "def add_aminoacid_sequence(model, seq, protein):\n", - " \"\"\"\n", - " model: COBRA model\n", - " seq: dict with {aminoacid_id: freq} key, value pairs\n", - " protein: enzyme variable\n", - " \"\"\"\n", - " for aa, freq in seq.items():\n", - " model.constraints[aa].set_linear_coefficients({\n", - " protein.forward_variable: -freq/protein.molmass,\n", - " protein.reverse_variable: -freq/protein.molmass\n", - " }) \n", - " return model" - ] - }, - { - "cell_type": "markdown", - "id": "c259d078-bc25-4abc-9725-54b159fcbe79", - "metadata": {}, - "source": [ - "### 2 Run PAM simulations with default protein content\n", - "#### 2.1 Build the model and add the eGFP protein" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "edf9d83b-43b2-469d-a031-2a487f22efd7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "Read LP format model from file /tmp/tmp3wthx7xm.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.258 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel = set_up_ecoli_pam()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "bd4a7d2e-577c-4f26-8093-3745d491f685", - "metadata": {}, - "outputs": [], - "source": [ - "#add eGFP enzyme and aminoacid consumption\n", - "eGFP_enzyme = Enzyme('eGFP', {}, molmass = 1e6) #molmass of 1e6 to get a direct relation between enzyme concentration and total protein content\n", - "pamodel.add_enzymes([eGFP_enzyme])\n", - "pamodel = add_aminoacid_sequence(pamodel, aa_biggid_freq, pamodel.enzyme_variables.get_by_id('eGFP'))\n", - "\n", - "#turn off Pyruvate Formate Lyase (PFL) reaction (inhibited by oxygen)\n", - "pamodel.change_reaction_bounds('PFL',0,0)" - ] - }, - { - "cell_type": "markdown", - "id": "2b696f7d-d722-49ba-b5ff-4ac0e846aa79", - "metadata": {}, - "source": [ - "#### 2.3 Run simulations for different eGFP concentrations" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "6b9e3329-f635-4e9f-be49-bfe044aeab30", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running simulations with the following eGFP concentration: 0.0 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.003936\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.056634 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.01 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.004081\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.042788 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.02 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.004237\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.027883 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.03 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.004406\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.011792 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.04 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.004834\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.04969 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.05 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.006501\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.114598 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.06 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.006836\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.105106 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.07 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.007206\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.094585 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.08 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.008384\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.424535 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.09 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.011563\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.512574 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.1 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.012671\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.519756 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.11 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.014006\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.524647 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.12 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.015655\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.53069 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.13 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.018252\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.585651 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.14 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.019516\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.641382 \n", - "\n" - ] - } - ], - "source": [ - "results_pam = calculate_sensitivities(pamodel)\n", - "x_axis_csc_pam,x_axis_esc_pam = parse_x_axis_heatmap(results_pam['capacity coefficients'], results_pam['enzyme coefficients'])" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "0e45ccd4-69f1-4ca5-96fc-5e9c77523197", - "metadata": {}, - "outputs": [], - "source": [ - "#get nonzero sensitivities\n", - "csc_nonzero_pam, x_csc_nonzero_pam = find_nonzero_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam)\n", - "esc_nonzero_pam, x_esc_nonzero_pam = find_nonzero_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "d285624b-a4e2-4189-a7c1-c3abd74b7e05", - "metadata": {}, - "outputs": [], - "source": [ - "csc_nonzero_pam_t = np.transpose(np.array(csc_nonzero_pam))\n", - "esc_nonzero_pam_t = np.transpose(np.array(esc_nonzero_pam))" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "97d5be6c-4f10-46fb-82a2-f0bbad45e0e6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.7.3.9', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.7.3.9', '2.3.1.16', '2.3.1.16']\n" - ] - } - ], - "source": [ - "#get top5 nonzero sensitivities\n", - "csc_top5_pam, x_csc_top5_pam = find_top5_sensitivities(results_pam['Ccsc'], x_axis = x_axis_csc_pam, yaxis = eGFP_RANGE)\n", - "esc_top5_pam, x_esc_top5_pam = find_top5_sensitivities(results_pam['Cesc'], x_axis = x_axis_esc_pam, yaxis = eGFP_RANGE)" - ] - }, - { - "cell_type": "markdown", - "id": "de650fc9-f708-444b-a278-8f96fee23252", - "metadata": {}, - "source": [ - "### 3. Run PAM simulations with more efficient ATP synthase\n", - "ATP synthase is the enzyme contributing the most to the enzyme burden" - ] - }, - { - "cell_type": "markdown", - "id": "63313890-2a22-49c1-a3dc-96bbc359a378", - "metadata": {}, - "source": [ - "#### 3.1 Build PAModel" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "72498350-f562-4921-ae68-88b78c89917a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "79.0" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#get old kcat value to change\n", - "old_kcat_atp = pamodel.enzymes.get_by_id('3.6.3.14').get_kcat_values('ATPS4rpp')['f']\n", - "old_kcat_atp" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "ce56a430-0cc9-4a13-b163-5c1f0c5fafd0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmplht4er4g.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.258 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel_atp = set_up_ecoli_pam()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f969f4a4-a015-418a-ab13-f3eba1f914d6", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ATPS4rpp\n", - "{'f': 158.0}\n", - "{'3.6.3.14': {'f': 79.0, 'b': 285.0, 'molmass': 234022.89499999996}}\n" - ] - } - ], - "source": [ - "#make atp synthase enzyme twice as efficient\n", - "new_kcat_atp = old_kcat_atp*2\n", - "active_enzyme = pamodel.sectors.get_by_id('ActiveEnzymeSector')\n", - "for rxn, kcat in {'ATPS4rpp':{'f': new_kcat_atp}}.items():\n", - " print(rxn)\n", - " print(kcat)\n", - " print(active_enzyme.rxn2protein[rxn])\n", - "\n", - "pamodel_atp.change_kcat_value(enzyme_id = '3.6.3.14', kcats = {'ATPS4rpp':{'f': new_kcat_atp}})\n", - "\n", - "#add eGFP protein\n", - "pamodel_atp.add_enzymes([eGFP_enzyme])\n", - "pamodel_atp = add_aminoacid_sequence(pamodel_atp, aa_biggid_freq, pamodel_atp.enzyme_variables.get_by_id('eGFP'))\n", - "\n", - "#turn off Pyruvate Formate Lyase (PFL) reaction (inhibited by oxygen)\n", - "pamodel_atp.change_reaction_bounds('PFL',0,0)" - ] - }, - { - "cell_type": "markdown", - "id": "4b88ec03-baeb-4f0f-aaad-3ccb31373122", - "metadata": {}, - "source": [ - "#### 3.2 Run simulations for different eGFP concentrations" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "b242baad-747a-4fc6-8b8c-0e497ddf6628", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running simulations with the following eGFP concentration: 0.0 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.005016\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.062551 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.01 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.005213\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.052445 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.02 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.005426\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.041513 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.03 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.005657\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.029652 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.04 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.005933\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.020724 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.05 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.00621\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.005891 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.06 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.006515\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.989605 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.07 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.007183\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.020845 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.08 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.007594\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.008721 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.09 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.008054\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.995128 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.1 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.016283\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.386978 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.11 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.018327\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.419075 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.12 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.020439\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.42364 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.13 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.023101\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.429395 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.14 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.026561\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.436873 \n", - "\n" - ] - } - ], - "source": [ - "results_atp = calculate_sensitivities(pamodel_atp)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "60319828-627e-4d99-9e9d-ac9c764dad7e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['3.6.3.14', '3.6.3.14', '4.2.1.3']\n", - "['3.6.3.14', '3.6.3.14', '4.2.1.3']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '2.3.1.16', '2.3.1.16', '2.3.1.16']\n" - ] - } - ], - "source": [ - "#parse results for nice plotting\n", - "x_axis_csc_atp,x_axis_esc_atp = parse_x_axis_heatmap(results_atp['capacity coefficients'], results_atp['enzyme coefficients'])\n", - "\n", - "#find top 5 sensitivities\n", - "csc_top5_atp, x_csc_top5_atp = find_top5_sensitivities(results_atp['Ccsc'], x_axis = x_axis_csc_atp, yaxis = eGFP_RANGE)\n", - "esc_top5_atp, x_esc_top5_atp = find_top5_sensitivities(results_atp['Cesc'], x_axis = x_axis_esc_atp, yaxis = eGFP_RANGE)" - ] - }, - { - "cell_type": "markdown", - "id": "e58822c2-7c17-4c99-b0c5-cd52c6862db9", - "metadata": {}, - "source": [ - "### 3. Run PAM simulations with total protein content of 0.31 g/g_cdw\n", - "#### 3.1 Build PAModel" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "bf9cd6cb-6992-4df6-a10b-47405063969e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmprdt6lpj9.lp\n", - "Reading time = 0.01 seconds\n", - ": 1877 rows, 5424 columns, 21150 nonzeros\n", - "Setting up the proteome allocation model iML1515\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.31 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/PAModel.py:246: UserWarning: Molar mass for E332 is invalid: 0.0\n", - " warnings.warn(f\"Molar mass for {enz.id} is invalid: {molmass}\")\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model iML1515\n", - "\n" - ] - } - ], - "source": [ - "pamodel_inc = set_up_ecoli_pam(total_protein =0.31)\n", - "#add eGFP protein\n", - "pamodel_inc.add_enzymes([eGFP_enzyme])\n", - "pamodel_inc = add_aminoacid_sequence(pamodel_inc, aa_biggid_freq, pamodel_inc.enzyme_variables.get_by_id('eGFP'))\n", - "\n", - "#turn off Pyruvate Formate Lyase (PFL) reaction (inhibited by oxygen)\n", - "pamodel_inc.change_reaction_bounds('PFL',0,0)" - ] - }, - { - "cell_type": "markdown", - "id": "a87b7228-cc6b-42d7-81b1-ed069b20b55d", - "metadata": {}, - "source": [ - "#### 3.2 Run simulations for different eGFP concentrations" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "191d8735-d3a4-4fc0-afd7-960dc42bb628", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running simulations with the following eGFP concentration: 0.0 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000134\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.159221 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.01 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000231\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.964075 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.02 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000269\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.079922 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.03 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000277\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.068187 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.04 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.00029\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.069446 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.05 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.0003\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.056683 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.06 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000311\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.042989 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.07 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000323\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.02826 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.08 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000336\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.012373 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.09 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.00035\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 0.995185 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.1 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.000386\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.040508 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.11 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001556\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.102387 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.12 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.001639\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.091889 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.13 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.002074\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.292383 \n", - "\n", - "Running simulations with the following eGFP concentration: 0.14 mmol/g_cdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t 1.002605\n", - "Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t 1.502708 \n", - "\n" - ] - } - ], - "source": [ - "results_inc = calculate_sensitivities(pamodel_inc)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "3f87f6f6-a880-4adb-b1f8-3daf07f8a9f6", - "metadata": {}, - "outputs": [], - "source": [ - "#parse x-axis\n", - "x_axis_csc_inc,x_axis_esc_inc = parse_x_axis_heatmap(results_inc['capacity coefficients'], results_inc['enzyme coefficients'])\n", - "#get nonzero sensitivities\n", - "csc_nonzero_inc, x_csc_nonzero_inc = find_nonzero_sensitivities(results_inc['Ccsc'], x_axis = x_axis_csc_inc)\n", - "esc_nonzero_inc, x_esc_nonzero_inc = find_nonzero_sensitivities(results_inc['Cesc'], x_axis = x_axis_esc_inc)\n", - "csc_nonzero_inc_t = np.transpose(np.array(csc_nonzero_inc))\n", - "esc_nonzero_inc_t = np.transpose(np.array(esc_nonzero_inc))" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "9e342a99-72d7-4180-85c4-79611d46e01f", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['EX_glc__D_e_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min']\n", - "['EX_glc__D_e_flux_lb', 'TotalProteinConstraint_proteome', 'eGFP_enzyme_min', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "['EX_glc__D_e_flux_lb', 'eGFP_enzyme_min', 'TotalProteinConstraint_proteome', 'ATPM_flux_lb']\n", - "[]\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n", - "['3.6.3.14', '3.6.3.14', '1.2.4.1', '2.3.1.16', '2.3.1.16']\n" - ] - } - ], - "source": [ - "#get top5 nonzero sensitivities\n", - "csc_top5_inc, x_csc_top5_inc = find_top5_sensitivities(results_inc['Ccsc'], x_axis = x_axis_csc_inc, yaxis = eGFP_RANGE)\n", - "esc_top5_inc, x_esc_top5_inc = find_top5_sensitivities(results_inc['Cesc'], x_axis = x_axis_esc_inc, yaxis = eGFP_RANGE)" - ] - }, - { - "cell_type": "markdown", - "id": "999ba86b-fa39-470a-ad63-9c99da737ee7", - "metadata": {}, - "source": [ - "### 4 Create plot" - ] - }, - { - "cell_type": "markdown", - "id": "df74f52e-fdf7-4b82-b797-297bd4a139a7", - "metadata": {}, - "source": [ - "#### 4.1 Load phenotypic data" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "b11ae446-27b5-4e18-b3bb-9553acbb397c", - "metadata": {}, - "outputs": [], - "source": [ - "# load phenotype data from excel file\n", - "pt_data = pd.read_excel(os.path.join(DATA_DIR, 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'), sheet_name='Yields', index_col=None)\n", - "pt_data['EX_glc__D_e'] = -pt_data['EX_glc__D_e']" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "8ce6c775-f714-4534-91ce-9c4cfd4e73c2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Software/anaconda3/envs/PAModelpy/lib/python3.9/site-packages/IPython/core/events.py:82: UserWarning: constrained_layout not applied because axes sizes collapsed to zero. Try making figure larger or axes decorations smaller.\n", - " func(*args, **kwargs)\n", - "/home/samiralvdb/Software/anaconda3/envs/PAModelpy/lib/python3.9/site-packages/IPython/core/pylabtools.py:152: UserWarning: constrained_layout not applied because axes sizes collapsed to zero. Try making figure larger or axes decorations smaller.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#create 1 big plot\n", - "fontsize = 26\n", - "vmin =-1\n", - "vmax=1\n", - "width = 40\n", - "height = 15\n", - "cmap = None\n", - "\n", - "# gridspec inside gridspec\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "gs0 = gridspec.GridSpec(1, 14, figure=fig, wspace = 25)\n", - "gs_pam = gs0[:6]\n", - "gs_inc = gs0[7:]\n", - "\n", - "#adjust labels for better readibility\n", - "x_csc_label_pam = adjust_heatmap_labels(x_csc_top5_pam)\n", - "x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam)\n", - "\n", - "\n", - "fig_pam = make_heatmap_subfigure(results = results_pam, csc_matrix=csc_top5_pam, esc_matrix =esc_top5_pam,cbar =False,title = 'WT PAM',\n", - " x_csc=x_csc_label_pam, x_esc=x_esc_label_pam, yaxis = eGFP_RANGE, fig = fig, grdspc = gs_pam,\n", - " annotate = 'A', vmin = vmin, vmax=vmax, fontsize = fontsize)\n", - "\n", - "# adjust labels for better readibility\n", - "x_csc_label_atp = adjust_heatmap_labels(x_csc_top5_atp)\n", - "x_esc_label_atp = adjust_heatmap_labels(x_esc_top5_atp)\n", - "\n", - "\n", - "fig_inc = make_heatmap_subfigure(results = results_atp, csc_matrix=csc_top5_atp, esc_matrix =esc_top5_atp, ylabels = False,\n", - " title = 'ATP synthase efficient PAM', x_csc=x_csc_label_atp, x_esc=x_esc_label_atp, yaxis = eGFP_RANGE, \n", - " fig = fig, grdspc=gs_inc, annotate = 'B', vmin = vmin, vmax=vmax, fontsize = fontsize)\n", - "\n", - "#set common x axis title\n", - "ax_xlabel = fig.add_subplot(gs0[0, :2])\n", - "ax_xlabel.set_xticks([])\n", - "ax_xlabel.set_yticks([])\n", - "ax_xlabel.set_frame_on(False)\n", - "ax_xlabel.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}/h$]', fontsize = fontsize*1.25)\n", - "ax_xlabel.xaxis.set_label_coords(6, -.15)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "# fig.savefig('Figure3_sensitivities_protein-overproduction.png', dpi =200,bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "7dc9eac9", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_84559/886489897.py:25: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# 2 separate plots: 1 main text, 1 supplements\n", - "fontsize = 26\n", - "vmin =-1\n", - "vmax=1\n", - "width = 20\n", - "height = 13\n", - "cmap = None\n", - "\n", - "\n", - "#supplemental figure (GECKO)\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs = gridspec.GridSpec(1, 1, figure=fig)\n", - "#adjust labels for better readibility\n", - "x_csc_label_atp = adjust_heatmap_labels(x_csc_top5_atp)\n", - "x_esc_label_atp = adjust_heatmap_labels(x_esc_top5_atp)\n", - "\n", - "\n", - "fig_inc = make_heatmap_subfigure(results = results_atp, csc_matrix=csc_top5_atp, esc_matrix =esc_top5_atp, \n", - " ylabels = True, xlabels = True, x_csc=x_csc_label_atp, x_esc=x_esc_label_atp, \n", - " yaxis = eGFP_RANGE, fig = fig, grdspc=gs[0], vmin = vmin, vmax=vmax, \n", - " fontsize = fontsize, cmap = cmap)\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/SuppFigure5_sensitivities_protein-overproduction_atp.png', dpi =300, bbox_inches='tight')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "44a1ed1c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_84559/3854418348.py:24: UserWarning: This figure was using a layout engine that is incompatible with subplots_adjust and/or tight_layout; not calling subplots_adjust.\n", - " fig.subplots_adjust(left=0.3)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#supplemental figure: mutant strain\n", - "fontsize = 26\n", - "vmin =-1\n", - "vmax=1\n", - "width = 20\n", - "height = 13\n", - "cmap = None\n", - "\n", - "\n", - "#supplemental figure (GECKO)\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "\n", - "gs = gridspec.GridSpec(1, 1, figure=fig)\n", - "#adjust labels for better readibility\n", - "x_csc_label_pam = adjust_heatmap_labels(x_csc_top5_pam)\n", - "x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam)\n", - "\n", - "fig_pam = make_heatmap_subfigure(results = results_pam, csc_matrix=csc_top5_pam, esc_matrix =esc_top5_pam,cbar =True,\n", - " x_csc=x_csc_label_pam, x_esc=x_esc_label_pam, yaxis = eGFP_RANGE, xlabels = True,\n", - " fig = fig, grdspc = gs[0], vmin = vmin, vmax=vmax, fontsize = fontsize, \n", - " cmap = cmap)\n", - "plt.plasma()\n", - "fig.subplots_adjust(left=0.3)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.align_labels()\n", - "\n", - "fig.savefig('Figures/Figure3_sensitivities_protein-overproduction_wt.png', dpi =300, bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "id": "5acc414c-f74f-40a4-8017-9cd24f5cfee2", - "metadata": {}, - "source": [ - "## 4.2 plot normalized growth rate as function of eGFP concentration\n", - "Similar to what has been done by [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "316180b7-a40d-4e3d-8a65-4b08c9aa6ab1", - "metadata": {}, - "outputs": [], - "source": [ - "#get eGFP data from Bienick et al. (2014)\n", - "egfp_exp = pd.read_excel(eGFP_BEINICK_DATA_PATH, sheet_name='eGFPvsMu')\n", - "mu_wt = 0.75\n", - "\n", - "egfp_exp['mu_normalized'] = egfp_exp['Growth rate'].apply(lambda x: x/mu_wt)\n", - "egfp_exp['mu_error_normalized'] = egfp_exp['Growth rate error'].apply(lambda x: x/mu_wt)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "b13a70c0-e06b-4746-a620-7308de72b7b7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots()\n", - "ax.plot(results_pam['results']['eGFP'], results_pam['results']['mu_normalized'], label = 'default PAM')\n", - "ax.plot(results_inc['results']['eGFP'], results_inc['results']['mu_normalized'], label = 'PAM with $\\phi_{E} = 0.31 g_{P}/g_{CDW}$')\n", - "ax.plot(results_atp['results']['eGFP'], results_atp['results']['mu_normalized'], label = 'PAM with $2 \\cdot k_{cat,ATPsynt}$')\n", - "\n", - "ax.scatter(egfp_exp['eGFP concentration'], egfp_exp['mu_normalized'],\n", - " color='purple', marker='o', s=30, linewidths=1.3,\n", - " facecolors=None, zorder=0,\n", - " label='Bienick et al. (2014)')\n", - "ax.errorbar(egfp_exp['eGFP concentration'], egfp_exp['mu_normalized'], \n", - " yerr= egfp_exp['mu_error_normalized'], xerr = egfp_exp['eGFP concentration error'],\n", - " fmt=\"o\", color='purple')\n", - "\n", - "\n", - "# Set the tick labels font\n", - "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n", - " label.set_fontsize(15)\n", - "ax.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}$]', fontsize = 15)\n", - "ax.set_ylabel('normalized growth rate', fontsize =15)\n", - "\n", - "ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1)\n", - "\n", - "\n", - "plt.show()\n", - "\n", - "fig.savefig('Figures/SuppFigure6_eGFP-normalized_mu.png', dpi =1200, bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "id": "9e43d84f-2d6e-418e-a7b7-9c05fc1018e3", - "metadata": {}, - "source": [ - "## 4.3 plot predicted exchange rates\n", - "Similar to what has been done by [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "14fe29c7-81a3-4a80-84f4-19d90375ef08", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_84559/744768918.py:48: UserWarning: The figure layout has changed to tight\n", - " plt.tight_layout()\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 10\n", - "\n", - "#plot flux changes with eGFP concentration\n", - "import matplotlib.transforms as mtransforms\n", - "fig, axs = plt.subplot_mosaic([['A', 'B'], ['C', 'D'], ['E', '']],\n", - " layout='constrained', dpi =100)\n", - "for label, ax in axs.items():\n", - " # label physical distance to the left and up:\n", - " trans = mtransforms.ScaledTranslation(-20/72, 7/72, fig.dpi_scale_trans)\n", - " ax.text(0.0, 1.0, label, transform=ax.transAxes + trans,\n", - " fontsize='medium', va='bottom', fontfamily='serif', weight = 'bold')\n", - "\n", - "rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'EX_pyr_e', BIOMASS_RXNID]\n", - "ylabels = ['Acetate excretion [$mmol_{ac}/g_{CDW}/h$]', 'CO2 excretion [$mmol_{CO2}/g_{CDW}/h$]', \n", - " 'Oxygen uptake [$mmol_{O2}/g_{CDW}/h]$', 'Pyruvate excretion [$mmol_{for}/g_{CDW}/h$]','Growth rate [$h^{-1}$]']\n", - "# fig, axs = plt.subplots(2,2, dpi=100)\n", - "for i,r in enumerate(rxn_id):\n", - " ax_label = ['A', 'B', 'C', 'D', 'E'][i]\n", - " ax = axs[ax_label]\n", - " if r != BIOMASS_RXNID:\n", - " ax.set_ylim([0,27])\n", - " # plot simulation\n", - " line_pam = ax.plot(eGFP_RANGE, [abs(f[r]) for f in results_pam['fluxes']], linewidth=2.5,\n", - " zorder=5, color ='#440154')\n", - " \n", - " # plot simulation with increases protein capacity\n", - " line_atp = ax.plot(eGFP_RANGE, [abs(f[r]) for f in results_atp['fluxes']],linewidth=2.5,\n", - " zorder=5, color = '#21918c')\n", - " \n", - " \n", - " # options\n", - " ax.set_xlabel('eGFP concentration [$g_{eGFP}/g_{CDW}$]', fontsize = fontsize)\n", - " ax.set_ylabel(ylabels[i], fontsize = fontsize)\n", - " # set grid\n", - " ax.grid(True, axis='both', linestyle='--', linewidth=0.5, alpha=0.6 )\n", - " ax.set_axisbelow(True)\n", - " # show legend\n", - " # ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1)\n", - " \n", - "axs[''].axis('off')\n", - "# Manually create legend handles (patches)\n", - "blue_patch = matplotlib.patches.Patch(color='#440154', label='default PAM')\n", - "orange_patch = matplotlib.patches.Patch(color='#21918c', label='PAM with $2 \\cdot k_{cat,ATPsynt}$')\n", - "\n", - "# Add legend to bottom-right ax\n", - "axs[''].legend(handles=[blue_patch, orange_patch], loc='center', fontsize = fontsize+7)\n", - "\n", - "plt.tight_layout()\n", - "plt.subplots_adjust(wspace=0.2, hspace=0.2)\n", - "fig.set_figheight(13)\n", - "fig.set_figwidth(10)\n", - "fig.savefig('Figures/SuppFigure4_simulated-physiology.png', dpi =1200, bbox_inches='tight' )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8a2977be-a4c9-4c75-9369-cd210d95bd0d", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAModelpy", - "language": "python", - "name": "pamodelpy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/Figure3_sensitivities_protein-overproduction_wt.png b/Figures/Figure3_sensitivities_protein-overproduction_wt.png deleted file mode 100644 index 07a7839..0000000 Binary files a/Figures/Figure3_sensitivities_protein-overproduction_wt.png and /dev/null differ diff --git a/Figures/SuppFigure1_ESC-distributions.png b/Figures/SuppFigure1_ESC-distributions.png deleted file mode 100644 index cf10df4..0000000 Binary files a/Figures/SuppFigure1_ESC-distributions.png and /dev/null differ diff --git a/Figures/SuppFigure1_VSC-distributions.ipynb b/Figures/SuppFigure1_VSC-distributions.ipynb deleted file mode 100644 index 8145546..0000000 --- a/Figures/SuppFigure1_VSC-distributions.ipynb +++ /dev/null @@ -1,292 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "103b5cd5-1305-46a1-b1b5-02f13237c53c", - "metadata": {}, - "source": [ - "# Code to generate Supplemtary Figure 1 in the publication\n", - "Distribution of variable sensitivity coefficients and finite difference coefficients for the core *E.coli* Protein Allocation Model (PAM)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "6b112820-0936-4550-8972-24673f8ee8dc", - "metadata": {}, - "outputs": [], - "source": [ - "from matplotlib import pyplot as plt\n", - "import matplotlib\n", - "import matplotlib.gridspec as gridspec\n", - "\n", - "import pandas as pd\n", - "import numpy as np\n", - "import os\n", - "\n", - "if os.path.split(os.getcwd())[1] == 'Figures':\n", - " os.chdir('..')\n", - " \n", - "from Scripts.pam_generation import set_up_ecolicore_pam, parse_esc, set_up_ecoli_pam\n", - "\n", - "from Scripts.numeric_error_estimation_schemes_esc import (first_central_numeric_esc_optimizations,\n", - " fcc_numeric_esc_optimizations,\n", - " first_central_numeric_esc_calculation, fcc_numeric_esc_calculation)\n", - "\n", - "GLC_UPTAKE = 9.81 #mmol/gcdw/h\n", - "RESULT_DIR = 'Results'" - ] - }, - { - "cell_type": "markdown", - "id": "dee026a3-d8aa-41f0-9b54-2a9d02d7bba1", - "metadata": {}, - "source": [ - "## 1. set up *E.coli* core PAM" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "a642c93c-cfba-45d2-8d40-769c26b44c75", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No enzyme information found for reaction: FRD7\n", - "Read LP format model from file /tmp/tmpbzy54o7p.lp\n", - "Reading time = 0.00 seconds\n", - ": 72 rows, 190 columns, 720 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy/src/PAModelpy/EnzymeSectors.py:300: UserWarning: FORt: reaction directionality does not match provided kcat values. Skip reaction\n", - " warn(\n" - ] - } - ], - "source": [ - "ecolicore_pam = set_up_ecolicore_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "d1d983b9-2d6f-4a5f-b5e8-138367f318df", - "metadata": {}, - "source": [ - "## 2. Calculate sensitivity coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "04130b6d-6035-4614-b824-952a93252372", - "metadata": {}, - "outputs": [], - "source": [ - "#set glucose uptake rate in the ecoli models to 9.81 for reproducible results\n", - "ecolicore_pam.change_reaction_bounds(rxn_id = 'EX_glc__D_e',\n", - " lower_bound = -GLC_UPTAKE, upper_bound = -GLC_UPTAKE)\n", - "\n", - "ecolicore_pam.optimize()\n", - "#calculate flux control coefficients\n", - "fcc_esc = fcc_numeric_esc_optimizations(ecolicore_pam)\n", - "ecolicore_pam.optimize()\n", - "#calculate first order central difference coefficients\n", - "fcn_esc = first_central_numeric_esc_optimizations(ecolicore_pam)\n", - "ecolicore_pam.optimize()\n", - "#calculate enzyme variable sensitivity coefficients\n", - "Cesc = parse_esc(ecolicore_pam)" - ] - }, - { - "cell_type": "markdown", - "id": "92640927-cc99-4a08-ab24-8e2599049635", - "metadata": {}, - "source": [ - "## 3. Plot distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "4129e80b-4f6a-4df9-ab48-e287a2fa7ca2", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_log_hist(axes, data, logbins, fontsize = 16, color = 'blue', annotate = None):\n", - " #add annotation for subfigure (A or B)\n", - " if annotate is not None:\n", - " axes.annotate(annotate, xy=(2, 1), xycoords='data',\n", - " xytext=(-0.05,1.1), textcoords='axes fraction',\n", - " va='top', ha='left', fontsize = fontsize+5, weight = 'bold')\n", - " \n", - " axes.hist(data, bins=logbins, color = color, alpha = 0.5)\n", - " axes.tick_params(axis='x', labelsize=fontsize)\n", - " axes.tick_params(axis='y', labelsize=fontsize)\n", - " axes.set_ylabel('Frequency', fontsize = fontsize)\n", - " axes.set_xscale('log')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "66ce6aff-3956-4dd3-a7dc-302c52cba10c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_80078/1534277179.py:35: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 30\n", - "width =25\n", - "height =10\n", - "numbins =50\n", - "\n", - "fig = plt.figure(layout = 'constrained')\n", - "\n", - "#set 3 grids for the three methods, with individual axes\n", - "gs0 = gridspec.GridSpec(1, 3, figure=fig)\n", - "gs_fcc = gs0[0]\n", - "gs_fcn = gs0[1]\n", - "gs_esc = gs0[2]\n", - "\n", - "ax_fcc = fig.add_subplot(gs_fcc)\n", - "ax_fcn = fig.add_subplot(gs_fcn)\n", - "ax_esc = fig.add_subplot(gs_esc)\n", - "\n", - "#plot logarithmic histograms to see the distribution of the calculated coefficients\n", - "logbins = np.logspace(np.log10(1e-9),np.log10(1e4), numbins)\n", - "plot_log_hist(ax_fcc, fcc_esc, logbins, fontsize = fontsize, color = 'darkgreen', annotate = 'A')\n", - "plot_log_hist(ax_fcn, fcn_esc, logbins, fontsize = fontsize, annotate = 'B')\n", - "plot_log_hist(ax_esc, Cesc, logbins, fontsize = fontsize, color = 'orange', annotate = 'C')\n", - "\n", - "#set common xlabel\n", - "ax_xlabel = fig.add_subplot(gs0[0, :2])\n", - "ax_xlabel.set_xticks([])\n", - "ax_xlabel.set_yticks([])\n", - "ax_xlabel.set_frame_on(False)\n", - "ax_xlabel.set_xlabel('Enzyme Sensitivity coefficients', fontsize = fontsize*1.25)\n", - "ax_xlabel.xaxis.set_label_coords(0.75, -.15)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "fig.savefig('SuppFigure1_ESC-distributions.png', dpi =1200,bbox_inches='tight')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "16a5425d-8c24-405a-925c-dcdfb00030ee", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_80078/218524460.py:22: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", - " fig.show()\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fontsize = 16\n", - "width =10\n", - "height =5\n", - "numbins =50\n", - "\n", - "fig,ax = plt.subplots()\n", - "\n", - "#plot logarithmic histograms to see the distribution of the calculated coefficients\n", - "logbins = np.logspace(np.log10(1e-9),np.log10(1e4), numbins)\n", - "plt.hist(fcc_esc, bins=logbins, alpha =0.5, histtype='step', fill =False, label = 'flux control coefficient')\n", - "plt.hist(fcn_esc, bins=logbins, alpha =0.5, histtype='step',fill =False,label = 'finite central \\ndifference coefficient')\n", - "plt.hist(Cesc, bins=logbins, alpha =0.4, histtype='step',fill =False,label = 'enzyme sensitivity coefficient', color ='red')\n", - "plt.xscale('log')\n", - "plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize = fontsize)\n", - "\n", - "plt.xticks(fontsize = fontsize)\n", - "plt.yticks(fontsize = fontsize)\n", - "\n", - "fig.set_figwidth(width)\n", - "fig.set_figheight(height)\n", - "# fig.savefig('SuppFigure1_ESC-distributions.png')\n", - "fig.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "83a02342-fde5-43b2-9f21-94419fbcefd6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAModelpy", - "language": "python", - "name": "pamodelpy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Figures/SuppFigure2_sensitivities_gecko.png b/Figures/SuppFigure2_sensitivities_gecko.png deleted file mode 100644 index 081b5ef..0000000 Binary files a/Figures/SuppFigure2_sensitivities_gecko.png and /dev/null differ diff --git a/Figures/SuppFigure3_proteomap_file_generation.py b/Figures/SuppFigure3_proteomap_file_generation.py deleted file mode 100644 index b4ccb7e..0000000 --- a/Figures/SuppFigure3_proteomap_file_generation.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -import pandas as pd -from Scripts.pam_generation import set_up_ecoli_pam - - -# NOTE:RUN THIS SCRIPT FROM THE MAIN DIRECTORY IN THE COMMAND LINE. -# otherwise the relative paths set in this script will result in errors - -def calculate_sensitivities(pamodel): - glc =10 - - # disable pyruvate formate lyase (inhibited by oxygen) - pamodel.change_reaction_bounds(rxn_id='PFL', upper_bound=0) - - print('glucose uptake rate ', glc, ' mmol/gcdw/h') - with pamodel: - # change glucose uptake rate - pamodel.change_reaction_bounds(rxn_id='EX_glc__D_e', - lower_bound=-glc, upper_bound=-glc) - # solve the model - pamodel.optimize() - if pamodel.solver.status == 'optimal': - genes = [] - enzyme_coeff = pamodel.enzyme_sensitivity_coefficients - Cesc = enzyme_coeff.coefficient.to_list() - - #get the first gene from the first reaction as this all the reactions and genes in one row relate to the same enzyme - for i,rxn in enumerate(enzyme_coeff.rxn_id.to_list()): - rxn_genes = pamodel.reactions.get_by_id(rxn.split(',')[0])._genes - if all(gene.id in genes for gene in rxn_genes): - gene = rxn_genes.pop() - genes.append(gene.id) - - else: - for gene in rxn_genes: - if gene.id not in genes: - genes.append(gene.id) - break - - - print('Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t', round(sum(Cesc), 6), '\n') - return { 'genes': genes,'Cesc': Cesc} - -if __name__ == '__main__': - result = calculate_sensitivities(set_up_ecoli_pam()) - result_df = pd.DataFrame(result) - result_df = result_df.groupby('genes', as_index=False)['Cesc'].sum() - - result_df.to_csv('Results/sensitivity_coefficient_proteomap_ecoli_vs10.tsv',sep = '\t', - header=False, index = False) diff --git a/Figures/SuppFigure3_proteomaps_ecolipam.png b/Figures/SuppFigure3_proteomaps_ecolipam.png deleted file mode 100644 index 657b84a..0000000 Binary files a/Figures/SuppFigure3_proteomaps_ecolipam.png and /dev/null differ diff --git a/Figures/SuppFigure4_simulated-physiology.png b/Figures/SuppFigure4_simulated-physiology.png deleted file mode 100644 index 927b6d0..0000000 Binary files a/Figures/SuppFigure4_simulated-physiology.png and /dev/null differ diff --git a/Figures/SuppFigure5_sensitivities_protein-overproduction_atp.png b/Figures/SuppFigure5_sensitivities_protein-overproduction_atp.png deleted file mode 100644 index e6034f7..0000000 Binary files a/Figures/SuppFigure5_sensitivities_protein-overproduction_atp.png and /dev/null differ diff --git a/Figures/SuppFigure6_eGFP-normalized_mu.png b/Figures/SuppFigure6_eGFP-normalized_mu.png deleted file mode 100644 index bc86d58..0000000 Binary files a/Figures/SuppFigure6_eGFP-normalized_mu.png and /dev/null differ diff --git a/MATLAB/e-coli_core/e_coli_core.mat b/MATLAB/e-coli_core/e_coli_core.mat deleted file mode 100644 index 5805065..0000000 Binary files a/MATLAB/e-coli_core/e_coli_core.mat and /dev/null differ diff --git a/MATLAB/e-coli_core/enzymatic_data.mat b/MATLAB/e-coli_core/enzymatic_data.mat deleted file mode 100644 index 57d9dbb..0000000 Binary files a/MATLAB/e-coli_core/enzymatic_data.mat and /dev/null differ diff --git a/MATLAB/e-coli_core/enzymatic_data.xls b/MATLAB/e-coli_core/enzymatic_data.xls deleted file mode 100644 index 87651af..0000000 Binary files a/MATLAB/e-coli_core/enzymatic_data.xls and /dev/null differ diff --git a/MATLAB/e-coli_core/loadModel.m b/MATLAB/e-coli_core/loadModel.m deleted file mode 100644 index 30c3e9e..0000000 --- a/MATLAB/e-coli_core/loadModel.m +++ /dev/null @@ -1,98 +0,0 @@ -clc, clearvars - -load e_coli_core.mat - -% Reduce model manually -nRxns = numel(model.rxns); -lb = zeros(nRxns,1); -ub = lb; -model.c = ub; -vTol = 1e-8; % flux tolerance -for ix = 1:nRxns - model.c(ix) = 1; - solMin = optimizeCbModel(model,'min'); - solMax = optimizeCbModel(model,'max'); - if abs(solMin.f)>vTol - lb(ix) = solMin.f; - end - if abs(solMax.f)>vTol - ub(ix) = solMax.f; - end - model.c(ix) = 0; -end - -% Find zero reactions -ixZeroRxns = (abs(lb)==0)&(abs(ub)==0); -rxns = model.rxns; -S = model.S; -rxns(ixZeroRxns) = []; -lb(ixZeroRxns) = []; -ub(ixZeroRxns) = []; -S(:,ixZeroRxns) = []; -orphanMets = all(S==0,2); -S(orphanMets,:) = []; - -% Figure out irreversible rxns -rev = sign(lb).*sign(ub); -rev(rev~=-1) = 0; -rev(rev==-1) = 1; -revRxns = rxns(rev==1); - -% Load enzymatic data -load enzymatic_data.mat -MW = MW/1e3; % g E/mmol E -kcat = kcat*3600; % mmol S/mmol E/h -keff = kcat./MW; % mmol S/g E/h - -% Pre-process enzyme Ids -hits = 0; -keff_temp = nan(numel(rxns),1); -for jx = 1:numel(rxnIds) - - testID = regexp(rxnIds{jx},'_f','split'); - testID = regexp(testID{1},'_b','split'); - - % Map data - hit = strcmp(rxns,testID{1}); - if any(hit) -% [testID{1},rxns{hit}] - keff_temp(hit) = keff(jx); - hits = hits+1; - end -end -hits - -% Make model irreversible manually -for ix = 1:numel(rev) - if rev(ix)==1 - keff_temp(end+1) = keff_temp(ix); - S(:,end+1) = -S(:,ix); - ub(end+1) = -lb(ix); % make positve upper bound - lb(end+1) = 0; % make new rxn irreversible - lb(ix) = 0; % make original rxn irrversible - rxns{end+1} = [rxns{ix},'_neg']; - else - if lb(ix)<0 - lb_temp = lb(ix); - lb(ix) = -ub(ix); - ub(ix) = -lb_temp; - S(:,ix) = -S(:,ix); - end - end -end -keff = keff_temp; -table(rxns,keff_temp,lb,ub) - -% % Test problem -% problem.A=sparse(S); -% problem.lb=lb; -% problem.ub=ub; -% problem.obj=zeros(numel(lb),1); -% problem.sense='='; -% problem.modelsense='max'; -% problem.obj(25)=1; -% sol=gurobi(problem) - -% Extract fields for later use -clearvars -except S lb ub keff rxns -save model_data \ No newline at end of file diff --git a/MATLAB/e-coli_core/model_data.mat b/MATLAB/e-coli_core/model_data.mat deleted file mode 100644 index 273d872..0000000 Binary files a/MATLAB/e-coli_core/model_data.mat and /dev/null differ diff --git a/MATLAB/e-coli_core/test_ecoli_core.m b/MATLAB/e-coli_core/test_ecoli_core.m deleted file mode 100644 index 0aee422..0000000 --- a/MATLAB/e-coli_core/test_ecoli_core.m +++ /dev/null @@ -1,262 +0,0 @@ - % Protein allocation formulation (without translation sector) -% -% --------------------- Pedro Saa UC 2023 ---------------------------------- -clc,clearvars,close all - -% Show results -showResults = 3; % 1,2 or 3 - -% Let us consider the following LP problem -% max. vi -% s.t. -% S*v = 0 -% Kin*v - E = 0 -% v <= ub -% -v <= -lb -% E <= E_max -% -E <= -E_min -% wUE*v + sum(Ei) <= phi_P0 - phi_U0 - phi_T0 -% Variables -% v_i, E_i -% - -%% Load data -load model_data.mat - -%% Initialize enzymatic parameters (from excel) -phi_P0 = .14; % total enzyme pool -phi_U0 = 0; % unused enzyme pool -phi_T0 = 0.049920; % translation pool -phi_E0 = phi_P0-phi_U0-phi_T0; % net enzyme pool -enz = sum(~isnan(keff)); % mapped enzymes -rxnID = rxns; - -% Newtork parameters -[m,n] = size(S); -Kinv = diag(keff.^-1); -Kinv(isnan(keff),:) = []; - -% Index of important reactions -idBio = 25; -idGlc = 50; - -% Parameters of the translation sector -wT = zeros(1,n); -wT(idBio) = 0.002944; % g*h/gDCW - -% Parameters of the unused enzyme sector -wUE = zeros(1,n); -wUE(idGlc) = 0; - -w = wUE + wT; - -% Definition of enzyme bounds -Emin = zeros(enz,1); -Emax = phi_E0*ones(enz,1); - -% Set up optimization problem -model.obj= zeros(n+enz,1); % Null objective -model.A = sparse([S,zeros(m,enz);... % Coefficients matrix - Kinv,-eye(enz);... - eye(n),zeros(n,enz);... - -eye(n),zeros(n,enz);... - zeros(enz,n),eye(enz);... - zeros(enz,n),-eye(enz);... - w,ones(1,enz)]); -b = [zeros(m,1);zeros(enz,1);ub;-lb;Emax;-Emin;phi_E0]; % Right-hand side definition -LB = -1e6*ones(n+enz,1); % Bounds definition (unconstrained) -UB = 1e6*ones(n+enz,1); - -% Assign values to the gurobi variable structure -model.rhs = b; % Right-hand side -model.lb = LB; % Bounds -model.ub = UB; - -% Define constraint and model sense -for ix = 1:size(model.A,1) - if ix <= m+enz - model.sense(ix) = '='; - else - model.sense(ix) = '<'; - end -end -model.modelsense = 'max'; % Model sense -model.vtype = 'C'; % Variable type - -% Set optimization parameters -params.FeasibilityTol = 1e-9; -params.OptimalityTol = 1e-9; -params.OutputFlag = 0; % Gurobi parameter - - -%% Main loop for Capacity Allocation Coeficients calculations -Crhs = []; -bdual = b(m+enz+1:end); -for jx = 1:n - - % Objective vector - model.obj(jx) = 1; - - % Solve model - sol = gurobi(model,params); - - % Extract solution - vz = sol.objval; % optimal objective value - mu = sol.pi(m+enz+1:end); % shadow prices of inequality constraints - - % Calculate control coefficients - Ctemp = (bdual.*mu)'/vz; - Crhs = [Crhs;Ctemp]; - - % Show results for the biomass optimization - if jx==idBio - disp('Max. biomass growth') - opt_mu = sol.objval - nonZeros = (abs(Ctemp)>1e-8); - disp('Non-zero Capacity Control Coefficients') - Ctemp(nonZeros) - disp('Capacity Control Coefficients sum') - sum(Ctemp) - end - - % Clear objetive vector - model.obj(jx) = 0; -end - -% Check summation result -% assert(all(max(abs(sum(Crhs,2)-1))<1e-6)) -if showResults==1 - disp('% Deviation from theoretical result') - control_sum = sum(Crhs,2); - dev_from_theory = 100*abs(sum(Crhs,2)-1); - table(rxns,control_sum,dev_from_theory) -end - -% Plot heatmap -figure(1) -subplot(3,1,1) -h = heatmap(Crhs); -colormap jet -h.XDisplayLabels=repmat({''},numel(h.XDisplayLabels),1); -h.YDisplayLabels=repmat({''},numel(h.YDisplayLabels),1); -h.Title = 'Capacity Control Coefficients'; -xlabel('Right-hand side (Capacity parameter)') -ylabel('Reaction') - -%% Main loop for Flux Control Coefficients -Cvars = []; -for jx = 1:n - - % Objective vector - model.obj(jx) = 1; - - % Solve model - sol = gurobi(model,params); - - % Extract solution - vz = sol.objval; % optimal objective value - v = sol.x(1:n); % optimal primal vector (fluxes) - e = sol.x(n+1:end); % optimal primal vector (enzymes) - mu_e = sol.pi(m+1:m+enz); % shadow prices of the E-constraints - mu_ub = sol.pi(m+enz+1:m+enz+n); % shadow prices of the mu_UB - mu_lb = sol.pi(m+enz+n+1:m+enz+2*n); % shadow prices of the mu_LB - mu_E = sol.pi(end); % shadow price of the total enzyme pool - - % Calculate control coefficients - Ctemp = [(mu_ub-mu_lb+mu_E*wUE').*v;mu_e.*e]/vz; - Cvars = [Cvars;Ctemp']; - - % Show results for the biomass optimization - if jx==idBio - disp('Max. biomass growth') - opt_mu = sol.objval - nonZeros = (abs(Ctemp)>1e-8); - disp('Non-zero Flux Control Coefficients') - Ctemp(nonZeros) - disp('Flux Control Coefficients sum') - sum(Ctemp) - end - - % Clear objetive vector - model.obj(jx) = 0; -end - -% Check summation result -if showResults==2 - disp('% Deviation from theoretical results') - control_sum = sum(Cvars,2); - dev_from_theory = 100*abs(sum(Cvars,2)-1); - table(rxnID,control_sum,dev_from_theory); -end - -% Plot heatmap -figure(1) -subplot(3,1,2) -h = heatmap(Cvars); -colormap jet -h.XDisplayLabels=repmat({''},numel(h.XDisplayLabels),1); -h.YDisplayLabels=repmat({''},numel(h.YDisplayLabels),1); -h.Title = 'Flux Control Coefficients'; -xlabel('Decision variable (flux, enz)') -ylabel('Reaction') - -%% Main loop Connectivity Relationships for enzyme-related dual variables -Cvars = []; -for jx = 1:n - - % Objective vector - model.obj(jx) = 1; - - % Solve model - sol = gurobi(model,params); - - % Extract solution - vz = sol.objval; % optimal objective value - v = sol.x(1:n); % optimal primal vector (fluxes) - e = sol.x(n+1:end); % optimal primal vector (enzymes) - alpha = sum(e)/phi_E0; % utilized enzyme fraction - mu_e = sol.pi(m+1:m+enz); % shadow prices of the E-constraints - mu_ub = sol.pi(m+enz+1:m+enz+n); % shadow prices of the mu_UB - mu_lb = sol.pi(m+enz+n+1:m+enz+2*n); % shadow prices of the mu_LB - e_max = sol.pi(m+2*n+enz+1:m+2*n+2*enz); % shadow prices of the epsilon_max - e_min = sol.pi(m+2*n+2*enz+1:m+2*n+3*enz); % shadow prices of the epsilon_min - lambda = sol.pi(1:m); - pi = sol.pi(end); - - % Calculate control coefficients - Ctemp = [e.*(-mu_e + e_max - e_min);alpha*pi]/vz; - Cvars = [Cvars;Ctemp']; - - % Show results for the biomass optimization - if jx==idBio - disp('Max. biomass growth') - opt_mu = sol.objval - nonZeros = (abs(Ctemp)>1e-8); - disp('Non-zero Enzyme Allocation Coefficients') - Ctemp(nonZeros) - disp('Enzyme Allocation Coefficients sum') - sum(Ctemp) - end - - % Clear objetive vector - model.obj(jx) = 0; -end - -% Check summation result -if showResults==3 - disp('Deviation from theoretical results') - control_sum = sum(Cvars,2); - dev_from_theory = sum(Cvars,2); - table(rxnID,control_sum,dev_from_theory) -end - -% Plot heatmap -figure(1) -subplot(3,1,3) -h = heatmap(Cvars); -colormap jet -h.XDisplayLabels=repmat({''},numel(h.XDisplayLabels),1); -h.YDisplayLabels=repmat({''},numel(h.YDisplayLabels),1); -h.Title = 'Enzyme Allocation Coefficients'; -xlabel('Enzymes + Total enzyme pool') -ylabel('Reaction') \ No newline at end of file diff --git a/MATLAB/toyModel_paper.m b/MATLAB/toyModel_paper.m deleted file mode 100644 index 2ee33ce..0000000 --- a/MATLAB/toyModel_paper.m +++ /dev/null @@ -1,154 +0,0 @@ -% Protein allocation formulation (Toy Model) -% -% --------------------- Pedro Saa UC 2023 ---------------------------------- -clc,clearvars,close all - -% Let us consider the following LP problem -% max. vi -% s.t. -% S*v = 0 -% Kin*v - E = 0 -% v <= ub -% -v <= -lb -% E <= E_max -% -E <= -E_min -% sum(Ei) + w'*v <= phi_E0 -% Variables -% v_i, E_i -% - -%% Model set up -% Stoichiometric matrix definition -% sub int byp atp co2 pre bio -R1 = [ 1, 0, 0, 0, 0, 0, 0]; -R2 = [-1, 1, 0, 0, 1, 0, 0]; -R3 = [ 0, -1, 1, 1, 0, 0, 0]; -R3r= -R3; -R4 = [ 0, -1, 0, 2, 1, 0, 0]; -R5 = [ 0, -1, 0, 0, 0, 1, 0]; -R6 = [ 0, 0, 0, -1, 0, -1, 1]; -R7 = [ 0, 0, 0, 0, 0, 0, -1]; -R8 = [ 0, 0, 0, 0, -1, 0, 0]; -R9 = [ 0, 0, -1, 0, 0, 0, 0]; -S = [R1;R2;R3;R3r;R4;R5;R6;R7;R8;R9]'; - -% Additional enzymatic parameters -phi_P0 = 0.60; % total enzyme pool -phi_U0 = 0.10; % unused enzyme pool -phi_T0 = 0.01; % translation sector -phi_E0 = phi_P0-phi_U0-phi_T0; % net enzyme pool -kcat = [1, 0.5, 1, 1, 0.5, 0.45, 1.5]; % catalytic turnovers -enz = numel(kcat); - -% Newtork parameters -[m,n] = size(S); -Kinv = [diag(kcat.^-1),zeros(enz,n-enz)]; -w = [-0.01, 0, 0, 0, 0, 0, 0, 0.01, 0, 0]; % flux weights allocation - -% Definition of reaction bounds -lb = zeros(n,1); -ub = 1e2*ones(n,1); -Emin = zeros(enz,1); -Emax = phi_E0*ones(enz,1); - -% Set up optimization problem -params.OutputFlag = 0; % Gurobi parameter -model.obj = zeros(n+enz,1); % Null objective -model.A = sparse([S,zeros(m,enz);... % Coefficients matrix - Kinv,-eye(enz);... - eye(n),zeros(n,enz);... - -eye(n),zeros(n,enz);... - zeros(enz,n),eye(enz);... - zeros(enz,n),-eye(enz);... - w,ones(1,enz)]); -b = [zeros(m+enz,1);ub;-lb;Emax;-Emin;phi_E0]; % Right-hand side definition -LB = zeros(n+enz,1); % Bounds definition (unconstrained) -UB = 1e3*ones(n+enz,1); -model.rhs = b; % Right-hand side -model.lb = LB; % Bounds -model.ub = UB; - -% Constraint and model sense -for ix = 1:size(model.A,1) - if ix <= m+enz - model.sense(ix) = '='; - else - model.sense(ix) = '<'; - end -end -model.modelsense = 'max'; % Model sense -model.vtype = 'C'; % Variable type - -%% Main loop for CACs and FACs calculation -GRate = []; -ES = []; -FS = []; -PS = []; -EFS = []; -ixBio = 8; -vmax = 1e-3:1e-3:1e-1; -model.obj(ixBio) = 1; % Objective vector -for jx = 1:numel(vmax) - - % Set new bound for vs_max - model.rhs(m+enz+1) = vmax(jx); - sol = gurobi(model,params); - - % Extract solution - vz = sol.objval; % optimal objective value - GRate = [GRate;vz]; - mu = sol.pi(m+enz+1:end); % shadow prices of inequality constraints - bdual = model.rhs(m+enz+1:end); - - % Extract solution - v = sol.x(1:n); % optimal primal vector (fluxes) - e = sol.x(n+1:end); % optimal primal vector (enzymes) - mu_e = sol.pi(m+1:m+enz); % shadow prices of e-constraints - mu_v_ub = sol.pi(m+enz+1:m+enz+n); % shadow prices of v_max - mu_v_lb = sol.pi(m+enz+n+1:m+enz+2*n); % shadow prices of v_min - mu_e_ub = sol.pi(m+enz+2*n+1:m+2*enz+2*n); % shadow prices of e_max - mu_e_lb = sol.pi(m+2*enz+2*n+1:m+3*enz+2*n); % shadow prices of e_min - mu_pi = sol.pi(end); % shadow price of total available enzyme pool - alpha = sum(e)/phi_E0; - - % Calculate Capacity Sensitivity Coefficients - FCS = (mu_v_ub - mu_v_lb).*(v/vz); % Flux Capacity Sensitivities - ECS = (mu_e_ub - mu_e_lb).*(e/vz); % Enzyme Capacity Sensitivities - PCS = mu_pi*(phi_E0/vz); % Proteome Capacity Sensitivities - - % Check theorem (Strong Duality) - assert(abs(sum(FCS)+sum(ECS)+PCS-1)<1e-8) - - % Calculate Flux-Enzyme Sensitivity - FES = mu_e.*(e/vz); - - % Check realtionships - [sum(FES) + sum(FCS) + (1-alpha)*PCS,-sum(FES) + sum(ECS) + alpha*PCS] - - % Save results - FS = [FS;FCS(1)]; - ES = [ES;ECS(1)]; - EFS = [EFS;FES(1)]; - PS = [PS;PCS]; -end - -% Plot results for the substrate consumption and growth -subplot(2,1,1) -plot(vmax,GRate,'-k','LineWidth',1) -ylabel('\mu') - -subplot(2,1,2) -plot(vmax,FS,'-r','LineWidth',1) -hold on -plot(vmax,EFS,'-b','LineWidth',1) -plot(vmax,PS,'-g','LineWidth',1) -ylabel('Sensitivity coefficient value (-)') -xlabel('v_{s,max}') -legend({'FCS_1','FES_1','PCS'},'Location','northwest') -% -% subplot(3,1,3) -% plot(vmax,FACs(:,1),'-r','LineWidth',1) -% hold on -% plot(vmax,FACs(:,n+1),'-b','LineWidth',1) -% ylabel('FACs') -% legend({'v_s','e_s'},'Location','northwest') \ No newline at end of file diff --git a/Models/toy_model.json b/Models/toy_model.json index b79a1b6..2097bca 100644 --- a/Models/toy_model.json +++ b/Models/toy_model.json @@ -1,83 +1,101 @@ { "metabolites":[ { -"id":"Substrate", -"name":"", -"compartment":"" +"id":"Glc_ex", +"name":"Glucose extracellular", +"compartment":"ex" }, { -"id":"Intermediate", -"name":"", -"compartment":"" +"id":"Glc_int", +"name":"Glucose intracellular", +"compartment":"c" }, { -"id":"CO2", -"name":"", -"compartment":"" +"id":"Pyr", +"name":"Pyruvate", +"compartment":"c" }, { -"id":"Byproduct", -"name":"", -"compartment":"" +"id":"Act_int", +"name":"Acetate intracellular", +"compartment":"c" }, { -"id":"ATP", -"name":"", -"compartment":"" +"id":"Act_ex", +"name":"Acetate extracellular", +"compartment":"ex" +}, +{ +"id":"NADH", +"name":"NADH", +"compartment":"c" +}, +{ +"id":"AcoA", +"name":"Acetyl coA", +"compartment":"c" }, { -"id":"Precursor", -"name":"", -"compartment":"" +"id":"Intermediates", +"name":"Intermediates", +"compartment":"c" }, { "id":"Biomass", -"name":"", -"compartment":"" +"name":"Biomass", +"compartment":"c" +}, +{ +"id":"ATP", +"name":"Adenosine Triphosphate", +"compartment":"c" +}, +{ +"id":"CO2", +"name":"Carbon dioxide", +"compartment":"c" } ], "reactions":[ { "id":"R1", -"name":"", +"name":"Glucose diffusion", "metabolites":{ -"Substrate":1 +"Glc_ex":1 }, -"lower_bound":1, +"lower_bound":0, "upper_bound":100, "gene_reaction_rule":"" }, -{ + { "id":"R2", -"name":"", +"name":"Glucose uptake", "metabolites":{ -"CO2":1, -"Intermediate":1, -"Substrate":-1 +"Glc_ex":-1, +"Glc_int":1 }, -"lower_bound":0, +"lower_bound":1, "upper_bound":100, "gene_reaction_rule":"" }, { "id":"R3", -"name":"", +"name":"Glycolysis", "metabolites":{ -"ATP":1, -"Byproduct":1, -"Intermediate":-1 +"Glc_int":-1, +"Pyr":1 }, -"lower_bound":-1000000.0, +"lower_bound":0, "upper_bound":100, "gene_reaction_rule":"" }, { "id":"R4", -"name":"", +"name":"TCA cycle", "metabolites":{ -"ATP":2, -"CO2":1, -"Intermediate":-1 +"Pyr":-1, +"NADH":1, +"AcoA":1 }, "lower_bound":0, "upper_bound":100, @@ -85,22 +103,21 @@ }, { "id":"R5", -"name":"", +"name":"Respiration", "metabolites":{ -"Intermediate":-1, -"Precursor":1 +"NADH":-1, +"ATP":1 }, -"lower_bound":-1000000.0, +"lower_bound":0, "upper_bound":100, "gene_reaction_rule":"" }, { "id":"R6", -"name":"", +"name":"Fermentation", "metabolites":{ -"ATP":-1, -"Biomass":1, -"Precursor":-1 +"Pyr":-1, +"Act_int":1 }, "lower_bound":0, "upper_bound":100, @@ -108,37 +125,68 @@ }, { "id":"R7", -"name":"", +"name":"Acetate transport", "metabolites":{ -"Biomass":-1 +"Act_int":-1, +"Act_ex":1 }, "lower_bound":0, "upper_bound":100, "gene_reaction_rule":"" }, { -"id":"R8", -"name":"", +"id": "R8", +"name": "Intermediate synthesis", +"metabolites": { + "AcoA": -1, + "ATP": -1, + "Intermediates": 1, + "CO2": 1 +}, +"lower_bound":0, +"upper_bound":100, +"gene_reaction_rule":"" +}, +{"id":"R9", +"name":"Biomass synthesis", "metabolites":{ -"CO2":-1 +"Intermediates":-1, +"Biomass":1 }, "lower_bound":0, "upper_bound":100, "gene_reaction_rule":"" }, { -"id":"R9", -"name":"", +"id":"R10", +"name":"Acetate diffusion", "metabolites":{ -"Byproduct":-1 +"Act_ex": -1 }, "lower_bound":0, "upper_bound":100, "gene_reaction_rule":"" -} +}, +{"id":"R11", +"name":"Growth", +"metabolites":{ +"Biomass":-1 +}, +"lower_bound":0, +"upper_bound":100, +"gene_reaction_rule":"" +}, +{"id":"R12", +"name":"CO2 exchange rate", +"metabolites":{ +"CO2":-1 +}, +"lower_bound":0, +"upper_bound":100, +"gene_reaction_rule":""} ], "genes":[], -"id":"toy_model", +"id":"toy_model_TS", "compartments":{}, "version":"1" } \ No newline at end of file diff --git a/Models/toy_model_TS_v1.json b/Models/toy_model_TS_v1.json deleted file mode 100644 index 2097bca..0000000 --- a/Models/toy_model_TS_v1.json +++ /dev/null @@ -1,192 +0,0 @@ -{ -"metabolites":[ -{ -"id":"Glc_ex", -"name":"Glucose extracellular", -"compartment":"ex" -}, -{ -"id":"Glc_int", -"name":"Glucose intracellular", -"compartment":"c" -}, -{ -"id":"Pyr", -"name":"Pyruvate", -"compartment":"c" -}, -{ -"id":"Act_int", -"name":"Acetate intracellular", -"compartment":"c" -}, -{ -"id":"Act_ex", -"name":"Acetate extracellular", -"compartment":"ex" -}, -{ -"id":"NADH", -"name":"NADH", -"compartment":"c" -}, -{ -"id":"AcoA", -"name":"Acetyl coA", -"compartment":"c" -}, -{ -"id":"Intermediates", -"name":"Intermediates", -"compartment":"c" -}, -{ -"id":"Biomass", -"name":"Biomass", -"compartment":"c" -}, -{ -"id":"ATP", -"name":"Adenosine Triphosphate", -"compartment":"c" -}, -{ -"id":"CO2", -"name":"Carbon dioxide", -"compartment":"c" -} -], -"reactions":[ -{ -"id":"R1", -"name":"Glucose diffusion", -"metabolites":{ -"Glc_ex":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, - { -"id":"R2", -"name":"Glucose uptake", -"metabolites":{ -"Glc_ex":-1, -"Glc_int":1 -}, -"lower_bound":1, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id":"R3", -"name":"Glycolysis", -"metabolites":{ -"Glc_int":-1, -"Pyr":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id":"R4", -"name":"TCA cycle", -"metabolites":{ -"Pyr":-1, -"NADH":1, -"AcoA":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id":"R5", -"name":"Respiration", -"metabolites":{ -"NADH":-1, -"ATP":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id":"R6", -"name":"Fermentation", -"metabolites":{ -"Pyr":-1, -"Act_int":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id":"R7", -"name":"Acetate transport", -"metabolites":{ -"Act_int":-1, -"Act_ex":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id": "R8", -"name": "Intermediate synthesis", -"metabolites": { - "AcoA": -1, - "ATP": -1, - "Intermediates": 1, - "CO2": 1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{"id":"R9", -"name":"Biomass synthesis", -"metabolites":{ -"Intermediates":-1, -"Biomass":1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{ -"id":"R10", -"name":"Acetate diffusion", -"metabolites":{ -"Act_ex": -1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{"id":"R11", -"name":"Growth", -"metabolites":{ -"Biomass":-1 -}, -"lower_bound":0, -"upper_bound":100, -"gene_reaction_rule":"" -}, -{"id":"R12", -"name":"CO2 exchange rate", -"metabolites":{ -"CO2":-1 -}, 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Binary files a/Results/toy-core-full_model_ESC.xlsx and /dev/null differ diff --git a/Results/toy-core-full_model_performance_esc.xlsx b/Results/toy-core-full_model_performance_esc.xlsx deleted file mode 100644 index 112f7e3..0000000 Binary files a/Results/toy-core-full_model_performance_esc.xlsx and /dev/null differ diff --git a/Scripts/.ipynb_checkpoints/Ecoli_core_sensitivity_analysis-checkpoint.ipynb b/Scripts/.ipynb_checkpoints/Ecoli_core_sensitivity_analysis-checkpoint.ipynb deleted file mode 100644 index 9fb073a..0000000 --- a/Scripts/.ipynb_checkpoints/Ecoli_core_sensitivity_analysis-checkpoint.ipynb +++ /dev/null @@ -1,20842 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "9f8b50b7", - "metadata": {}, - "outputs": [], - "source": [ - "import cobra\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import plotly.express\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "7e7cf57d", - "metadata": {}, - "source": [ - "# Load PAMpy modules" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bd572e36", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading PAModelpy modules version 0.0.3.11\n", - "Loading PAModelpy modules version 0.0.3.3\n" - ] - } - ], - "source": [ - "# load PAMpy modules\n", - "if os.path.split(os.getcwd())[1] == 'Scripts':\n", - " os.chdir('..')\n", - " \n", - "from src.PAModelpy import PAModel, ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n", - "from src.PAModelpy.PAMValidator import PAMValidator\n", - "\n", - "from Scripts.pam_generation import set_up_ecolicore_pam" - ] - }, - { - "cell_type": "markdown", - "id": "8975cb71", - "metadata": {}, - "source": [ - "# Set-up E.coli core proteome allocation model (PAM)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "05f72e15", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No enzyme information found for reaction: FRD7\n", - "Read LP format model from file /tmp/tmpo3jjwb11.lp\n", - "Reading time = 0.00 seconds\n", - ": 72 rows, 190 columns, 720 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: \n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/EnzymeSectors.py:219: UserWarning: FORt: reaction directionality does not match provided kcat values. Skip reaction\n", - " warn(reaction.id + ': reaction directionality does not match provided kcat values. Skip reaction')\n" - ] - } - ], - "source": [ - "# set up PAM using a predefined method\n", - "pamodel = set_up_ecolicore_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "c151400f", - "metadata": {}, - "source": [ - "## 3. Sensitivity of the Ecoli core constraints" - ] - }, - { - "cell_type": "markdown", - "id": "69e02e47", - "metadata": {}, - "source": [ - "### 3.1 simulations with only active enzyme sectors for a range of glucose uptake rates" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f11ff845", - "metadata": {}, - "outputs": [], - "source": [ - "#some usefull functions\n", - "def print_heatmap(xaxis, yaxis, matrix, title = ''):\n", - " fig = plotly.express.imshow(matrix, aspect=\"auto\",\n", - " x = xaxis, y = yaxis,\n", - " labels = dict(x = 'constraint', y='glucose uptake rate [mmol/gcdw/h]'),\n", - " title = title)\n", - " \n", - " fig.update_layout(font=dict(size=20), \n", - " yaxis = dict(tickfont = dict(size=20)),\n", - " xaxis = dict(tickfont = dict(size=8)))\n", - "# fig.update_xaxes(title_font=dict(size=8))\n", - "# fig.update_yaxes(title_font=dict(size=18))\n", - " \n", - " fig.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c01a90f9", - "metadata": {}, - "outputs": [], - "source": [ - "def print_heatmap_transposed(xaxis, yaxis, matrix, title = '', cbarlabel = ''):\n", - " import matplotlib.pyplot as plt\n", - " \n", - " matrix_t = np.transpose(np.array(matrix))\n", - "\n", - " \n", - " fig, ax = plt.subplots()\n", - " im = ax.imshow(matrix_t)\n", - " cbar = fig.colorbar(im, ax=ax)\n", - " \n", - "# im = ax.imshow(matrix_t)\n", - "\n", - " \n", - " ax.set_xticks(np.arange(len(yaxis)), labels=yaxis)\n", - " ax.set_yticks(np.arange(len(xaxis)), labels=xaxis)\n", - " \n", - " # axes titles\n", - " ax.set_xlabel('Glucose uptake rate [mmol/gcdw/h]', fontsize =16)\n", - " ax.set_ylabel('Enzyme', fontsize =16)\n", - " \n", - "\n", - " # make a colorbar\n", - "# add_colorbar(im)\n", - " \n", - " ax.set_title(title)\n", - " fig.tight_layout()\n", - "# fig.set_figwidth(20)\n", - "# fig.set_figheight(100)\n", - " plt.show()\n", - " \n", - " \n", - "def add_colorbar(im, width=None, pad=None, **kwargs):\n", - "\n", - " l, b, w, h = im.axes.get_position().bounds # get boundaries\n", - " width = width or 0.1 * w # get width of the colorbar\n", - " pad = pad or width # get pad between im and cbar\n", - " fig = im.axes.figure # get figure of image\n", - " cax = fig.add_axes([l + w + pad, b, width, h]) # define cbar Axes\n", - " return fig.colorbar(im, cax=cax, **kwargs) # draw cbar" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5bb60ce5", - "metadata": {}, - "outputs": [], - "source": [ - "def print_heatmap_matrix_row(xaxis, yaxis, matrix, row, title = '', vmin =0, vmax=1,\n", - " cbarlabel = '', save_to= 'ecolicore_heatmap_maxmu.png'):\n", - " import matplotlib\n", - " \n", - " norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)\n", - " grid = dict(height_ratios=[1, len(matrix)], width_ratios=[len(matrix[0]), 0.5 ])\n", - " fig, axes = plt.subplots(ncols=2, nrows=2, gridspec_kw = grid)\n", - "\n", - " plt.rcParams['font.size'] = '16'\n", - "\n", - " im = axes[1,0].imshow(matrix, aspect=\"auto\", cmap=\"viridis\", vmin = vmin, vmax =vmax)\n", - " axes[0,0].imshow(row, aspect=\"auto\", cmap=\"viridis\")\n", - "\n", - " axes[0,0].get_xaxis().set_visible(False)\n", - " axes[0,1].axis('off')\n", - "\n", - " axes[1,0].set_xlabel('Glucose uptake rate [mmol/gcdw/h]', fontsize =16)\n", - " axes[1,0].set_ylabel('Enzyme', fontsize =16)\n", - "\n", - " # Show all ticks and label them with the respective list entries\n", - " axes[0,0].set_yticks(np.arange(1), labels=['Total Protein'], fontsize =16)\n", - " axes[1,0].set_xticks(np.arange(len(yaxis)), labels=yaxis, fontsize =16)\n", - " axes[1,0].set_yticks(np.arange(len(xaxis)), labels=xaxis, fontsize =16)\n", - "\n", - " sm = matplotlib.cm.ScalarMappable(cmap=\"viridis\", norm = norm)\n", - " sm.set_array([])\n", - "\n", - " fig.subplots_adjust(top=0.4,bottom=0.1) \n", - " fig.colorbar(sm,label = cbarlabel, ax =axes, cax=axes[1,1], shrink = 1.3, fraction = 1.5)\n", - "\n", - " fig.tight_layout()\n", - " fig.set_figwidth(20)\n", - " fig.set_figheight(20)\n", - " plt.savefig('../Results/'+save_to, dpi =100)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3efd9e27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "glucose uptake rate 0.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.113747 \n", - "\n", - "glucose uptake rate 2.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.076687 \n", - "\n", - "glucose uptake rate 2.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.057647 \n", - "\n", - "glucose uptake rate 3.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.046055 \n", - "\n", - "glucose uptake rate 3.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.038255 \n", - "\n", - "glucose uptake rate 4.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.032648 \n", - "\n", - "glucose uptake rate 4.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.028423 \n", - "\n", - "glucose uptake rate 5.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.025126 \n", - "\n", - "glucose uptake rate 5.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.491201 \n", - "\n", - "glucose uptake rate 6.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.457812 \n", - "\n", - "glucose uptake rate 6.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.427968 \n", - "\n", - "glucose uptake rate 7.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.53318 \n", - "\n", - "glucose uptake rate 7.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.505137 \n", - "\n", - "glucose uptake rate 8.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.955718 \n", - "\n", - "glucose uptake rate 8.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.932464 \n", - "\n", - "glucose uptake rate 9.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.565391 \n", - "\n", - "glucose uptake rate 9.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.580651 \n", - "\n", - "glucose uptake rate 10.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.597214 \n", - "\n" - ] - }, - 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}, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "tickfont": { - "size": 8 - }, - "title": { - "text": "constraint" - } - }, - "yaxis": { - "anchor": "x", - "autorange": "reversed", - "domain": [ - 0, - 1 - ], - "tickfont": { - "size": 20 - }, - "title": { - "text": "glucose uptake rate [mmol/gcdw/h]" - } - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - "Ccsc = []\n", - "Cesc = []\n", - "y_axis = []\n", - "fluxes = []\n", - "xaxis_csc = []\n", - " \n", - "# disable pyruvate formate lyase (inhibited by oxygen)\n", - "pamodel.change_reaction_bounds(rxn_id = 'PFL', upper_bound = 0)\n", - " \n", - "for glc in glc_uptake_rates:\n", - " print('glucose uptake rate ', glc, ' mmol/gcdw/h')\n", - " with pamodel:\n", - " # change glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -glc, upper_bound = -glc)\n", - " # solve the model\n", - " sol_pam = pamodel.optimize()\n", - " fluxes.append(sol_pam.fluxes)\n", - " if pamodel.solver.status == 'optimal': y_axis += [glc]\n", - " # save data\n", - " Ccsc_new = list()\n", - " if pamodel.solver.status == 'optimal':\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - "\n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of enzyme sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6),'\\n')\n", - "\n", - "for cc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if cc == 'flux_ub' or cc == 'flux_lb':\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[capacity_coeff['constraint'] == cc].rxn_id.to_list()]\n", - " else:\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == cc].enzyme_id.to_list()]\n", - " \n", - "xaxis_esc = enzyme_coeff.enzyme_id.to_list()\n", - " \n", - "#make yaxis\n", - "\n", - "print_heatmap(xaxis_csc, y_axis, Ccsc, title = 'Capacity Sensitivity Coefficients')\n", - "print_heatmap(xaxis_esc, y_axis, Cesc, title = 'Enzyme Sensitivity Coefficients')" - ] - }, - { - "cell_type": "markdown", - "id": "4a9ad912", - "metadata": {}, - "source": [ - "## 4. Changing the optimization objective" - ] - }, - { - "cell_type": "markdown", - "id": "cdd569a8", - "metadata": {}, - "source": [ - "### 4.1: pfba: minimize proteins after maximizing growth\n", - "fraction of optimum: 0.8, allowing 20% change of max growth rate" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3d89ea2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.789713 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.973766\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.086677 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.973382\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.08275 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.97295\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.080169 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.972402\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.08151 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.971831\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.082906 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.095023 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.097581 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.109082 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.127212 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.151346 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.167514 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.184968 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.203867 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.224397 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.24678 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.271278 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.298207 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.365924 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.411097 \n", - "\n", - "20 20\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "coloraxis": "coloraxis", - "hovertemplate": "constraint: %{x}
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"automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "Enzyme Sensitivity Coefficients" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "tickfont": { - "size": 8 - }, - "title": { - "text": "constraint" - } - }, - "yaxis": { - "anchor": "x", - "autorange": "reversed", - "domain": [ - 0, - 1 - ], - "tickfont": { - "size": 20 - }, - "title": { - "text": "glucose uptake rate [mmol/gcdw/h]" - } - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#set the correct biomass reaction id\n", - "pa_model.BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'\n", - "frac_opt = 0.8\n", - "\n", - "glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - "Ccsc = []\n", - "Cesc = []\n", - "y_axis = []\n", - "fluxes = []\n", - "xaxis_csc = []\n", - " \n", - "\n", - "# disable pyruvate formate lyase (inhibited by oxygen)\n", - "pa_model.change_reaction_bounds(rxn_id = 'PFL', upper_bound = 0)\n", - "\n", - "for glc in glc_uptake_rates:\n", - " y_axis += [glc]\n", - " with pamodel:\n", - " # change glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -glc, upper_bound = -glc)\n", - " #reset the objective to maximize growth\n", - " pamodel.objective = 'BIOMASS_Ecoli_core_w_GAM'\n", - " # solve the model\n", - " pamodel.optimize()\n", - " #if optimal, perform pFBA: minimization of sum of total proteins\n", - " if pamodel.solver.status == 'optimal':\n", - " #set the growth rate\n", - " growth_rate = pamodel.reactions.get_by_id('BIOMASS_Ecoli_core_w_GAM').flux\n", - " pamodel.reactions.get_by_id('BIOMASS_Ecoli_core_w_GAM').bound = frac_opt* growth_rate, (2-frac_opt)*growth_rate\n", - " #make the new objective\n", - " pamodel.objective = {enz:1 for enz in pamodel.enzyme_variables}\n", - " #also account for reverse enzymes\n", - " pamodel.objective.set_linear_coefficients({enz.reverse_variable:-1 for enz in pamodel.enzyme_variables})\n", - " #run the model\n", - " pamodel.optimize()\n", - " # save data\n", - " Ccsc_new = list()\n", - " if pamodel.solver.status == 'optimal':\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - "\n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of enzyme sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6),'\\n')\n", - "\n", - "for cc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if cc == 'flux_ub' or cc == 'flux_lb':\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[capacity_coeff['constraint'] == cc].rxn_id.to_list()]\n", - " else:\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == cc].enzyme_id.to_list()]\n", - " \n", - "xaxis_esc = enzyme_coeff.enzyme_id.to_list()\n", - "\n", - "print_heatmap(xaxis_csc, y_axis, Ccsc, title = 'Capacity Sensitivity Coefficients')\n", - "print_heatmap(xaxis_esc, y_axis, Cesc, title = 'Enzyme Sensitivity Coefficients')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ea093fb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Scripts/.ipynb_checkpoints/analyze_proteome-checkpoint.ipynb b/Scripts/.ipynb_checkpoints/analyze_proteome-checkpoint.ipynb deleted file mode 100644 index 71bd3e4..0000000 --- a/Scripts/.ipynb_checkpoints/analyze_proteome-checkpoint.ipynb +++ /dev/null @@ -1,2072 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Analyze the proteome for the E. coli core model\n", - "- Map measured protein abundaces to model genes/enzymes" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import cobra\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import linregress" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load proteome data\n", - "## Protein abundances" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GlucoseLBGlycerol + AAAcetateFumarateGlucosamineGlycerolPyruvateChemostat µ=0.5Chemostat µ=0.35...Stationary phase 1 dayStationary phase 3 daysOsmotic-stress glucose42°C glucosepH6 glucoseXyloseMannoseGalactoseSuccinateFructose
Bnumber
b39880.7153491.8445151.1593310.5612530.6293180.8536420.7323470.7028201.2307661.004203...4.466397e-014.910739e-010.5787231.026062e+000.8597390.9349638.757239e-015.813814e-017.116349e-011.162894
b39870.9891352.2216141.2993460.6651630.7995150.9995450.9369900.9283981.3108991.096833...5.944920e-015.565920e-010.8133041.230390e+001.0076871.1090021.035491e+007.049528e-019.635688e-011.317061
b01181.1782832.5750862.7222443.5437453.0236802.1334451.6741742.5433622.4406663.143073...1.447816e-011.173512e-010.4366107.511055e-010.7211801.0389632.562241e+001.997324e+003.032313e+001.362394
b25570.5763040.1925540.5487960.3373130.4860280.4596180.4851430.6353900.5361790.405983...1.449196e-011.724214e-010.3092674.423852e-010.4007930.4037344.139472e-013.684797e-014.489504e-010.564445
b32120.7746530.1637420.1766520.3692120.3470260.5357620.5971610.4581320.6290090.530940...6.220451e-024.961751e-020.2258658.072853e-010.7379510.6763515.631967e-013.788294e-014.207655e-011.045406
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2359 rows × 22 columns

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" - ], - "text/plain": [ - " Glucose LB Glycerol + AA Acetate Fumarate Glucosamine \\\n", - "Bnumber \n", - "b3988 0.715349 1.844515 1.159331 0.561253 0.629318 0.853642 \n", - "b3987 0.989135 2.221614 1.299346 0.665163 0.799515 0.999545 \n", - "b0118 1.178283 2.575086 2.722244 3.543745 3.023680 2.133445 \n", - "b2557 0.576304 0.192554 0.548796 0.337313 0.486028 0.459618 \n", - "b3212 0.774653 0.163742 0.176652 0.369212 0.347026 0.535762 \n", - "... ... ... ... ... ... ... \n", - "NaN 0.000167 0.001322 NaN 0.000097 0.000504 0.000339 \n", - "NaN 0.000084 0.000012 0.000002 0.000249 0.000235 0.000125 \n", - "NaN 0.002472 0.000022 0.000066 0.000770 0.000334 0.000590 \n", - "NaN 0.000119 0.000300 0.000166 0.000093 0.000142 0.000094 \n", - "NaN 0.001575 0.000632 NaN 0.000451 0.000526 0.000346 \n", - "\n", - " Glycerol Pyruvate Chemostat µ=0.5 Chemostat µ=0.35 ... \\\n", - "Bnumber ... \n", - "b3988 0.732347 0.702820 1.230766 1.004203 ... \n", - "b3987 0.936990 0.928398 1.310899 1.096833 ... \n", - "b0118 1.674174 2.543362 2.440666 3.143073 ... \n", - "b2557 0.485143 0.635390 0.536179 0.405983 ... \n", - "b3212 0.597161 0.458132 0.629009 0.530940 ... \n", - "... ... ... ... ... ... \n", - "NaN 0.000170 0.000183 0.000151 0.000068 ... \n", - "NaN 0.000252 0.000419 0.000644 0.000241 ... \n", - "NaN 0.000709 0.000805 0.000652 0.000320 ... \n", - "NaN 0.000113 0.000125 0.000088 0.000096 ... \n", - "NaN 0.000367 0.000291 0.000656 0.001451 ... \n", - "\n", - " Stationary phase 1 day Stationary phase 3 days \\\n", - "Bnumber \n", - "b3988 4.466397e-01 4.910739e-01 \n", - "b3987 5.944920e-01 5.565920e-01 \n", - "b0118 1.447816e-01 1.173512e-01 \n", - "b2557 1.449196e-01 1.724214e-01 \n", - "b3212 6.220451e-02 4.961751e-02 \n", - "... ... ... \n", - "NaN 1.524261e-04 0.000000e+00 \n", - "NaN 6.333976e-07 6.237618e-07 \n", - "NaN 3.374665e-07 1.943465e-04 \n", - "NaN 2.921593e-07 2.642997e-06 \n", - "NaN 4.131408e-05 1.144572e-04 \n", - "\n", - " Osmotic-stress glucose 42°C glucose pH6 glucose Xylose \\\n", - "Bnumber \n", - "b3988 0.578723 1.026062e+00 0.859739 0.934963 \n", - "b3987 0.813304 1.230390e+00 1.007687 1.109002 \n", - "b0118 0.436610 7.511055e-01 0.721180 1.038963 \n", - "b2557 0.309267 4.423852e-01 0.400793 0.403734 \n", - "b3212 0.225865 8.072853e-01 0.737951 0.676351 \n", - "... ... ... ... ... \n", - "NaN 0.000010 2.769145e-04 0.000304 NaN \n", - "NaN 0.000004 9.527910e-06 0.000001 0.000001 \n", - "NaN 0.000770 1.372202e-03 0.001054 0.001213 \n", - "NaN 0.000014 5.439283e-07 0.000035 0.000022 \n", - "NaN 0.000012 3.876813e-04 0.000049 NaN \n", - "\n", - " Mannose Galactose Succinate Fructose \n", - "Bnumber \n", - "b3988 8.757239e-01 5.813814e-01 7.116349e-01 1.162894 \n", - "b3987 1.035491e+00 7.049528e-01 9.635688e-01 1.317061 \n", - "b0118 2.562241e+00 1.997324e+00 3.032313e+00 1.362394 \n", - "b2557 4.139472e-01 3.684797e-01 4.489504e-01 0.564445 \n", - "b3212 5.631967e-01 3.788294e-01 4.207655e-01 1.045406 \n", - "... ... ... ... ... \n", - "NaN NaN 6.570810e-05 2.050574e-04 NaN \n", - "NaN 1.052190e-06 8.777261e-07 1.026332e-06 0.000001 \n", - "NaN 9.174705e-04 3.517107e-04 2.757719e-04 0.001285 \n", - "NaN 4.876650e-07 1.863777e-05 4.725847e-07 0.000013 \n", - "NaN NaN 3.836446e-04 2.818039e-04 NaN \n", - "\n", - "[2359 rows x 22 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load proteome data (Schmidt et al. 2016)\n", - "proteome_df = pd.read_excel('../Data/proteome_data_extract_schmidt2016.xlsx',\n", - " sheet_name='ProteinMasses',\n", - " engine='openpyxl',\n", - " index_col=0)\n", - "\n", - "proteome_df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GlucoseLBGlycerol + AAAcetateFumarateGlucosamineGlycerolPyruvateChemostat µ=0.5Chemostat µ=0.35...Stationary phase 1 dayStationary phase 3 daysOsmotic-stress glucose42°C glucosepH6 glucoseXyloseMannoseGalactoseSuccinateFructose
Bnumber
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..................................................................
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2359 rows × 22 columns

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" - ], - "text/plain": [ - " Glucose LB Glycerol + AA Acetate \\\n", - "Bnumber \n", - "b3988 2.941474e-03 5.026393e-03 3.550227e-03 2.858254e-03 \n", - "b3987 4.067266e-03 6.054005e-03 3.978997e-03 3.387428e-03 \n", - "b0118 4.845034e-03 7.017233e-03 8.336348e-03 1.804699e-02 \n", - "b2557 2.369729e-03 5.247197e-04 1.680582e-03 1.717809e-03 \n", - "b3212 3.185331e-03 4.462054e-04 5.409631e-04 1.880260e-03 \n", - "... ... ... ... ... \n", - "NaN 6.851077e-07 3.602707e-06 NaN 4.952390e-07 \n", - "NaN 3.436903e-07 3.297128e-08 4.692171e-09 1.266535e-06 \n", - "NaN 1.016463e-05 5.960815e-08 2.028619e-07 3.922468e-06 \n", - "NaN 4.873215e-07 8.167594e-07 5.090973e-07 4.756701e-07 \n", - "NaN 6.477288e-06 1.722441e-06 NaN 2.295474e-06 \n", - "\n", - " Fumarate Glucosamine Glycerol Pyruvate \\\n", - "Bnumber \n", - "b3988 2.896226e-03 3.786753e-03 3.247723e-03 3.286201e-03 \n", - "b3987 3.679503e-03 4.433976e-03 4.155249e-03 4.340946e-03 \n", - "b0118 1.391549e-02 9.463954e-03 7.424421e-03 1.189210e-02 \n", - "b2557 2.236781e-03 2.038865e-03 2.151453e-03 2.970919e-03 \n", - "b3212 1.597071e-03 2.376639e-03 2.648216e-03 2.142107e-03 \n", - "... ... ... ... ... \n", - "NaN 2.317645e-06 1.502131e-06 7.546521e-07 8.554820e-07 \n", - "NaN 1.081327e-06 5.536750e-07 1.118401e-06 1.958754e-06 \n", - "NaN 1.535391e-06 2.616758e-06 3.142143e-06 3.764873e-06 \n", - "NaN 6.550601e-07 4.167513e-07 5.023276e-07 5.839774e-07 \n", - "NaN 2.420377e-06 1.535065e-06 1.626043e-06 1.361277e-06 \n", - "\n", - " Chemostat µ=0.5 Chemostat µ=0.35 ... Stationary phase 1 day \\\n", - "Bnumber ... \n", - "b3988 5.342251e-03 4.889101e-03 ... 3.199374e-03 \n", - "b3987 5.690078e-03 5.340084e-03 ... 4.258471e-03 \n", - "b0118 1.059393e-02 1.530249e-02 ... 1.037101e-03 \n", - "b2557 2.327332e-03 1.976587e-03 ... 1.038090e-03 \n", - "b3212 2.730272e-03 2.584953e-03 ... 4.455840e-04 \n", - "... ... ... ... ... \n", - "NaN 6.538958e-07 3.325533e-07 ... 1.091860e-06 \n", - "NaN 2.794743e-06 1.172058e-06 ... 4.537160e-09 \n", - "NaN 2.828644e-06 1.556013e-06 ... 2.417344e-09 \n", - "NaN 3.815181e-07 4.679845e-07 ... 2.092799e-09 \n", - "NaN 2.845535e-06 7.065384e-06 ... 2.959414e-07 \n", - "\n", - " Stationary phase 3 days Osmotic-stress glucose 42°C glucose \\\n", - "Bnumber \n", - "b3988 3.536290e-03 2.428557e-03 4.022638e-03 \n", - "b3987 4.008095e-03 3.412954e-03 4.823697e-03 \n", - "b0118 8.450620e-04 1.832193e-03 2.944681e-03 \n", - "b2557 1.241630e-03 1.297808e-03 1.734354e-03 \n", - "b3212 3.573025e-04 9.478229e-04 3.164931e-03 \n", - "... ... ... ... \n", - "NaN 0.000000e+00 4.164754e-08 1.085633e-06 \n", - "NaN 4.491794e-09 1.575463e-08 3.735381e-08 \n", - "NaN 1.399516e-06 3.232982e-06 5.379667e-06 \n", - "NaN 1.903258e-08 6.027465e-08 2.132450e-09 \n", - "NaN 8.242219e-07 5.154440e-08 1.519890e-06 \n", - "\n", - " pH6 glucose Xylose Mannose Galactose Succinate \\\n", - "Bnumber \n", - "b3988 3.431053e-03 3.932964e-03 3.892800e-03 3.075565e-03 3.226823e-03 \n", - "b3987 4.021487e-03 4.665070e-03 4.603003e-03 3.729270e-03 4.369188e-03 \n", - "b0118 2.878090e-03 4.370448e-03 1.138977e-02 1.056604e-02 1.374966e-02 \n", - "b2557 1.599490e-03 1.698326e-03 1.840094e-03 1.949294e-03 2.035712e-03 \n", - "b3212 2.945022e-03 2.845100e-03 2.503543e-03 2.004045e-03 1.907911e-03 \n", - "... ... ... ... ... ... \n", - "NaN 1.214564e-06 NaN NaN 3.476024e-07 9.298084e-07 \n", - "NaN 4.633229e-09 4.624998e-09 4.677236e-09 4.643258e-09 4.653780e-09 \n", - "NaN 4.205954e-06 5.102658e-06 4.078374e-06 1.860585e-06 1.250455e-06 \n", - "NaN 1.413368e-07 9.141116e-08 2.167786e-09 9.859566e-08 2.142879e-09 \n", - "NaN 1.944331e-07 NaN NaN 2.029518e-06 1.277806e-06 \n", - "\n", - " Fructose \n", - "Bnumber \n", - "b3988 4.598847e-03 \n", - "b3987 5.208525e-03 \n", - "b0118 5.387801e-03 \n", - "b2557 2.232186e-03 \n", - "b3212 4.134224e-03 \n", - "... ... \n", - "NaN NaN \n", - "NaN 4.681092e-09 \n", - "NaN 5.079908e-06 \n", - "NaN 5.000788e-08 \n", - "NaN NaN \n", - "\n", - "[2359 rows x 22 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# normalize protein abundances to total protein abundance\n", - "sum_proteines = proteome_df.sum(axis=0)\n", - "proteome_norm_df = proteome_df / sum_proteines\n", - "proteome_norm_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Growth rates" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Growth rates
Condition
LB1.90
Glycerol + AA1.27
Acetate0.30
Fumarate0.42
Galactose0.26
Glucose0.58
Glucosamine0.46
Glycerol0.47
Pyruvate0.40
Succinate0.44
Fructose0.65
Mannose0.47
Xylose0.55
Osmotic-stress glucose0.55
42°C glucose0.66
pH6 glucose0.63
Stationary phase 1 day-0.01
Stationary phase 3 days-0.01
Chemostat µ=0.120.12
Chemostat µ=0.200.20
Chemostat µ=0.350.35
Chemostat µ=0.50.50
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" - ], - "text/plain": [ - " Growth rates\n", - "Condition \n", - "LB 1.90\n", - "Glycerol + AA 1.27\n", - "Acetate 0.30\n", - "Fumarate 0.42\n", - "Galactose 0.26\n", - "Glucose 0.58\n", - "Glucosamine 0.46\n", - "Glycerol 0.47\n", - "Pyruvate 0.40\n", - "Succinate 0.44\n", - "Fructose 0.65\n", - "Mannose 0.47\n", - "Xylose 0.55\n", - "Osmotic-stress glucose 0.55\n", - "42°C glucose 0.66\n", - "pH6 glucose 0.63\n", - "Stationary phase 1 day -0.01\n", - "Stationary phase 3 days -0.01\n", - "Chemostat µ=0.12 0.12\n", - "Chemostat µ=0.20 0.20\n", - "Chemostat µ=0.35 0.35\n", - "Chemostat µ=0.5 0.50" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load growth rate data [1/h] (Schmidt et al. 2016)\n", - "growth_rate_df = pd.read_excel('../Data/proteome_data_extract_schmidt2016.xlsx',\n", - " sheet_name='GrowthRates',\n", - " engine='openpyxl',\n", - " index_col=0\n", - " )\n", - "growth_rate_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## COG" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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COG IDCOG Name
Bnumber
b3988KTranscription
b3987KTranscription
b0118CEnergy production and conversion
b2557FNucleotide transport and metabolism
b3212EAmino acid transport and metabolism
.........
NaN-NaN
NaN-NaN
NaN-NaN
NaN-NaN
NaN-NaN
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2359 rows × 2 columns

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" - ], - "text/plain": [ - " COG ID COG Name\n", - "Bnumber \n", - "b3988 K Transcription\n", - "b3987 K Transcription\n", - "b0118 C Energy production and conversion\n", - "b2557 F Nucleotide transport and metabolism\n", - "b3212 E Amino acid transport and metabolism\n", - "... ... ...\n", - "NaN - NaN\n", - "NaN - NaN\n", - "NaN - NaN\n", - "NaN - NaN\n", - "NaN - NaN\n", - "\n", - "[2359 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load conversion table from gene to COG\n", - "gene2cog_df = pd.read_excel('../Data/proteome_data_extract_schmidt2016.xlsx',\n", - " sheet_name='Gene2COG',\n", - " engine='openpyxl',\n", - " index_col=0)\n", - "gene2cog_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2024-03-07\n" - ] - } - ], - "source": [ - "model = cobra.io.load_json_model('../Models/e_coli_core.json')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# General proteome analysis\n", - "## RNA polymerase" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# analyze RNA polymerase abundances\n", - "# rnap_genes = ['b3988', 'b3987', 'b3295', 'b3067', 'b2741', 'b3202', 'b3649', 'b2573']\n", - "rnap_genes = ['b3295', 'b3987', 'b3988'], # rpoA, rpoB, rpoC -> RNA polymerase core enzyme \n", - "# get abundaces of RNA polymerase genes (rnap_genes)\n", - "rnap_fractions_ds = proteome_norm_df.loc[rnap_genes].sum(axis=0)\n", - "rnap_fractions_ds.rename('Proteinfraction', inplace=True)\n", - "\n", - "# merge sum of protein abundaces with growth rate data\n", - "rnap_abundances_df = pd.concat([rnap_fractions_ds, growth_rate_df], axis=1)\n", - "rnap_abundances_df\n", - "\n", - "# print as scatter plot\n", - "rnap_abundances_df.plot.scatter(x='Growth rates', y='Proteinfraction')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyze proteome of E. coli core model\n", - "## Protein abundance of model proteins" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "s0001 not in proteome data\n", - "b3115 tdcD not in proteome data\n", - "b0116 lpd not in proteome data\n", - "b3739 atpI not in proteome data\n", - "b0979 cbdB not in proteome data\n", - "b3603 lldP not in proteome data\n", - "b1773 ydjI not in proteome data\n", - "b2492 focB not in proteome data\n", - "b4152 frdC not in proteome data\n", - "b4151 frdD not in proteome data\n", - "b1621 malX not in proteome data\n", - "b1524 glsB not in proteome data\n", - "b0485 glsA not in proteome data\n", - "b0875 aqpZ not in proteome data\n", - "b2280 nuoJ not in proteome data\n", - "b2579 grcA not in proteome data\n", - "b3951 pflD not in proteome data\n", - "b3952 pflC not in proteome data\n", - "b3612 gpmM not in proteome data\n", - "b4395 ytjC not in proteome data\n", - "b2987 pitB not in proteome data\n", - "b3403 pck not in proteome data\n", - "b2458 eutD not in proteome data\n", - "b4301 sgcE not in proteome data\n" - ] - }, - { - "data": { - "text/html": [ - "
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GlucoseLBGlycerol + AAAcetateFumarateGlucosamineGlycerolPyruvateChemostat µ=0.5Chemostat µ=0.35...Stationary phase 1 dayStationary phase 3 daysOsmotic-stress glucose42°C glucosepH6 glucoseXyloseMannoseGalactoseSuccinateFructose
Bnumber
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b03510.0000580.000030NaN0.0000220.0000270.0000330.0000240.0000210.0000390.000051...0.0000140.0000110.0000490.0000410.000044NaNNaN0.0000170.000034NaN
b18490.0006880.0000700.0002780.0002630.0004090.0005290.0004440.0007770.0004330.000316...0.0003380.0003380.0004950.0006150.0004630.0004460.0004460.0004670.0003960.000381
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..................................................................
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113 rows × 22 columns

\n", - "
" - ], - "text/plain": [ - " Glucose LB Glycerol + AA Acetate Fumarate Glucosamine \\\n", - "Bnumber \n", - "b1241 0.003181 0.002378 0.002752 0.003663 0.002941 0.005208 \n", - "b0351 0.000058 0.000030 NaN 0.000022 0.000027 0.000033 \n", - "b1849 0.000688 0.000070 0.000278 0.000263 0.000409 0.000529 \n", - "b2296 0.000904 0.001685 0.000870 0.000533 0.000792 0.000658 \n", - "b1276 0.000397 0.000431 0.000390 0.001321 0.000994 0.001096 \n", - "... ... ... ... ... ... ... \n", - "b0008 0.002553 0.002517 0.003291 0.002000 0.002605 0.002547 \n", - "b2464 0.000352 0.000154 0.000202 0.000400 0.000348 0.000389 \n", - "b2465 0.000375 0.000125 0.000185 0.000594 0.000509 0.000502 \n", - "b2935 0.001913 0.002455 0.001935 0.001624 0.002019 0.001706 \n", - "b3919 0.001368 0.000917 0.001329 0.001758 0.001597 0.001466 \n", - "\n", - " Glycerol Pyruvate Chemostat µ=0.5 Chemostat µ=0.35 ... \\\n", - "Bnumber ... \n", - "b1241 0.003668 0.002455 0.007168 0.005931 ... \n", - "b0351 0.000024 0.000021 0.000039 0.000051 ... \n", - "b1849 0.000444 0.000777 0.000433 0.000316 ... \n", - "b2296 0.000824 0.002779 0.000625 0.000706 ... \n", - "b1276 0.000581 0.000595 0.000690 0.001024 ... \n", - "... ... ... ... ... ... \n", - "b0008 0.002926 0.003065 0.002528 0.001977 ... \n", - "b2464 0.000358 0.000235 0.000248 0.000306 ... \n", - "b2465 0.000496 0.000356 0.000466 0.000552 ... \n", - "b2935 0.001802 0.002058 0.001762 0.001647 ... \n", - "b3919 0.001479 0.001427 0.001808 0.001536 ... \n", - "\n", - " Stationary phase 1 day Stationary phase 3 days \\\n", - "Bnumber \n", - "b1241 0.008040 0.007712 \n", - "b0351 0.000014 0.000011 \n", - "b1849 0.000338 0.000338 \n", - "b2296 0.001196 0.001083 \n", - "b1276 0.000623 0.000662 \n", - "... ... ... \n", - "b0008 0.002751 0.003207 \n", - "b2464 0.001226 0.001371 \n", - "b2465 0.000998 0.001070 \n", - "b2935 0.000767 0.000746 \n", - "b3919 0.002204 0.002696 \n", - "\n", - " Osmotic-stress glucose 42°C glucose pH6 glucose Xylose \\\n", - "Bnumber \n", - "b1241 0.003491 0.005646 0.005738 0.005630 \n", - "b0351 0.000049 0.000041 0.000044 NaN \n", - "b1849 0.000495 0.000615 0.000463 0.000446 \n", - "b2296 0.000880 0.000948 0.001086 0.001512 \n", - "b1276 0.000407 0.000424 0.000427 0.000344 \n", - "... ... ... ... ... \n", - "b0008 0.003328 0.004449 0.003079 0.003284 \n", - "b2464 0.000501 0.000534 0.000893 0.000297 \n", - "b2465 0.000496 0.000485 0.000784 0.000334 \n", - "b2935 0.001250 0.001583 0.001521 0.001988 \n", - "b3919 0.002496 0.001431 0.002680 0.001845 \n", - "\n", - " Mannose Galactose Succinate Fructose \n", - "Bnumber \n", - "b1241 0.004238 0.005336 0.002931 0.012611 \n", - "b0351 NaN 0.000017 0.000034 NaN \n", - "b1849 0.000446 0.000467 0.000396 0.000381 \n", - "b2296 0.001176 0.001133 0.001115 0.001357 \n", - "b1276 0.000912 0.001122 0.000867 0.000433 \n", - "... ... ... ... ... \n", - "b0008 0.002368 0.002386 0.002547 0.002890 \n", - "b2464 0.000343 0.000810 0.000297 0.000231 \n", - "b2465 0.000523 0.000955 0.000469 0.000319 \n", - "b2935 0.001700 0.001030 0.002021 0.001738 \n", - "b3919 0.001238 0.001751 0.001268 0.001653 \n", - "\n", - "[113 rows x 22 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genes_in_dataset = []\n", - "for g in model.genes:\n", - " if g.id in proteome_norm_df.index:\n", - " genes_in_dataset.append(g.id)\n", - " else:\n", - " print(f'{g.id} {g.name} not in proteome data')\n", - "\n", - "# extract protein abundances for genes in dataset\n", - "proteome_norm_model_df = proteome_norm_df.loc[genes_in_dataset]\n", - "proteome_norm_model_df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Model protein abundance')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# sum protein abundances for each condition\n", - "proteome_norm_model_sum_ds = proteome_norm_model_df.sum(axis=0)\n", - "proteome_norm_model_sum_ds.rename('Protein abundance sum', inplace=True)\n", - "\n", - "# merge sum of protein abundaces with growth rate data\n", - "proteome_norm_model_sum_df = pd.concat([proteome_norm_model_sum_ds, growth_rate_df], axis=1)\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(proteome_norm_model_sum_df['Growth rates'], proteome_norm_model_sum_df['Protein abundance sum'])\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Model protein abundance')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Translational sector (J)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Translational protein abundance')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# sum protein abundances of proteins from COG J\n", - "# get Bnumbers associated with COG ID J from gene2cog_df\n", - "bnumbers_j = gene2cog_df.loc[gene2cog_df['COG ID'] == 'J'].index.to_list()\n", - "\n", - "# sum protein abundances of translational protein sector\n", - "proteome_norm_sum_j = proteome_norm_df.loc[bnumbers_j].sum(axis=0)\n", - "proteome_norm_sum_j.rename('Protein abundance sum', inplace=True)\n", - "\n", - "# merge with growth rates\n", - "proteome_norm_sum_j_df = pd.concat([proteome_norm_sum_j, growth_rate_df], axis=1)\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(proteome_norm_sum_j_df['Growth rates'], proteome_norm_sum_j_df['Protein abundance sum'])\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Translational protein abundance')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merge translational and model protein sector " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Protein abundance sum Growth rates\n", - "Stationary phase 1 day 0.353414 -0.01\n", - "Stationary phase 3 days 0.358775 -0.01\n", - "Chemostat µ=0.12 0.443853 0.12\n", - "Chemostat µ=0.20 0.438787 0.20\n", - "Galactose 0.432214 0.26\n", - "Acetate 0.480055 0.30\n", - "Chemostat µ=0.35 0.437606 0.35\n", - "Pyruvate 0.447661 0.40\n", - "Fumarate 0.437880 0.42\n", - "Succinate 0.445109 0.44\n", - "Glucosamine 0.431779 0.46\n", - "Mannose 0.447771 0.47\n", - "Glycerol 0.423328 0.47\n", - "Chemostat µ=0.5 0.447803 0.50\n", - "Osmotic-stress glucose 0.402520 0.55\n", - "Xylose 0.441492 0.55\n", - "Glucose 0.439572 0.58\n", - "pH6 glucose 0.397945 0.63\n", - "Fructose 0.467924 0.65\n", - "42°C glucose 0.442561 0.66\n", - "Glycerol + AA 0.482146 1.27\n", - "LB 0.531097 1.90\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Protein abundance')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Merge protein abundances\n", - "proteome_norm_sum_merge = proteome_norm_model_sum_df.copy()\n", - "proteome_norm_sum_merge['Protein abundance sum'] = proteome_norm_sum_merge['Protein abundance sum'] + proteome_norm_sum_j_df['Protein abundance sum']\n", - "proteome_norm_sum_merge_sort = proteome_norm_sum_merge.sort_values(by='Growth rates')\n", - "print(proteome_norm_sum_merge_sort)\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(proteome_norm_sum_merge['Growth rates'], proteome_norm_sum_merge['Protein abundance sum'])\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Protein abundance')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean total protein abundance: 0.1401 g/gDW\n" - ] - } - ], - "source": [ - "# mean protein abundances for each condition\n", - "protein_abundance_mean = proteome_norm_sum_merge['Protein abundance sum'].mean()\n", - "print(f'Mean total protein abundance: {round(protein_abundance_mean*0.32, 4)} g/gDW')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Unused Protein Sector" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "#linear relation growth rate and protein abundance\n", - "protein_vs_mu = linregress(x=proteome_norm_sum_merge['Growth rates'], y = proteome_norm_sum_merge['Protein abundance sum']*0.32)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Protein abundance')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "slope = protein_vs_mu.slope\n", - "intercept = protein_vs_mu.intercept\n", - "\n", - "y= [intercept + slope*mu for mu in proteome_norm_sum_merge['Growth rates']]\n", - "#inverse relation is the fraction of unused proteins to fill up the protein space\n", - "y2 = [intercept - slope*mu for mu in proteome_norm_sum_merge['Growth rates']]\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.plot(proteome_norm_sum_merge['Growth rates'],y)\n", - "ax.plot(proteome_norm_sum_merge['Growth rates'],y2)\n", - "ax.scatter(proteome_norm_sum_merge['Growth rates'], proteome_norm_sum_merge['Protein abundance sum']*0.32)\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Protein abundance')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max total protein abundance: 0.16995 g/gDW\n", - "Unused enzymes at zero growth: 0.0407 g/gDW\n", - "Change in unused enzymes with increasing growth rate: -0.0214 g/gDW/h\n" - ] - } - ], - "source": [ - "print(f'Max total protein abundance: {round(proteome_norm_sum_merge[\"Protein abundance sum\"][\"LB\"]*0.32, 5)} g/gDW')\n", - "print('Unused enzymes at zero growth: ', round(proteome_norm_sum_merge['Protein abundance sum']['LB']*0.32-intercept,4), ' g/gDW')\n", - "print('Change in unused enzymes with increasing growth rate: ', -round(slope,4), ' g/gDW/h')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb b/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb deleted file mode 100644 index 360faa4..0000000 --- a/Scripts/.ipynb_checkpoints/create_ecolicore_pam_incl_UE-checkpoint.ipynb +++ /dev/null @@ -1,959 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import cobra\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load PAMpy modules" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy\n", - "Loading PAModelpy modules version 0.0.3.11\n" - ] - } - ], - "source": [ - "# load PAMpy modules\n", - "if os.path.split(os.getcwd())[1] == 'Scripts':\n", - " os.chdir('..')\n", - " \n", - "from src.PAModelpy import PAModel, ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n", - "from src.PAModelpy.PAMValidator import PAMValidator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load E. coli core model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "Objective\n", - "=========\n", - "1.0 BIOMASS_Ecoli_core_w_GAM = 0.8739215069684301\n", - "\n", - "Uptake\n", - "------\n", - "Metabolite Reaction Flux C-Number C-Flux\n", - " glc__D_e EX_glc__D_e 10 6 100.00%\n", - " nh4_e EX_nh4_e 4.765 0 0.00%\n", - " o2_e EX_o2_e 21.8 0 0.00%\n", - " pi_e EX_pi_e 3.215 0 0.00%\n", - "\n", - "Secretion\n", - "---------\n", - "Metabolite Reaction Flux C-Number C-Flux\n", - " co2_e EX_co2_e -22.81 1 100.00%\n", - " h2o_e EX_h2o_e -29.18 0 0.00%\n", - " h_e EX_h_e -17.53 0 0.00%\n", - "\n" - ] - } - ], - "source": [ - "# load the model\n", - "model = cobra.io.load_json_model(os.path.join('Models','e_coli_core.json'))\n", - "# test the model\n", - "sol_t = model.optimize()\n", - "print(model.summary())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Build E. coli core PAM\n", - "## Create Protein Sectors\n", - "### Active Enzymes" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rxnNamerxnEquatEC_nmbrmolMasskcat
rxnID
ALATA_D2D-alanine transaminasepydx5p_c + ala__D_c -> pyam5p_c + pyr_c2.1.2.1, 4.1.2.4840904.48506.862500e+00
SHCHD2Sirohydrochlorin dehydrogenase (NAD)nad_c + dscl_c -> h_c + scl_c + nadh_c1.3.1.7649950.00004.500000e-01
CPPPGOCoproporphyrinogen oxidase (O2 required)2 h_c + o2_c + cpppg3_c -> 2 h2o_c + pppg9_c ...1.3.3.334321.49003.000000e-03
GTHOr_fGlutathione oxidoreductaseh_c + nadph_c + gthox_c -> 2 gthrd_c + nadp_c1.8.1.748771.05007.333000e+02
DHORD5Dihydroorotic acid (menaquinone-8)dhor__S_c + mqn8_c -> orot_c + mql8_c1.3.5.236773.46008.200000e+01
..................
AI2t_bAi2 transport, outer membranemththf_p -> mththf_eNaN39959.48252.200000e+01
RHMND_bL-rhamnonate dehydrataselkdr_c + h2o_c -> rhmn_c4.2.1.9044225.10002.000000e-02
ASPtpp_bL-aspartate uptake via facillitated diffusionasp__L_c -> asp__L_pNaN59427.58001.000000e+08
FUMt1_bFumarate transport via diffusion in (periplasm)fum_c -> fum_pNaN59427.58001.000000e+08
SUCCt1_bSuccinate transport via diffusion in (periplasm)succ_c -> succ_pNaN59427.58001.000000e+08
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2843 rows × 5 columns

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" - ], - "text/plain": [ - " rxnName \\\n", - "rxnID \n", - "ALATA_D2 D-alanine transaminase \n", - "SHCHD2 Sirohydrochlorin dehydrogenase (NAD) \n", - "CPPPGO Coproporphyrinogen oxidase (O2 required) \n", - "GTHOr_f Glutathione oxidoreductase \n", - "DHORD5 Dihydroorotic acid (menaquinone-8) \n", - "... ... \n", - "AI2t_b Ai2 transport, outer membrane \n", - "RHMND_b L-rhamnonate dehydratase \n", - "ASPtpp_b L-aspartate uptake via facillitated diffusion \n", - "FUMt1_b Fumarate transport via diffusion in (periplasm) \n", - "SUCCt1_b Succinate transport via diffusion in (periplasm) \n", - "\n", - " rxnEquat \\\n", - "rxnID \n", - "ALATA_D2 pydx5p_c + ala__D_c -> pyam5p_c + pyr_c \n", - "SHCHD2 nad_c + dscl_c -> h_c + scl_c + nadh_c \n", - "CPPPGO 2 h_c + o2_c + cpppg3_c -> 2 h2o_c + pppg9_c ... \n", - "GTHOr_f h_c + nadph_c + gthox_c -> 2 gthrd_c + nadp_c \n", - "DHORD5 dhor__S_c + mqn8_c -> orot_c + mql8_c \n", - "... ... \n", - "AI2t_b mththf_p -> mththf_e \n", - "RHMND_b lkdr_c + h2o_c -> rhmn_c \n", - "ASPtpp_b asp__L_c -> asp__L_p \n", - "FUMt1_b fum_c -> fum_p \n", - "SUCCt1_b succ_c -> succ_p \n", - "\n", - " EC_nmbr molMass kcat \n", - "rxnID \n", - "ALATA_D2 2.1.2.1, 4.1.2.48 40904.4850 6.862500e+00 \n", - "SHCHD2 1.3.1.76 49950.0000 4.500000e-01 \n", - "CPPPGO 1.3.3.3 34321.4900 3.000000e-03 \n", - "GTHOr_f 1.8.1.7 48771.0500 7.333000e+02 \n", - "DHORD5 1.3.5.2 36773.4600 8.200000e+01 \n", - "... ... ... ... \n", - "AI2t_b NaN 39959.4825 2.200000e+01 \n", - "RHMND_b 4.2.1.90 44225.1000 2.000000e-02 \n", - "ASPtpp_b NaN 59427.5800 1.000000e+08 \n", - "FUMt1_b NaN 59427.5800 1.000000e+08 \n", - "SUCCt1_b NaN 59427.5800 1.000000e+08 \n", - "\n", - "[2843 rows x 5 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load enzyme database from genome-scale E. coli PAM\n", - "enzyme_db = pd.read_excel(os.path.join('Data','proteinAllocationModel_iML1515_EnzymaticData_py.xls'),\n", - " sheet_name='ActiveEnzymes', index_col=0)\n", - "\n", - "# correct reaction IDS\n", - "for idx in enzyme_db.index.to_list():\n", - " # transprt reactions<\n", - " \n", - " if 'pp' in idx:\n", - " idx_new = idx.replace('pp', '')\n", - " if idx_new not in enzyme_db.index:\n", - " enzyme_db.rename(index={idx: idx_new}, inplace=True)\n", - " if 'ex' in idx:\n", - " idx_new = idx.replace('ex', '')\n", - " if idx_new not in enzyme_db.index:\n", - " enzyme_db.rename(index={idx: idx_new}, inplace=True) \n", - "\n", - "enzyme_db" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rxnNamerxnEquatEC_nmbrmolMasskcat
rxnID
GLUt_fL-glutamate transport via diffusion (extracell...glu__L_e -> glu__L_pE039959.4825100000000.0
GLYt_fGlycine transport via diffusion (extracellular...gly_e -> gly_pE139959.4825100000000.0
GLYALDt_fGlyceraldehyde transport via diffusion (extrac...glyald_e -> glyald_pE239959.4825100000000.0
GLYBt_fGlycine betaine transport via diffusion (extra...glyb_e -> glyb_pE339959.4825100000000.0
GLYCt_fGlycerol transport via diffusion (extracellula...glyc_e -> glyc_pE439959.4825100000000.0
..................
METGLCURt_b1-O-methyl-Beta-D-glucuronate via diffusion (e...metglcur_p -> metglcur_eE95339959.4825100000000.0
AI2t_bAi2 transport, outer membranemththf_p -> mththf_eE95439959.482522.0
ASPtpp_bL-aspartate uptake via facillitated diffusionasp__L_c -> asp__L_pE95559427.5800100000000.0
FUMt1_bFumarate transport via diffusion in (periplasm)fum_c -> fum_pE95659427.5800100000000.0
SUCCt1_bSuccinate transport via diffusion in (periplasm)succ_c -> succ_pE95759427.5800100000000.0
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958 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " rxnName \\\n", - "rxnID \n", - "GLUt_f L-glutamate transport via diffusion (extracell... \n", - "GLYt_f Glycine transport via diffusion (extracellular... \n", - "GLYALDt_f Glyceraldehyde transport via diffusion (extrac... \n", - "GLYBt_f Glycine betaine transport via diffusion (extra... \n", - "GLYCt_f Glycerol transport via diffusion (extracellula... \n", - "... ... \n", - "METGLCURt_b 1-O-methyl-Beta-D-glucuronate via diffusion (e... \n", - "AI2t_b Ai2 transport, outer membrane \n", - "ASPtpp_b L-aspartate uptake via facillitated diffusion \n", - "FUMt1_b Fumarate transport via diffusion in (periplasm) \n", - "SUCCt1_b Succinate transport via diffusion in (periplasm) \n", - "\n", - " rxnEquat EC_nmbr molMass kcat \n", - "rxnID \n", - "GLUt_f glu__L_e -> glu__L_p E0 39959.4825 100000000.0 \n", - "GLYt_f gly_e -> gly_p E1 39959.4825 100000000.0 \n", - "GLYALDt_f glyald_e -> glyald_p E2 39959.4825 100000000.0 \n", - "GLYBt_f glyb_e -> glyb_p E3 39959.4825 100000000.0 \n", - "GLYCt_f glyc_e -> glyc_p E4 39959.4825 100000000.0 \n", - "... ... ... ... ... \n", - "METGLCURt_b metglcur_p -> metglcur_e E953 39959.4825 100000000.0 \n", - "AI2t_b mththf_p -> mththf_e E954 39959.4825 22.0 \n", - "ASPtpp_b asp__L_c -> asp__L_p E955 59427.5800 100000000.0 \n", - "FUMt1_b fum_c -> fum_p E956 59427.5800 100000000.0 \n", - "SUCCt1_b succ_c -> succ_p E957 59427.5800 100000000.0 \n", - "\n", - "[958 rows x 5 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#replace NaN enzyme ids with a dummy enzyme identifier\n", - "#select the NaN values \n", - "nan_values = enzyme_db['EC_nmbr'].isnull()\n", - "#make a list with unique ids\n", - "nan_ids = [f'E{i}' for i in range(nan_values.sum())]\n", - "#replace nan values by unique id\n", - "enzyme_db.loc[nan_values, 'EC_nmbr'] = nan_ids\n", - "\n", - "#check if it worked:\n", - "enzyme_db[nan_values]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No enzyme information found for reaction: FRD7\n" - ] - } - ], - "source": [ - "# create enzyme objects for each gene-associated reaction\n", - "kcats = {}\n", - "rxn2ec = {}\n", - "molmass = {}\n", - "for rxn in model.reactions:\n", - " if rxn.genes:\n", - " # correct transport reactions\n", - " if 't' in rxn.id:\n", - " rxn.id = rxn.id\n", - " # are enzyme information in the PAM database?\n", - " rev = 0 # denotes reversibility\n", - " if rxn.lower_bound >= 0:\n", - " # irreversible reaction (forward direction)\n", - " rev = 0\n", - " rxn_id = rxn.id # save reaction ID for retrieveing molar masses/enzyme information later\n", - " if rxn.id in enzyme_db.index:\n", - " kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat']}\n", - " elif rxn.upper_bound <= 0:\n", - " # irreversible reaction (reverse direction)\n", - " rev = 1\n", - " rxn_id = rxn.id + '_b'\n", - " if rxn_id in enzyme_db.index:\n", - " kcats[rxn.id] = {'b': enzyme_db.loc[rxn_id, 'kcat']}\n", - " else:\n", - " rev = 2\n", - " # reversible reaction\n", - " rxn_id_f = rxn.id + '_f'\n", - " rxn_id_b = rxn.id + '_b'\n", - " if rxn_id_f in enzyme_db.index and rxn_id_b in enzyme_db.index:\n", - " rxn_id = rxn_id_f # save reaction ID for retrieveing molar masses/enzyme information later\n", - " kcats[rxn.id] = {'f': enzyme_db.loc[rxn_id_f, 'kcat'],\n", - " 'b': enzyme_db.loc[rxn_id_b, 'kcat']}\n", - "\n", - " else:\n", - " # try if only forward reaction is in database\n", - " rxn_id = rxn.id # save reaction ID for retrieveing molar masses/enzyme information later\n", - " kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat'],\n", - " 'b': enzyme_db.loc[rxn.id, 'kcat']/2} # deduce backwards kcat from forward value\n", - "\n", - " # where enzyme information found?\n", - " if rxn.id in kcats.keys():\n", - " # save molmass\n", - " molmass[rxn.id] = enzyme_db.loc[rxn_id, 'molMass']\n", - " #save enzyme information\n", - " # is enzyme information NaN?\n", - " if pd.isna(enzyme_db.loc[rxn_id, 'EC_nmbr']):\n", - " rxn2ec[rxn.id] = ''\n", - " else:\n", - " rxn2ec[rxn.id] = enzyme_db.loc[rxn_id, 'EC_nmbr']\n", - " \n", - " \n", - " else:\n", - " # no enzyme information found\n", - " print('No enzyme information found for reaction: ' + rxn.id)\n", - " # Create generic Enzyme with mean molar masses and kcat\n", - " if rev == 0:\n", - " kcats[rxn.id] = {'f': 22}\n", - " elif rev == 1: \n", - " kcats[rxn.id] = {'b': 22}\n", - " else:\n", - " kcats[rxn.id] = {'f': 22, 'b': 22}\n", - " \n", - " molmass[rxn.id] = 3.947778784340140e04\n", - "\n", - "rxn2protein = {}\n", - "for rxn, ec in rxn2ec.items():\n", - " ec_dict = {**kcats[rxn], **{'molmass': molmass[rxn], 'protein_reaction_association':[[ec]]}}\n", - " #add enzyme to enzymes related to reaction if these are already stored\n", - " if rxn in rxn2protein.keys():\n", - " rxn2protein[rxn] = {**rxn2protein[rxn], **{ec:ec_dict}}\n", - " #if not create new reaction entry\n", - " else:\n", - " rxn2protein[rxn] = {ec:ec_dict}\n", - "\n", - "# create active enzymes sector\n", - "active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unused Protein Sector" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "id_list_ups = ['BIOMASS_Ecoli_core_w_GAM']\n", - "ups_0 = [0.0407] # g/gDW\n", - "ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate\n", - "molmass_ups = [405903.94] # g/mol\n", - "\n", - "unused_enzyme_sector = UnusedEnzymeSector(\n", - " id_list=id_list_ups,\n", - " ups_0=ups_0,\n", - " ups_mu=ups_mu,\n", - " mol_mass = molmass_ups,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translational Protein Sector" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# translational protein sector parameter (substrate dependent)\n", - "id_list_tps = ['EX_glc__D_e']\n", - "tps_0 = [0.04992] # g/gDW\n", - "tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable\n", - "molmass_tps = [405903.94] # g/mol\n", - "\n", - "# translational protein sector\n", - "translational_enzyme_sector = TransEnzymeSector(\n", - " id_list=id_list_tps,\n", - " tps_0=tps_0,\n", - " tps_mu=tps_mu,\n", - " mol_mass = molmass_tps,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total Protein Constraint" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# total protein constraint (cf. analyze_proteome.ipynb)\n", - "p_tot = 0.16995 # g/gDW -> Standard 0.14 g/gDW " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build PAM\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmpivldy03z.lp\n", - "Reading time = 0.00 seconds\n", - ": 73 rows, 191 columns, 721 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Change total condition-dependent protein constraint from 0.16995 to 0.16995\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - } - ], - "source": [ - "# set up PAM\n", - "pa_model = PAModel(\n", - " id_or_model=model,\n", - " p_tot=p_tot,\n", - " sensitivity = False,\n", - " translational_sector=translational_enzyme_sector,\n", - " unused_sector = unused_enzyme_sector,\n", - " active_sector=active_enzyme_sector,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test PAM model\n", - "## Load phenotype data" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# load phenotype data from excel file\n", - "pt_data = pd.read_excel(os.path.join('Data', 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'),\n", - " sheet_name='Yields', index_col=None)\n", - "pt_data\n", - "\n", - "# extract reaction specific data \n", - "rxn_to_pt = {}\n", - "rxn_transform = {\n", - " 'EX_ac_e': 'EX_ac_e',\n", - " 'EX_co2_e': 'EX_co2_e',\n", - " 'EX_o2_e': 'EX_o2_e',\n", - " 'BIOMASS_Ecoli_core_w_GAM':'BIOMASS_Ec_iML1515_core_75p37M'\n", - "}\n", - "for rxn_id, pt_id in rxn_transform.items():\n", - " rxn_to_pt[rxn_id] = pt_data[['EX_glc__D_e', pt_id]].dropna().rename(columns={pt_id: rxn_id})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate PAM" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Optimal solution with objective value 0.574
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fluxesreduced_costs
PFK7.7871237.372575e-18
PFL10.3239681.626303e-19
PGI4.9577332.818926e-18
PGK-16.8315221.219727e-19
PGL4.9245113.794708e-19
.........
CE_NADH16_1.6.5.1120.9457641.364972e-03
CE_NADTRHD_1.6.1.10.000000-5.955446e-04
CE_NH4t_E1343.1321857.749636e-10
CE_O2t_E8410.4728827.749636e-10
CE_PDH_1.2.4.12.0767836.316039e-03
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163 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pa_model:\n", - " # change glucose uptake rate\n", - " pa_model.reactions.EX_glc__D_e.lower_bound = -6.0\n", - " # solve the model\n", - " sol_pam = pa_model.optimize()\n", - " # print(pa_model.summary())\n", - " # with pd.option_context('display.max_rows', None):\n", - " # print(sol_pam.fluxes)\n", - "pa_model.optimize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scan glucose uptake rates" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - "fluxes = []\n", - "concentrations = [0]\n", - "for glc in glc_uptake_rates:\n", - " with pa_model:\n", - " # change glucose uptake rate\n", - " pa_model.reactions.EX_glc__D_e.lower_bound = -glc\n", - " # disable pyruvate formate lyase (inhibited by oxygen)\n", - " pa_model.reactions.PFL.upper_bound = 0\n", - " # solve the model\n", - " sol_pam = pa_model.optimize()\n", - " # save data\n", - " fluxes.append(sol_pam.fluxes) # flux distributions\n", - " concentration = 0\n", - " for enz_var in pa_model.enzyme_variables:\n", - " concentration += enz_var.concentration\n", - " concentrations.append(concentration)\n", - "\n", - "# plot flux changes with glucose uptake\n", - "rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM']\n", - "fig, axs = plt.subplots(2,2, dpi=100)\n", - "for r, ax in zip(rxn_id, axs.flatten()):\n", - " # plot data\n", - " if r in rxn_to_pt.keys():\n", - " ax.scatter(abs(rxn_to_pt[r]['EX_glc__D_e']), abs(rxn_to_pt[r][r]),\n", - " color='firebrick', marker='o', s=30, linewidths=1.3,\n", - " facecolors=None, zorder=0,\n", - " label='Data')\n", - " \n", - " # plot simulation\n", - " ax.plot(glc_uptake_rates, [abs(f[r]) for f in fluxes],\n", - " label='Simulation', linewidth=2.5,\n", - " zorder=5)\n", - " \n", - " \n", - " # options\n", - " ax.set_xlabel('glc uptake rate [mmol/gDW/h]')\n", - " ax.set_ylabel('flux [mmol/gDW/h]')\n", - " ax.set_title(r)\n", - " # set grid\n", - " ax.grid(True, axis='both', linestyle='--', linewidth=0.5, alpha=0.6 )\n", - " ax.set_axisbelow(True)\n", - " # show legend\n", - " ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1)\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Scripts/Ecoli_core_sensitivity_analysis.ipynb b/Scripts/Ecoli_core_sensitivity_analysis.ipynb deleted file mode 100644 index 9fb073a..0000000 --- a/Scripts/Ecoli_core_sensitivity_analysis.ipynb +++ /dev/null @@ -1,20842 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "9f8b50b7", - "metadata": {}, - "outputs": [], - "source": [ - "import cobra\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import plotly.express\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "id": "7e7cf57d", - "metadata": {}, - "source": [ - "# Load PAMpy modules" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "bd572e36", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading PAModelpy modules version 0.0.3.11\n", - "Loading PAModelpy modules version 0.0.3.3\n" - ] - } - ], - "source": [ - "# load PAMpy modules\n", - "if os.path.split(os.getcwd())[1] == 'Scripts':\n", - " os.chdir('..')\n", - " \n", - "from src.PAModelpy import PAModel, ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n", - "from src.PAModelpy.PAMValidator import PAMValidator\n", - "\n", - "from Scripts.pam_generation import set_up_ecolicore_pam" - ] - }, - { - "cell_type": "markdown", - "id": "8975cb71", - "metadata": {}, - "source": [ - "# Set-up E.coli core proteome allocation model (PAM)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "05f72e15", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No enzyme information found for reaction: FRD7\n", - "Read LP format model from file /tmp/tmpo3jjwb11.lp\n", - "Reading time = 0.00 seconds\n", - ": 72 rows, 190 columns, 720 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: \n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/samiralvdb/.local/lib/python3.10/site-packages/PAModelpy/EnzymeSectors.py:219: UserWarning: FORt: reaction directionality does not match provided kcat values. Skip reaction\n", - " warn(reaction.id + ': reaction directionality does not match provided kcat values. Skip reaction')\n" - ] - } - ], - "source": [ - "# set up PAM using a predefined method\n", - "pamodel = set_up_ecolicore_pam()" - ] - }, - { - "cell_type": "markdown", - "id": "c151400f", - "metadata": {}, - "source": [ - "## 3. Sensitivity of the Ecoli core constraints" - ] - }, - { - "cell_type": "markdown", - "id": "69e02e47", - "metadata": {}, - "source": [ - "### 3.1 simulations with only active enzyme sectors for a range of glucose uptake rates" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f11ff845", - "metadata": {}, - "outputs": [], - "source": [ - "#some usefull functions\n", - "def print_heatmap(xaxis, yaxis, matrix, title = ''):\n", - " fig = plotly.express.imshow(matrix, aspect=\"auto\",\n", - " x = xaxis, y = yaxis,\n", - " labels = dict(x = 'constraint', y='glucose uptake rate [mmol/gcdw/h]'),\n", - " title = title)\n", - " \n", - " fig.update_layout(font=dict(size=20), \n", - " yaxis = dict(tickfont = dict(size=20)),\n", - " xaxis = dict(tickfont = dict(size=8)))\n", - "# fig.update_xaxes(title_font=dict(size=8))\n", - "# fig.update_yaxes(title_font=dict(size=18))\n", - " \n", - " fig.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "c01a90f9", - "metadata": {}, - "outputs": [], - "source": [ - "def print_heatmap_transposed(xaxis, yaxis, matrix, title = '', cbarlabel = ''):\n", - " import matplotlib.pyplot as plt\n", - " \n", - " matrix_t = np.transpose(np.array(matrix))\n", - "\n", - " \n", - " fig, ax = plt.subplots()\n", - " im = ax.imshow(matrix_t)\n", - " cbar = fig.colorbar(im, ax=ax)\n", - " \n", - "# im = ax.imshow(matrix_t)\n", - "\n", - " \n", - " ax.set_xticks(np.arange(len(yaxis)), labels=yaxis)\n", - " ax.set_yticks(np.arange(len(xaxis)), labels=xaxis)\n", - " \n", - " # axes titles\n", - " ax.set_xlabel('Glucose uptake rate [mmol/gcdw/h]', fontsize =16)\n", - " ax.set_ylabel('Enzyme', fontsize =16)\n", - " \n", - "\n", - " # make a colorbar\n", - "# add_colorbar(im)\n", - " \n", - " ax.set_title(title)\n", - " fig.tight_layout()\n", - "# fig.set_figwidth(20)\n", - "# fig.set_figheight(100)\n", - " plt.show()\n", - " \n", - " \n", - "def add_colorbar(im, width=None, pad=None, **kwargs):\n", - "\n", - " l, b, w, h = im.axes.get_position().bounds # get boundaries\n", - " width = width or 0.1 * w # get width of the colorbar\n", - " pad = pad or width # get pad between im and cbar\n", - " fig = im.axes.figure # get figure of image\n", - " cax = fig.add_axes([l + w + pad, b, width, h]) # define cbar Axes\n", - " return fig.colorbar(im, cax=cax, **kwargs) # draw cbar" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "5bb60ce5", - "metadata": {}, - "outputs": [], - "source": [ - "def print_heatmap_matrix_row(xaxis, yaxis, matrix, row, title = '', vmin =0, vmax=1,\n", - " cbarlabel = '', save_to= 'ecolicore_heatmap_maxmu.png'):\n", - " import matplotlib\n", - " \n", - " norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax)\n", - " grid = dict(height_ratios=[1, len(matrix)], width_ratios=[len(matrix[0]), 0.5 ])\n", - " fig, axes = plt.subplots(ncols=2, nrows=2, gridspec_kw = grid)\n", - "\n", - " plt.rcParams['font.size'] = '16'\n", - "\n", - " im = axes[1,0].imshow(matrix, aspect=\"auto\", cmap=\"viridis\", vmin = vmin, vmax =vmax)\n", - " axes[0,0].imshow(row, aspect=\"auto\", cmap=\"viridis\")\n", - "\n", - " axes[0,0].get_xaxis().set_visible(False)\n", - " axes[0,1].axis('off')\n", - "\n", - " axes[1,0].set_xlabel('Glucose uptake rate [mmol/gcdw/h]', fontsize =16)\n", - " axes[1,0].set_ylabel('Enzyme', fontsize =16)\n", - "\n", - " # Show all ticks and label them with the respective list entries\n", - " axes[0,0].set_yticks(np.arange(1), labels=['Total Protein'], fontsize =16)\n", - " axes[1,0].set_xticks(np.arange(len(yaxis)), labels=yaxis, fontsize =16)\n", - " axes[1,0].set_yticks(np.arange(len(xaxis)), labels=xaxis, fontsize =16)\n", - "\n", - " sm = matplotlib.cm.ScalarMappable(cmap=\"viridis\", norm = norm)\n", - " sm.set_array([])\n", - "\n", - " fig.subplots_adjust(top=0.4,bottom=0.1) \n", - " fig.colorbar(sm,label = cbarlabel, ax =axes, cax=axes[1,1], shrink = 1.3, fraction = 1.5)\n", - "\n", - " fig.tight_layout()\n", - " fig.set_figwidth(20)\n", - " fig.set_figheight(20)\n", - " plt.savefig('../Results/'+save_to, dpi =100)\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "3efd9e27", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "glucose uptake rate 0.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.0 \n", - "\n", - "glucose uptake rate 1.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.113747 \n", - "\n", - "glucose uptake rate 2.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.076687 \n", - "\n", - "glucose uptake rate 2.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.057647 \n", - "\n", - "glucose uptake rate 3.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.046055 \n", - "\n", - "glucose uptake rate 3.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.038255 \n", - "\n", - "glucose uptake rate 4.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.032648 \n", - "\n", - "glucose uptake rate 4.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.028423 \n", - "\n", - "glucose uptake rate 5.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.025126 \n", - "\n", - "glucose uptake rate 5.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.491201 \n", - "\n", - "glucose uptake rate 6.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.457812 \n", - "\n", - "glucose uptake rate 6.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.427968 \n", - "\n", - "glucose uptake rate 7.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.53318 \n", - "\n", - "glucose uptake rate 7.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.505137 \n", - "\n", - "glucose uptake rate 8.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.955718 \n", - "\n", - "glucose uptake rate 8.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.932464 \n", - "\n", - "glucose uptake rate 9.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.565391 \n", - "\n", - "glucose uptake rate 9.5 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.580651 \n", - "\n", - "glucose uptake rate 10.0 mmol/gcdw/h\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 1.597214 \n", - "\n" - ] - }, - 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}, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "tickfont": { - "size": 8 - }, - "title": { - "text": "constraint" - } - }, - "yaxis": { - "anchor": "x", - "autorange": "reversed", - "domain": [ - 0, - 1 - ], - "tickfont": { - "size": 20 - }, - "title": { - "text": "glucose uptake rate [mmol/gcdw/h]" - } - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - "Ccsc = []\n", - "Cesc = []\n", - "y_axis = []\n", - "fluxes = []\n", - "xaxis_csc = []\n", - " \n", - "# disable pyruvate formate lyase (inhibited by oxygen)\n", - "pamodel.change_reaction_bounds(rxn_id = 'PFL', upper_bound = 0)\n", - " \n", - "for glc in glc_uptake_rates:\n", - " print('glucose uptake rate ', glc, ' mmol/gcdw/h')\n", - " with pamodel:\n", - " # change glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -glc, upper_bound = -glc)\n", - " # solve the model\n", - " sol_pam = pamodel.optimize()\n", - " fluxes.append(sol_pam.fluxes)\n", - " if pamodel.solver.status == 'optimal': y_axis += [glc]\n", - " # save data\n", - " Ccsc_new = list()\n", - " if pamodel.solver.status == 'optimal':\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - "\n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of enzyme sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6),'\\n')\n", - "\n", - "for cc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if cc == 'flux_ub' or cc == 'flux_lb':\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[capacity_coeff['constraint'] == cc].rxn_id.to_list()]\n", - " else:\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == cc].enzyme_id.to_list()]\n", - " \n", - "xaxis_esc = enzyme_coeff.enzyme_id.to_list()\n", - " \n", - "#make yaxis\n", - "\n", - "print_heatmap(xaxis_csc, y_axis, Ccsc, title = 'Capacity Sensitivity Coefficients')\n", - "print_heatmap(xaxis_esc, y_axis, Cesc, title = 'Enzyme Sensitivity Coefficients')" - ] - }, - { - "cell_type": "markdown", - "id": "4a9ad912", - "metadata": {}, - "source": [ - "## 4. Changing the optimization objective" - ] - }, - { - "cell_type": "markdown", - "id": "cdd569a8", - "metadata": {}, - "source": [ - "### 4.1: pfba: minimize proteins after maximizing growth\n", - "fraction of optimum: 0.8, allowing 20% change of max growth rate" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "3d89ea2d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.789713 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.973766\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.086677 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.973382\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.08275 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.97295\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.080169 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.972402\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.08151 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 0.971831\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.082906 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.095023 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.097581 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.109082 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.127212 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.151346 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.167514 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.184968 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.203867 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.224397 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.24678 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.271278 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.298207 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.365924 \n", - "\n", - "Sum of capacity sensitivity coefficients: \t \t \t \t \t \t 1.0\n", - "Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t 0.411097 \n", - "\n", - "20 20\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "coloraxis": "coloraxis", - "hovertemplate": "constraint: %{x}
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#set the correct biomass reaction id\n", - "pa_model.BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'\n", - "frac_opt = 0.8\n", - "\n", - "glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - "Ccsc = []\n", - "Cesc = []\n", - "y_axis = []\n", - "fluxes = []\n", - "xaxis_csc = []\n", - " \n", - "\n", - "# disable pyruvate formate lyase (inhibited by oxygen)\n", - "pa_model.change_reaction_bounds(rxn_id = 'PFL', upper_bound = 0)\n", - "\n", - "for glc in glc_uptake_rates:\n", - " y_axis += [glc]\n", - " with pamodel:\n", - " # change glucose uptake rate\n", - " pamodel.change_reaction_bounds(rxn_id = 'EX_glc__D_e', \n", - " lower_bound = -glc, upper_bound = -glc)\n", - " #reset the objective to maximize growth\n", - " pamodel.objective = 'BIOMASS_Ecoli_core_w_GAM'\n", - " # solve the model\n", - " pamodel.optimize()\n", - " #if optimal, perform pFBA: minimization of sum of total proteins\n", - " if pamodel.solver.status == 'optimal':\n", - " #set the growth rate\n", - " growth_rate = pamodel.reactions.get_by_id('BIOMASS_Ecoli_core_w_GAM').flux\n", - " pamodel.reactions.get_by_id('BIOMASS_Ecoli_core_w_GAM').bound = frac_opt* growth_rate, (2-frac_opt)*growth_rate\n", - " #make the new objective\n", - " pamodel.objective = {enz:1 for enz in pamodel.enzyme_variables}\n", - " #also account for reverse enzymes\n", - " pamodel.objective.set_linear_coefficients({enz.reverse_variable:-1 for enz in pamodel.enzyme_variables})\n", - " #run the model\n", - " pamodel.optimize()\n", - " # save data\n", - " Ccsc_new = list()\n", - " if pamodel.solver.status == 'optimal':\n", - " capacity_coeff = pamodel.capacity_sensitivity_coefficients\n", - " for csc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list()\n", - " \n", - " Ccsc += [Ccsc_new]\n", - "\n", - " enzyme_coeff = pamodel.enzyme_sensitivity_coefficients\n", - " Cesc += [enzyme_coeff.coefficient.to_list()]\n", - " \n", - " print('Sum of capacity sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Ccsc_new),6))\n", - " print('Sum of enzyme sensitivity coefficients: \\t \\t \\t \\t \\t \\t', round(sum(Cesc[-1]),6),'\\n')\n", - "\n", - "for cc in ['flux_ub', 'flux_lb', 'enzyme_max','enzyme_min','proteome', 'sector']:\n", - " if cc == 'flux_ub' or cc == 'flux_lb':\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[capacity_coeff['constraint'] == cc].rxn_id.to_list()]\n", - " else:\n", - " xaxis_csc += [coef+'_'+ cc for coef in capacity_coeff[\n", - " capacity_coeff['constraint'] == cc].enzyme_id.to_list()]\n", - " \n", - "xaxis_esc = enzyme_coeff.enzyme_id.to_list()\n", - "\n", - "print_heatmap(xaxis_csc, y_axis, Ccsc, title = 'Capacity Sensitivity Coefficients')\n", - "print_heatmap(xaxis_esc, y_axis, Cesc, title = 'Enzyme Sensitivity Coefficients')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4ea093fb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/Scripts/analyze_proteome.ipynb b/Scripts/analyze_proteome.ipynb deleted file mode 100644 index ac994ae..0000000 --- a/Scripts/analyze_proteome.ipynb +++ /dev/null @@ -1,2072 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Analyze the proteome for the E. coli core model\n", - "- Map measured protein abundaces to model genes/enzymes" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import cobra\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import linregress" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load proteome data\n", - "## Protein abundances" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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2359 rows × 22 columns

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" - ], - "text/plain": [ - " Glucose LB Glycerol + AA Acetate Fumarate Glucosamine \\\n", - "Bnumber \n", - "b3988 0.715349 1.844515 1.159331 0.561253 0.629318 0.853642 \n", - "b3987 0.989135 2.221614 1.299346 0.665163 0.799515 0.999545 \n", - "b0118 1.178283 2.575086 2.722244 3.543745 3.023680 2.133445 \n", - "b2557 0.576304 0.192554 0.548796 0.337313 0.486028 0.459618 \n", - "b3212 0.774653 0.163742 0.176652 0.369212 0.347026 0.535762 \n", - "... ... ... ... ... ... ... \n", - "NaN 0.000167 0.001322 NaN 0.000097 0.000504 0.000339 \n", - "NaN 0.000084 0.000012 0.000002 0.000249 0.000235 0.000125 \n", - "NaN 0.002472 0.000022 0.000066 0.000770 0.000334 0.000590 \n", - "NaN 0.000119 0.000300 0.000166 0.000093 0.000142 0.000094 \n", - "NaN 0.001575 0.000632 NaN 0.000451 0.000526 0.000346 \n", - "\n", - " Glycerol Pyruvate Chemostat µ=0.5 Chemostat µ=0.35 ... \\\n", - "Bnumber ... \n", - "b3988 0.732347 0.702820 1.230766 1.004203 ... \n", - "b3987 0.936990 0.928398 1.310899 1.096833 ... \n", - "b0118 1.674174 2.543362 2.440666 3.143073 ... \n", - "b2557 0.485143 0.635390 0.536179 0.405983 ... \n", - "b3212 0.597161 0.458132 0.629009 0.530940 ... \n", - "... ... ... ... ... ... \n", - "NaN 0.000170 0.000183 0.000151 0.000068 ... \n", - "NaN 0.000252 0.000419 0.000644 0.000241 ... \n", - "NaN 0.000709 0.000805 0.000652 0.000320 ... \n", - "NaN 0.000113 0.000125 0.000088 0.000096 ... \n", - "NaN 0.000367 0.000291 0.000656 0.001451 ... \n", - "\n", - " Stationary phase 1 day Stationary phase 3 days \\\n", - "Bnumber \n", - "b3988 4.466397e-01 4.910739e-01 \n", - "b3987 5.944920e-01 5.565920e-01 \n", - "b0118 1.447816e-01 1.173512e-01 \n", - "b2557 1.449196e-01 1.724214e-01 \n", - "b3212 6.220451e-02 4.961751e-02 \n", - "... ... ... \n", - "NaN 1.524261e-04 0.000000e+00 \n", - "NaN 6.333976e-07 6.237618e-07 \n", - "NaN 3.374665e-07 1.943465e-04 \n", - "NaN 2.921593e-07 2.642997e-06 \n", - "NaN 4.131408e-05 1.144572e-04 \n", - "\n", - " Osmotic-stress glucose 42°C glucose pH6 glucose Xylose \\\n", - "Bnumber \n", - "b3988 0.578723 1.026062e+00 0.859739 0.934963 \n", - "b3987 0.813304 1.230390e+00 1.007687 1.109002 \n", - "b0118 0.436610 7.511055e-01 0.721180 1.038963 \n", - "b2557 0.309267 4.423852e-01 0.400793 0.403734 \n", - "b3212 0.225865 8.072853e-01 0.737951 0.676351 \n", - "... ... ... ... ... \n", - "NaN 0.000010 2.769145e-04 0.000304 NaN \n", - "NaN 0.000004 9.527910e-06 0.000001 0.000001 \n", - "NaN 0.000770 1.372202e-03 0.001054 0.001213 \n", - "NaN 0.000014 5.439283e-07 0.000035 0.000022 \n", - "NaN 0.000012 3.876813e-04 0.000049 NaN \n", - "\n", - " Mannose Galactose Succinate Fructose \n", - "Bnumber \n", - "b3988 8.757239e-01 5.813814e-01 7.116349e-01 1.162894 \n", - "b3987 1.035491e+00 7.049528e-01 9.635688e-01 1.317061 \n", - "b0118 2.562241e+00 1.997324e+00 3.032313e+00 1.362394 \n", - "b2557 4.139472e-01 3.684797e-01 4.489504e-01 0.564445 \n", - "b3212 5.631967e-01 3.788294e-01 4.207655e-01 1.045406 \n", - "... ... ... ... ... \n", - "NaN NaN 6.570810e-05 2.050574e-04 NaN \n", - "NaN 1.052190e-06 8.777261e-07 1.026332e-06 0.000001 \n", - "NaN 9.174705e-04 3.517107e-04 2.757719e-04 0.001285 \n", - "NaN 4.876650e-07 1.863777e-05 4.725847e-07 0.000013 \n", - "NaN NaN 3.836446e-04 2.818039e-04 NaN \n", - "\n", - "[2359 rows x 22 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load proteome data (Schmidt et al. 2016)\n", - "proteome_df = pd.read_excel('../Data/proteome_data_extract_schmidt2016.xlsx',\n", - " sheet_name='ProteinMasses',\n", - " engine='openpyxl',\n", - " index_col=0)\n", - "\n", - "proteome_df\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GlucoseLBGlycerol + AAAcetateFumarateGlucosamineGlycerolPyruvateChemostat µ=0.5Chemostat µ=0.35...Stationary phase 1 dayStationary phase 3 daysOsmotic-stress glucose42°C glucosepH6 glucoseXyloseMannoseGalactoseSuccinateFructose
Bnumber
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..................................................................
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2359 rows × 22 columns

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" - ], - "text/plain": [ - " Glucose LB Glycerol + AA Acetate \\\n", - "Bnumber \n", - "b3988 2.941474e-03 5.026393e-03 3.550227e-03 2.858254e-03 \n", - "b3987 4.067266e-03 6.054005e-03 3.978997e-03 3.387428e-03 \n", - "b0118 4.845034e-03 7.017233e-03 8.336348e-03 1.804699e-02 \n", - "b2557 2.369729e-03 5.247197e-04 1.680582e-03 1.717809e-03 \n", - "b3212 3.185331e-03 4.462054e-04 5.409631e-04 1.880260e-03 \n", - "... ... ... ... ... \n", - "NaN 6.851077e-07 3.602707e-06 NaN 4.952390e-07 \n", - "NaN 3.436903e-07 3.297128e-08 4.692171e-09 1.266535e-06 \n", - "NaN 1.016463e-05 5.960815e-08 2.028619e-07 3.922468e-06 \n", - "NaN 4.873215e-07 8.167594e-07 5.090973e-07 4.756701e-07 \n", - "NaN 6.477288e-06 1.722441e-06 NaN 2.295474e-06 \n", - "\n", - " Fumarate Glucosamine Glycerol Pyruvate \\\n", - "Bnumber \n", - "b3988 2.896226e-03 3.786753e-03 3.247723e-03 3.286201e-03 \n", - "b3987 3.679503e-03 4.433976e-03 4.155249e-03 4.340946e-03 \n", - "b0118 1.391549e-02 9.463954e-03 7.424421e-03 1.189210e-02 \n", - "b2557 2.236781e-03 2.038865e-03 2.151453e-03 2.970919e-03 \n", - "b3212 1.597071e-03 2.376639e-03 2.648216e-03 2.142107e-03 \n", - "... ... ... ... ... \n", - "NaN 2.317645e-06 1.502131e-06 7.546521e-07 8.554820e-07 \n", - "NaN 1.081327e-06 5.536750e-07 1.118401e-06 1.958754e-06 \n", - "NaN 1.535391e-06 2.616758e-06 3.142143e-06 3.764873e-06 \n", - "NaN 6.550601e-07 4.167513e-07 5.023276e-07 5.839774e-07 \n", - "NaN 2.420377e-06 1.535065e-06 1.626043e-06 1.361277e-06 \n", - "\n", - " Chemostat µ=0.5 Chemostat µ=0.35 ... Stationary phase 1 day \\\n", - "Bnumber ... \n", - "b3988 5.342251e-03 4.889101e-03 ... 3.199374e-03 \n", - "b3987 5.690078e-03 5.340084e-03 ... 4.258471e-03 \n", - "b0118 1.059393e-02 1.530249e-02 ... 1.037101e-03 \n", - "b2557 2.327332e-03 1.976587e-03 ... 1.038090e-03 \n", - "b3212 2.730272e-03 2.584953e-03 ... 4.455840e-04 \n", - "... ... ... ... ... \n", - "NaN 6.538958e-07 3.325533e-07 ... 1.091860e-06 \n", - "NaN 2.794743e-06 1.172058e-06 ... 4.537160e-09 \n", - "NaN 2.828644e-06 1.556013e-06 ... 2.417344e-09 \n", - "NaN 3.815181e-07 4.679845e-07 ... 2.092799e-09 \n", - "NaN 2.845535e-06 7.065384e-06 ... 2.959414e-07 \n", - "\n", - " Stationary phase 3 days Osmotic-stress glucose 42°C glucose \\\n", - "Bnumber \n", - "b3988 3.536290e-03 2.428557e-03 4.022638e-03 \n", - "b3987 4.008095e-03 3.412954e-03 4.823697e-03 \n", - "b0118 8.450620e-04 1.832193e-03 2.944681e-03 \n", - "b2557 1.241630e-03 1.297808e-03 1.734354e-03 \n", - "b3212 3.573025e-04 9.478229e-04 3.164931e-03 \n", - "... ... ... ... \n", - "NaN 0.000000e+00 4.164754e-08 1.085633e-06 \n", - "NaN 4.491794e-09 1.575463e-08 3.735381e-08 \n", - "NaN 1.399516e-06 3.232982e-06 5.379667e-06 \n", - "NaN 1.903258e-08 6.027465e-08 2.132450e-09 \n", - "NaN 8.242219e-07 5.154440e-08 1.519890e-06 \n", - "\n", - " pH6 glucose Xylose Mannose Galactose Succinate \\\n", - "Bnumber \n", - "b3988 3.431053e-03 3.932964e-03 3.892800e-03 3.075565e-03 3.226823e-03 \n", - "b3987 4.021487e-03 4.665070e-03 4.603003e-03 3.729270e-03 4.369188e-03 \n", - "b0118 2.878090e-03 4.370448e-03 1.138977e-02 1.056604e-02 1.374966e-02 \n", - "b2557 1.599490e-03 1.698326e-03 1.840094e-03 1.949294e-03 2.035712e-03 \n", - "b3212 2.945022e-03 2.845100e-03 2.503543e-03 2.004045e-03 1.907911e-03 \n", - "... ... ... ... ... ... \n", - "NaN 1.214564e-06 NaN NaN 3.476024e-07 9.298084e-07 \n", - "NaN 4.633229e-09 4.624998e-09 4.677236e-09 4.643258e-09 4.653780e-09 \n", - "NaN 4.205954e-06 5.102658e-06 4.078374e-06 1.860585e-06 1.250455e-06 \n", - "NaN 1.413368e-07 9.141116e-08 2.167786e-09 9.859566e-08 2.142879e-09 \n", - "NaN 1.944331e-07 NaN NaN 2.029518e-06 1.277806e-06 \n", - "\n", - " Fructose \n", - "Bnumber \n", - "b3988 4.598847e-03 \n", - "b3987 5.208525e-03 \n", - "b0118 5.387801e-03 \n", - "b2557 2.232186e-03 \n", - "b3212 4.134224e-03 \n", - "... ... \n", - "NaN NaN \n", - "NaN 4.681092e-09 \n", - "NaN 5.079908e-06 \n", - "NaN 5.000788e-08 \n", - "NaN NaN \n", - "\n", - "[2359 rows x 22 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# normalize protein abundances to total protein abundance\n", - "sum_proteines = proteome_df.sum(axis=0)\n", - "proteome_norm_df = proteome_df / sum_proteines\n", - "proteome_norm_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Growth rates" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Growth rates
Condition
LB1.90
Glycerol + AA1.27
Acetate0.30
Fumarate0.42
Galactose0.26
Glucose0.58
Glucosamine0.46
Glycerol0.47
Pyruvate0.40
Succinate0.44
Fructose0.65
Mannose0.47
Xylose0.55
Osmotic-stress glucose0.55
42°C glucose0.66
pH6 glucose0.63
Stationary phase 1 day-0.01
Stationary phase 3 days-0.01
Chemostat µ=0.120.12
Chemostat µ=0.200.20
Chemostat µ=0.350.35
Chemostat µ=0.50.50
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" - ], - "text/plain": [ - " Growth rates\n", - "Condition \n", - "LB 1.90\n", - "Glycerol + AA 1.27\n", - "Acetate 0.30\n", - "Fumarate 0.42\n", - "Galactose 0.26\n", - "Glucose 0.58\n", - "Glucosamine 0.46\n", - "Glycerol 0.47\n", - "Pyruvate 0.40\n", - "Succinate 0.44\n", - "Fructose 0.65\n", - "Mannose 0.47\n", - "Xylose 0.55\n", - "Osmotic-stress glucose 0.55\n", - "42°C glucose 0.66\n", - "pH6 glucose 0.63\n", - "Stationary phase 1 day -0.01\n", - "Stationary phase 3 days -0.01\n", - "Chemostat µ=0.12 0.12\n", - "Chemostat µ=0.20 0.20\n", - "Chemostat µ=0.35 0.35\n", - "Chemostat µ=0.5 0.50" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load growth rate data [1/h] (Schmidt et al. 2016)\n", - "growth_rate_df = pd.read_excel('../Data/proteome_data_extract_schmidt2016.xlsx',\n", - " sheet_name='GrowthRates',\n", - " engine='openpyxl',\n", - " index_col=0\n", - " )\n", - "growth_rate_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## COG" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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COG IDCOG Name
Bnumber
b3988KTranscription
b3987KTranscription
b0118CEnergy production and conversion
b2557FNucleotide transport and metabolism
b3212EAmino acid transport and metabolism
.........
NaN-NaN
NaN-NaN
NaN-NaN
NaN-NaN
NaN-NaN
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2359 rows × 2 columns

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" - ], - "text/plain": [ - " COG ID COG Name\n", - "Bnumber \n", - "b3988 K Transcription\n", - "b3987 K Transcription\n", - "b0118 C Energy production and conversion\n", - "b2557 F Nucleotide transport and metabolism\n", - "b3212 E Amino acid transport and metabolism\n", - "... ... ...\n", - "NaN - NaN\n", - "NaN - NaN\n", - "NaN - NaN\n", - "NaN - NaN\n", - "NaN - NaN\n", - "\n", - "[2359 rows x 2 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load conversion table from gene to COG\n", - "gene2cog_df = pd.read_excel('../Data/proteome_data_extract_schmidt2016.xlsx',\n", - " sheet_name='Gene2COG',\n", - " engine='openpyxl',\n", - " index_col=0)\n", - "gene2cog_df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load model" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n" - ] - } - ], - "source": [ - "model = cobra.io.load_json_model('../Models/e_coli_core.json')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# General proteome analysis\n", - "## RNA polymerase" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# analyze RNA polymerase abundances\n", - "# rnap_genes = ['b3988', 'b3987', 'b3295', 'b3067', 'b2741', 'b3202', 'b3649', 'b2573']\n", - "rnap_genes = ['b3295', 'b3987', 'b3988'], # rpoA, rpoB, rpoC -> RNA polymerase core enzyme \n", - "# get abundaces of RNA polymerase genes (rnap_genes)\n", - "rnap_fractions_ds = proteome_norm_df.loc[rnap_genes].sum(axis=0)\n", - "rnap_fractions_ds.rename('Proteinfraction', inplace=True)\n", - "\n", - "# merge sum of protein abundaces with growth rate data\n", - "rnap_abundances_df = pd.concat([rnap_fractions_ds, growth_rate_df], axis=1)\n", - "rnap_abundances_df\n", - "\n", - "# print as scatter plot\n", - "rnap_abundances_df.plot.scatter(x='Growth rates', y='Proteinfraction')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Analyze proteome of E. coli core model\n", - "## Protein abundance of model proteins" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "s0001 not in proteome data\n", - "b3115 tdcD not in proteome data\n", - "b0116 lpd not in proteome data\n", - "b3739 atpI not in proteome data\n", - "b0979 cbdB not in proteome data\n", - "b3603 lldP not in proteome data\n", - "b1773 ydjI not in proteome data\n", - "b2492 focB not in proteome data\n", - "b4152 frdC not in proteome data\n", - "b4151 frdD not in proteome data\n", - "b1621 malX not in proteome data\n", - "b1524 glsB not in proteome data\n", - "b0485 glsA not in proteome data\n", - "b0875 aqpZ not in proteome data\n", - "b2280 nuoJ not in proteome data\n", - "b2579 grcA not in proteome data\n", - "b3951 pflD not in proteome data\n", - "b3952 pflC not in proteome data\n", - "b3612 gpmM not in proteome data\n", - "b4395 ytjC not in proteome data\n", - "b2987 pitB not in proteome data\n", - "b3403 pck not in proteome data\n", - "b2458 eutD not in proteome data\n", - "b4301 sgcE not in proteome data\n" - ] - }, - { - "data": { - "text/html": [ - "
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GlucoseLBGlycerol + AAAcetateFumarateGlucosamineGlycerolPyruvateChemostat µ=0.5Chemostat µ=0.35...Stationary phase 1 dayStationary phase 3 daysOsmotic-stress glucose42°C glucosepH6 glucoseXyloseMannoseGalactoseSuccinateFructose
Bnumber
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b03510.0000580.000030NaN0.0000220.0000270.0000330.0000240.0000210.0000390.000051...0.0000140.0000110.0000490.0000410.000044NaNNaN0.0000170.000034NaN
b18490.0006880.0000700.0002780.0002630.0004090.0005290.0004440.0007770.0004330.000316...0.0003380.0003380.0004950.0006150.0004630.0004460.0004460.0004670.0003960.000381
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..................................................................
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113 rows × 22 columns

\n", - "
" - ], - "text/plain": [ - " Glucose LB Glycerol + AA Acetate Fumarate Glucosamine \\\n", - "Bnumber \n", - "b1241 0.003181 0.002378 0.002752 0.003663 0.002941 0.005208 \n", - "b0351 0.000058 0.000030 NaN 0.000022 0.000027 0.000033 \n", - "b1849 0.000688 0.000070 0.000278 0.000263 0.000409 0.000529 \n", - "b2296 0.000904 0.001685 0.000870 0.000533 0.000792 0.000658 \n", - "b1276 0.000397 0.000431 0.000390 0.001321 0.000994 0.001096 \n", - "... ... ... ... ... ... ... \n", - "b0008 0.002553 0.002517 0.003291 0.002000 0.002605 0.002547 \n", - "b2464 0.000352 0.000154 0.000202 0.000400 0.000348 0.000389 \n", - "b2465 0.000375 0.000125 0.000185 0.000594 0.000509 0.000502 \n", - "b2935 0.001913 0.002455 0.001935 0.001624 0.002019 0.001706 \n", - "b3919 0.001368 0.000917 0.001329 0.001758 0.001597 0.001466 \n", - "\n", - " Glycerol Pyruvate Chemostat µ=0.5 Chemostat µ=0.35 ... \\\n", - "Bnumber ... \n", - "b1241 0.003668 0.002455 0.007168 0.005931 ... \n", - "b0351 0.000024 0.000021 0.000039 0.000051 ... \n", - "b1849 0.000444 0.000777 0.000433 0.000316 ... \n", - "b2296 0.000824 0.002779 0.000625 0.000706 ... \n", - "b1276 0.000581 0.000595 0.000690 0.001024 ... \n", - "... ... ... ... ... ... \n", - "b0008 0.002926 0.003065 0.002528 0.001977 ... \n", - "b2464 0.000358 0.000235 0.000248 0.000306 ... \n", - "b2465 0.000496 0.000356 0.000466 0.000552 ... \n", - "b2935 0.001802 0.002058 0.001762 0.001647 ... \n", - "b3919 0.001479 0.001427 0.001808 0.001536 ... \n", - "\n", - " Stationary phase 1 day Stationary phase 3 days \\\n", - "Bnumber \n", - "b1241 0.008040 0.007712 \n", - "b0351 0.000014 0.000011 \n", - "b1849 0.000338 0.000338 \n", - "b2296 0.001196 0.001083 \n", - "b1276 0.000623 0.000662 \n", - "... ... ... \n", - "b0008 0.002751 0.003207 \n", - "b2464 0.001226 0.001371 \n", - "b2465 0.000998 0.001070 \n", - "b2935 0.000767 0.000746 \n", - "b3919 0.002204 0.002696 \n", - "\n", - " Osmotic-stress glucose 42°C glucose pH6 glucose Xylose \\\n", - "Bnumber \n", - "b1241 0.003491 0.005646 0.005738 0.005630 \n", - "b0351 0.000049 0.000041 0.000044 NaN \n", - "b1849 0.000495 0.000615 0.000463 0.000446 \n", - "b2296 0.000880 0.000948 0.001086 0.001512 \n", - "b1276 0.000407 0.000424 0.000427 0.000344 \n", - "... ... ... ... ... \n", - "b0008 0.003328 0.004449 0.003079 0.003284 \n", - "b2464 0.000501 0.000534 0.000893 0.000297 \n", - "b2465 0.000496 0.000485 0.000784 0.000334 \n", - "b2935 0.001250 0.001583 0.001521 0.001988 \n", - "b3919 0.002496 0.001431 0.002680 0.001845 \n", - "\n", - " Mannose Galactose Succinate Fructose \n", - "Bnumber \n", - "b1241 0.004238 0.005336 0.002931 0.012611 \n", - "b0351 NaN 0.000017 0.000034 NaN \n", - "b1849 0.000446 0.000467 0.000396 0.000381 \n", - "b2296 0.001176 0.001133 0.001115 0.001357 \n", - "b1276 0.000912 0.001122 0.000867 0.000433 \n", - "... ... ... ... ... \n", - "b0008 0.002368 0.002386 0.002547 0.002890 \n", - "b2464 0.000343 0.000810 0.000297 0.000231 \n", - "b2465 0.000523 0.000955 0.000469 0.000319 \n", - "b2935 0.001700 0.001030 0.002021 0.001738 \n", - "b3919 0.001238 0.001751 0.001268 0.001653 \n", - "\n", - "[113 rows x 22 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "genes_in_dataset = []\n", - "for g in model.genes:\n", - " if g.id in proteome_norm_df.index:\n", - " genes_in_dataset.append(g.id)\n", - " else:\n", - " print(f'{g.id} {g.name} not in proteome data')\n", - "\n", - "# extract protein abundances for genes in dataset\n", - "proteome_norm_model_df = proteome_norm_df.loc[genes_in_dataset]\n", - "proteome_norm_model_df" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Model protein abundance')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# sum protein abundances for each condition\n", - "proteome_norm_model_sum_ds = proteome_norm_model_df.sum(axis=0)\n", - "proteome_norm_model_sum_ds.rename('Protein abundance sum', inplace=True)\n", - "\n", - "# merge sum of protein abundaces with growth rate data\n", - "proteome_norm_model_sum_df = pd.concat([proteome_norm_model_sum_ds, growth_rate_df], axis=1)\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(proteome_norm_model_sum_df['Growth rates'], proteome_norm_model_sum_df['Protein abundance sum'])\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Model protein abundance')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Translational sector (J)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Translational protein abundance')" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# sum protein abundances of proteins from COG J\n", - "# get Bnumbers associated with COG ID J from gene2cog_df\n", - "bnumbers_j = gene2cog_df.loc[gene2cog_df['COG ID'] == 'J'].index.to_list()\n", - "\n", - "# sum protein abundances of translational protein sector\n", - "proteome_norm_sum_j = proteome_norm_df.loc[bnumbers_j].sum(axis=0)\n", - "proteome_norm_sum_j.rename('Protein abundance sum', inplace=True)\n", - "\n", - "# merge with growth rates\n", - "proteome_norm_sum_j_df = pd.concat([proteome_norm_sum_j, growth_rate_df], axis=1)\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(proteome_norm_sum_j_df['Growth rates'], proteome_norm_sum_j_df['Protein abundance sum'])\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Translational protein abundance')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Merge translational and model protein sector " - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Protein abundance sum Growth rates\n", - "Stationary phase 1 day 0.353414 -0.01\n", - "Stationary phase 3 days 0.358775 -0.01\n", - "Chemostat µ=0.12 0.443853 0.12\n", - "Chemostat µ=0.20 0.438787 0.20\n", - "Galactose 0.432214 0.26\n", - "Acetate 0.480055 0.30\n", - "Chemostat µ=0.35 0.437606 0.35\n", - "Pyruvate 0.447661 0.40\n", - "Fumarate 0.437880 0.42\n", - "Succinate 0.445109 0.44\n", - "Glucosamine 0.431779 0.46\n", - "Mannose 0.447771 0.47\n", - "Glycerol 0.423328 0.47\n", - "Chemostat µ=0.5 0.447803 0.50\n", - "Osmotic-stress glucose 0.402520 0.55\n", - "Xylose 0.441492 0.55\n", - "Glucose 0.439572 0.58\n", - "pH6 glucose 0.397945 0.63\n", - "Fructose 0.467924 0.65\n", - "42°C glucose 0.442561 0.66\n", - "Glycerol + AA 0.482146 1.27\n", - "LB 0.531097 1.90\n" - ] - }, - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Protein abundance')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Merge protein abundances\n", - "proteome_norm_sum_merge = proteome_norm_model_sum_df.copy()\n", - "proteome_norm_sum_merge['Protein abundance sum'] = proteome_norm_sum_merge['Protein abundance sum'] + proteome_norm_sum_j_df['Protein abundance sum']\n", - "proteome_norm_sum_merge_sort = proteome_norm_sum_merge.sort_values(by='Growth rates')\n", - "print(proteome_norm_sum_merge_sort)\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.scatter(proteome_norm_sum_merge['Growth rates'], proteome_norm_sum_merge['Protein abundance sum'])\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Protein abundance')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean total protein abundance: 0.1401 g/gDW\n" - ] - } - ], - "source": [ - "# mean protein abundances for each condition\n", - "protein_abundance_mean = proteome_norm_sum_merge['Protein abundance sum'].mean()\n", - "print(f'Mean total protein abundance: {round(protein_abundance_mean*0.32, 4)} g/gDW')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Unused Protein Sector" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "#linear relation growth rate and protein abundance\n", - "protein_vs_mu = linregress(x=proteome_norm_sum_merge['Growth rates'], y = proteome_norm_sum_merge['Protein abundance sum']*0.32)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Protein abundance')" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "slope = protein_vs_mu.slope\n", - "intercept = protein_vs_mu.intercept\n", - "\n", - "y= [intercept + slope*mu for mu in proteome_norm_sum_merge['Growth rates']]\n", - "#inverse relation is the fraction of unused proteins to fill up the protein space\n", - "y2 = [intercept - slope*mu for mu in proteome_norm_sum_merge['Growth rates']]\n", - "\n", - "# plot sum of protein abundances against growth rate as scatter plot\n", - "fig, ax = plt.subplots()\n", - "ax.plot(proteome_norm_sum_merge['Growth rates'],y)\n", - "ax.plot(proteome_norm_sum_merge['Growth rates'],y2)\n", - "ax.scatter(proteome_norm_sum_merge['Growth rates'], proteome_norm_sum_merge['Protein abundance sum']*0.32)\n", - "ax.set_xlabel('Growth rate [1/h]')\n", - "ax.set_ylabel('Protein abundance')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max total protein abundance: 0.16995 g/gDW\n", - "Unused enzymes at zero growth: 0.0407 g/gDW\n", - "Change in unused enzymes with increasing growth rate: -0.0214 g/gDW/h\n" - ] - } - ], - "source": [ - "print(f'Max total protein abundance: {round(proteome_norm_sum_merge[\"Protein abundance sum\"][\"LB\"]*0.32, 5)} g/gDW')\n", - "print('Unused enzymes at zero growth: ', round(proteome_norm_sum_merge['Protein abundance sum']['LB']*0.32-intercept,4), ' g/gDW')\n", - "print('Change in unused enzymes with increasing growth rate: ', -round(slope,4), ' g/gDW/h')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Scripts/create_ecolicore_pam.py b/Scripts/create_ecolicore_pam.py deleted file mode 100644 index 7910c91..0000000 --- a/Scripts/create_ecolicore_pam.py +++ /dev/null @@ -1,100 +0,0 @@ -import cobra -import os -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt - -# load PAMpy modules -if os.path.split(os.getcwd())[1] == 'Scripts': - os.chdir('..') - -from src.PAModelpy import PAModel, ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector -from src.PAModelpy.PAMValidator import PAMValidator -from Scripts.pam_generation_uniprot_id import set_up_ecolicore_pam, set_up_ecolicore_mcpam - - -mcpam_core = set_up_ecolicore_mcpam(sensitivity=False) - -#### Fluxes simulations for different glc uptake rates -# load phenotype data from excel file -pt_data = pd.read_excel(os.path.join('Data', 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'), - sheet_name='Yields', index_col=None) - -# extract reaction specific data -rxn_to_pt = {} -rxn_transform = { - 'EX_ac_e': 'EX_ac_e', - 'EX_co2_e': 'EX_co2_e', - 'EX_o2_e': 'EX_o2_e', - 'BIOMASS_Ecoli_core_w_GAM':'BIOMASS_Ec_iML1515_core_75p37M' - # 'BIOMASS_Ecoli_core_w_GAM':'BIOMASS_Ecoli_core_w_GAM' -} -for rxn_id, pt_id in rxn_transform.items(): - rxn_to_pt[rxn_id] = pt_data[['EX_glc__D_e', pt_id]].dropna().rename(columns={pt_id: rxn_id}) - -with mcpam_core: - # change glucose uptake rate - mcpam_core.reactions.EX_glc__D_e.lower_bound = -6.0 - # solve the model - sol_pam = mcpam_core.optimize() - # print(pamodel.summary()) - # with pd.option_context('display.max_rows', None): - # print(sol_pam.fluxes) -mcpam_core.optimize() - -glc_uptake_rates = np.linspace(0.5, 11.5, 20) -fluxes = [] -concentrations = [0] -for glc in glc_uptake_rates: - with mcpam_core: - # change glucose uptake rate - mcpam_core.reactions.EX_glc__D_e.lower_bound = -glc - # disable pyruvate formate lyase (inhibited by oxygen) - mcpam_core.reactions.PFL.upper_bound = 0 - # solve the model - sol_pam = mcpam_core.optimize() - # save data - fluxes.append(sol_pam.fluxes) # flux distributions - concentration = 0 - for enz_var in mcpam_core.enzyme_variables: - concentration += enz_var.concentration - concentrations.append(concentration) - -# plot flux changes with glucose uptake -rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM'] -fig, axs = plt.subplots(2, 2, dpi=90) -for r, ax in zip(rxn_id, axs.flatten()): - # plot data - if r in rxn_to_pt.keys(): - ax.scatter(abs(rxn_to_pt[r]['EX_glc__D_e']), abs(rxn_to_pt[r][r]), - color='firebrick', marker='o', s=30, linewidths=1.3, - facecolors=None, zorder=0, - label='Data') - - # plot simulation - ax.plot(glc_uptake_rates, [abs(f[r]) for f in fluxes], - label='Simulation', linewidth=2.5, - zorder=5) - - # options - ax.set_xlabel('glc uptake rate [mmol/gDW/h]') - ax.set_ylabel('flux [mmol/gDW/h]') - ax.set_title(r) - # set grid - ax.grid(True, axis='both', linestyle='--', linewidth=0.5, alpha=0.6) - ax.set_axisbelow(True) - # show legend - ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1) - -# Add parameter box -param_text = ( - f"mcPAM_core model \n" - f"Total protein: 0.16995 g/g DW \n" - f"Max membrane area = 28.5%" -) -# Position the text box in the upper left corner of each subplot -fig.text(0.6, 2.7, param_text, transform=ax.transAxes, fontsize=8, - verticalalignment='top', bbox=dict(facecolor='white', alpha=0.5, boxstyle="round,pad=0.3")) - -plt.tight_layout() -plt.show() diff --git a/Scripts/create_ecolicore_pam_incl_UE.ipynb b/Scripts/create_ecolicore_pam_incl_UE.ipynb deleted file mode 100644 index 360faa4..0000000 --- a/Scripts/create_ecolicore_pam_incl_UE.ipynb +++ /dev/null @@ -1,959 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import cobra\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load PAMpy modules" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/samiralvdb/Documents/3_Projects/7_MCA_analysis/PAModelpy\n", - "Loading PAModelpy modules version 0.0.3.11\n" - ] - } - ], - "source": [ - "# load PAMpy modules\n", - "if os.path.split(os.getcwd())[1] == 'Scripts':\n", - " os.chdir('..')\n", - " \n", - "from src.PAModelpy import PAModel, ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector\n", - "from src.PAModelpy.PAMValidator import PAMValidator" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load E. coli core model" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Set parameter Username\n", - "Academic license - for non-commercial use only - expires 2025-03-06\n", - "Objective\n", - "=========\n", - "1.0 BIOMASS_Ecoli_core_w_GAM = 0.8739215069684301\n", - "\n", - "Uptake\n", - "------\n", - "Metabolite Reaction Flux C-Number C-Flux\n", - " glc__D_e EX_glc__D_e 10 6 100.00%\n", - " nh4_e EX_nh4_e 4.765 0 0.00%\n", - " o2_e EX_o2_e 21.8 0 0.00%\n", - " pi_e EX_pi_e 3.215 0 0.00%\n", - "\n", - "Secretion\n", - "---------\n", - "Metabolite Reaction Flux C-Number C-Flux\n", - " co2_e EX_co2_e -22.81 1 100.00%\n", - " h2o_e EX_h2o_e -29.18 0 0.00%\n", - " h_e EX_h_e -17.53 0 0.00%\n", - "\n" - ] - } - ], - "source": [ - "# load the model\n", - "model = cobra.io.load_json_model(os.path.join('Models','e_coli_core.json'))\n", - "# test the model\n", - "sol_t = model.optimize()\n", - "print(model.summary())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Build E. coli core PAM\n", - "## Create Protein Sectors\n", - "### Active Enzymes" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rxnNamerxnEquatEC_nmbrmolMasskcat
rxnID
ALATA_D2D-alanine transaminasepydx5p_c + ala__D_c -> pyam5p_c + pyr_c2.1.2.1, 4.1.2.4840904.48506.862500e+00
SHCHD2Sirohydrochlorin dehydrogenase (NAD)nad_c + dscl_c -> h_c + scl_c + nadh_c1.3.1.7649950.00004.500000e-01
CPPPGOCoproporphyrinogen oxidase (O2 required)2 h_c + o2_c + cpppg3_c -> 2 h2o_c + pppg9_c ...1.3.3.334321.49003.000000e-03
GTHOr_fGlutathione oxidoreductaseh_c + nadph_c + gthox_c -> 2 gthrd_c + nadp_c1.8.1.748771.05007.333000e+02
DHORD5Dihydroorotic acid (menaquinone-8)dhor__S_c + mqn8_c -> orot_c + mql8_c1.3.5.236773.46008.200000e+01
..................
AI2t_bAi2 transport, outer membranemththf_p -> mththf_eNaN39959.48252.200000e+01
RHMND_bL-rhamnonate dehydrataselkdr_c + h2o_c -> rhmn_c4.2.1.9044225.10002.000000e-02
ASPtpp_bL-aspartate uptake via facillitated diffusionasp__L_c -> asp__L_pNaN59427.58001.000000e+08
FUMt1_bFumarate transport via diffusion in (periplasm)fum_c -> fum_pNaN59427.58001.000000e+08
SUCCt1_bSuccinate transport via diffusion in (periplasm)succ_c -> succ_pNaN59427.58001.000000e+08
\n", - "

2843 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " rxnName \\\n", - "rxnID \n", - "ALATA_D2 D-alanine transaminase \n", - "SHCHD2 Sirohydrochlorin dehydrogenase (NAD) \n", - "CPPPGO Coproporphyrinogen oxidase (O2 required) \n", - "GTHOr_f Glutathione oxidoreductase \n", - "DHORD5 Dihydroorotic acid (menaquinone-8) \n", - "... ... \n", - "AI2t_b Ai2 transport, outer membrane \n", - "RHMND_b L-rhamnonate dehydratase \n", - "ASPtpp_b L-aspartate uptake via facillitated diffusion \n", - "FUMt1_b Fumarate transport via diffusion in (periplasm) \n", - "SUCCt1_b Succinate transport via diffusion in (periplasm) \n", - "\n", - " rxnEquat \\\n", - "rxnID \n", - "ALATA_D2 pydx5p_c + ala__D_c -> pyam5p_c + pyr_c \n", - "SHCHD2 nad_c + dscl_c -> h_c + scl_c + nadh_c \n", - "CPPPGO 2 h_c + o2_c + cpppg3_c -> 2 h2o_c + pppg9_c ... \n", - "GTHOr_f h_c + nadph_c + gthox_c -> 2 gthrd_c + nadp_c \n", - "DHORD5 dhor__S_c + mqn8_c -> orot_c + mql8_c \n", - "... ... \n", - "AI2t_b mththf_p -> mththf_e \n", - "RHMND_b lkdr_c + h2o_c -> rhmn_c \n", - "ASPtpp_b asp__L_c -> asp__L_p \n", - "FUMt1_b fum_c -> fum_p \n", - "SUCCt1_b succ_c -> succ_p \n", - "\n", - " EC_nmbr molMass kcat \n", - "rxnID \n", - "ALATA_D2 2.1.2.1, 4.1.2.48 40904.4850 6.862500e+00 \n", - "SHCHD2 1.3.1.76 49950.0000 4.500000e-01 \n", - "CPPPGO 1.3.3.3 34321.4900 3.000000e-03 \n", - "GTHOr_f 1.8.1.7 48771.0500 7.333000e+02 \n", - "DHORD5 1.3.5.2 36773.4600 8.200000e+01 \n", - "... ... ... ... \n", - "AI2t_b NaN 39959.4825 2.200000e+01 \n", - "RHMND_b 4.2.1.90 44225.1000 2.000000e-02 \n", - "ASPtpp_b NaN 59427.5800 1.000000e+08 \n", - "FUMt1_b NaN 59427.5800 1.000000e+08 \n", - "SUCCt1_b NaN 59427.5800 1.000000e+08 \n", - "\n", - "[2843 rows x 5 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# load enzyme database from genome-scale E. coli PAM\n", - "enzyme_db = pd.read_excel(os.path.join('Data','proteinAllocationModel_iML1515_EnzymaticData_py.xls'),\n", - " sheet_name='ActiveEnzymes', index_col=0)\n", - "\n", - "# correct reaction IDS\n", - "for idx in enzyme_db.index.to_list():\n", - " # transprt reactions<\n", - " \n", - " if 'pp' in idx:\n", - " idx_new = idx.replace('pp', '')\n", - " if idx_new not in enzyme_db.index:\n", - " enzyme_db.rename(index={idx: idx_new}, inplace=True)\n", - " if 'ex' in idx:\n", - " idx_new = idx.replace('ex', '')\n", - " if idx_new not in enzyme_db.index:\n", - " enzyme_db.rename(index={idx: idx_new}, inplace=True) \n", - "\n", - "enzyme_db" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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rxnNamerxnEquatEC_nmbrmolMasskcat
rxnID
GLUt_fL-glutamate transport via diffusion (extracell...glu__L_e -> glu__L_pE039959.4825100000000.0
GLYt_fGlycine transport via diffusion (extracellular...gly_e -> gly_pE139959.4825100000000.0
GLYALDt_fGlyceraldehyde transport via diffusion (extrac...glyald_e -> glyald_pE239959.4825100000000.0
GLYBt_fGlycine betaine transport via diffusion (extra...glyb_e -> glyb_pE339959.4825100000000.0
GLYCt_fGlycerol transport via diffusion (extracellula...glyc_e -> glyc_pE439959.4825100000000.0
..................
METGLCURt_b1-O-methyl-Beta-D-glucuronate via diffusion (e...metglcur_p -> metglcur_eE95339959.4825100000000.0
AI2t_bAi2 transport, outer membranemththf_p -> mththf_eE95439959.482522.0
ASPtpp_bL-aspartate uptake via facillitated diffusionasp__L_c -> asp__L_pE95559427.5800100000000.0
FUMt1_bFumarate transport via diffusion in (periplasm)fum_c -> fum_pE95659427.5800100000000.0
SUCCt1_bSuccinate transport via diffusion in (periplasm)succ_c -> succ_pE95759427.5800100000000.0
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958 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " rxnName \\\n", - "rxnID \n", - "GLUt_f L-glutamate transport via diffusion (extracell... \n", - "GLYt_f Glycine transport via diffusion (extracellular... \n", - "GLYALDt_f Glyceraldehyde transport via diffusion (extrac... \n", - "GLYBt_f Glycine betaine transport via diffusion (extra... \n", - "GLYCt_f Glycerol transport via diffusion (extracellula... \n", - "... ... \n", - "METGLCURt_b 1-O-methyl-Beta-D-glucuronate via diffusion (e... \n", - "AI2t_b Ai2 transport, outer membrane \n", - "ASPtpp_b L-aspartate uptake via facillitated diffusion \n", - "FUMt1_b Fumarate transport via diffusion in (periplasm) \n", - "SUCCt1_b Succinate transport via diffusion in (periplasm) \n", - "\n", - " rxnEquat EC_nmbr molMass kcat \n", - "rxnID \n", - "GLUt_f glu__L_e -> glu__L_p E0 39959.4825 100000000.0 \n", - "GLYt_f gly_e -> gly_p E1 39959.4825 100000000.0 \n", - "GLYALDt_f glyald_e -> glyald_p E2 39959.4825 100000000.0 \n", - "GLYBt_f glyb_e -> glyb_p E3 39959.4825 100000000.0 \n", - "GLYCt_f glyc_e -> glyc_p E4 39959.4825 100000000.0 \n", - "... ... ... ... ... \n", - "METGLCURt_b metglcur_p -> metglcur_e E953 39959.4825 100000000.0 \n", - "AI2t_b mththf_p -> mththf_e E954 39959.4825 22.0 \n", - "ASPtpp_b asp__L_c -> asp__L_p E955 59427.5800 100000000.0 \n", - "FUMt1_b fum_c -> fum_p E956 59427.5800 100000000.0 \n", - "SUCCt1_b succ_c -> succ_p E957 59427.5800 100000000.0 \n", - "\n", - "[958 rows x 5 columns]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "#replace NaN enzyme ids with a dummy enzyme identifier\n", - "#select the NaN values \n", - "nan_values = enzyme_db['EC_nmbr'].isnull()\n", - "#make a list with unique ids\n", - "nan_ids = [f'E{i}' for i in range(nan_values.sum())]\n", - "#replace nan values by unique id\n", - "enzyme_db.loc[nan_values, 'EC_nmbr'] = nan_ids\n", - "\n", - "#check if it worked:\n", - "enzyme_db[nan_values]" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "No enzyme information found for reaction: FRD7\n" - ] - } - ], - "source": [ - "# create enzyme objects for each gene-associated reaction\n", - "kcats = {}\n", - "rxn2ec = {}\n", - "molmass = {}\n", - "for rxn in model.reactions:\n", - " if rxn.genes:\n", - " # correct transport reactions\n", - " if 't' in rxn.id:\n", - " rxn.id = rxn.id\n", - " # are enzyme information in the PAM database?\n", - " rev = 0 # denotes reversibility\n", - " if rxn.lower_bound >= 0:\n", - " # irreversible reaction (forward direction)\n", - " rev = 0\n", - " rxn_id = rxn.id # save reaction ID for retrieveing molar masses/enzyme information later\n", - " if rxn.id in enzyme_db.index:\n", - " kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat']}\n", - " elif rxn.upper_bound <= 0:\n", - " # irreversible reaction (reverse direction)\n", - " rev = 1\n", - " rxn_id = rxn.id + '_b'\n", - " if rxn_id in enzyme_db.index:\n", - " kcats[rxn.id] = {'b': enzyme_db.loc[rxn_id, 'kcat']}\n", - " else:\n", - " rev = 2\n", - " # reversible reaction\n", - " rxn_id_f = rxn.id + '_f'\n", - " rxn_id_b = rxn.id + '_b'\n", - " if rxn_id_f in enzyme_db.index and rxn_id_b in enzyme_db.index:\n", - " rxn_id = rxn_id_f # save reaction ID for retrieveing molar masses/enzyme information later\n", - " kcats[rxn.id] = {'f': enzyme_db.loc[rxn_id_f, 'kcat'],\n", - " 'b': enzyme_db.loc[rxn_id_b, 'kcat']}\n", - "\n", - " else:\n", - " # try if only forward reaction is in database\n", - " rxn_id = rxn.id # save reaction ID for retrieveing molar masses/enzyme information later\n", - " kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat'],\n", - " 'b': enzyme_db.loc[rxn.id, 'kcat']/2} # deduce backwards kcat from forward value\n", - "\n", - " # where enzyme information found?\n", - " if rxn.id in kcats.keys():\n", - " # save molmass\n", - " molmass[rxn.id] = enzyme_db.loc[rxn_id, 'molMass']\n", - " #save enzyme information\n", - " # is enzyme information NaN?\n", - " if pd.isna(enzyme_db.loc[rxn_id, 'EC_nmbr']):\n", - " rxn2ec[rxn.id] = ''\n", - " else:\n", - " rxn2ec[rxn.id] = enzyme_db.loc[rxn_id, 'EC_nmbr']\n", - " \n", - " \n", - " else:\n", - " # no enzyme information found\n", - " print('No enzyme information found for reaction: ' + rxn.id)\n", - " # Create generic Enzyme with mean molar masses and kcat\n", - " if rev == 0:\n", - " kcats[rxn.id] = {'f': 22}\n", - " elif rev == 1: \n", - " kcats[rxn.id] = {'b': 22}\n", - " else:\n", - " kcats[rxn.id] = {'f': 22, 'b': 22}\n", - " \n", - " molmass[rxn.id] = 3.947778784340140e04\n", - "\n", - "rxn2protein = {}\n", - "for rxn, ec in rxn2ec.items():\n", - " ec_dict = {**kcats[rxn], **{'molmass': molmass[rxn], 'protein_reaction_association':[[ec]]}}\n", - " #add enzyme to enzymes related to reaction if these are already stored\n", - " if rxn in rxn2protein.keys():\n", - " rxn2protein[rxn] = {**rxn2protein[rxn], **{ec:ec_dict}}\n", - " #if not create new reaction entry\n", - " else:\n", - " rxn2protein[rxn] = {ec:ec_dict}\n", - "\n", - "# create active enzymes sector\n", - "active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Unused Protein Sector" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "id_list_ups = ['BIOMASS_Ecoli_core_w_GAM']\n", - "ups_0 = [0.0407] # g/gDW\n", - "ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate\n", - "molmass_ups = [405903.94] # g/mol\n", - "\n", - "unused_enzyme_sector = UnusedEnzymeSector(\n", - " id_list=id_list_ups,\n", - " ups_0=ups_0,\n", - " ups_mu=ups_mu,\n", - " mol_mass = molmass_ups,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Translational Protein Sector" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# translational protein sector parameter (substrate dependent)\n", - "id_list_tps = ['EX_glc__D_e']\n", - "tps_0 = [0.04992] # g/gDW\n", - "tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable\n", - "molmass_tps = [405903.94] # g/mol\n", - "\n", - "# translational protein sector\n", - "translational_enzyme_sector = TransEnzymeSector(\n", - " id_list=id_list_tps,\n", - " tps_0=tps_0,\n", - " tps_mu=tps_mu,\n", - " mol_mass = molmass_tps,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Total Protein Constraint" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [], - "source": [ - "# total protein constraint (cf. analyze_proteome.ipynb)\n", - "p_tot = 0.16995 # g/gDW -> Standard 0.14 g/gDW " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build PAM\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Read LP format model from file /tmp/tmpivldy03z.lp\n", - "Reading time = 0.00 seconds\n", - ": 73 rows, 191 columns, 721 nonzeros\n", - "Setting up the proteome allocation model e_coli_core\n", - "\n", - "Add total condition-dependent protein constraint\n", - "\tTotal protein concentration: 0.16995 g/gDW\n", - "\n", - "Change total condition-dependent protein constraint from 0.16995 to 0.16995\n", - "Add active protein sector\n", - "\n", - "Add the following protein sector: TranslationalProteinSector\n", - "\n", - "Add the following protein sector: UnusedEnzymeSector\n", - "\n", - "Done with setting up the proteome allocation model e_coli_core\n", - "\n" - ] - } - ], - "source": [ - "# set up PAM\n", - "pa_model = PAModel(\n", - " id_or_model=model,\n", - " p_tot=p_tot,\n", - " sensitivity = False,\n", - " translational_sector=translational_enzyme_sector,\n", - " unused_sector = unused_enzyme_sector,\n", - " active_sector=active_enzyme_sector,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Test PAM model\n", - "## Load phenotype data" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# load phenotype data from excel file\n", - "pt_data = pd.read_excel(os.path.join('Data', 'Ecoli_phenotypes','Ecoli_phenotypes_py_rev.xls'),\n", - " sheet_name='Yields', index_col=None)\n", - "pt_data\n", - "\n", - "# extract reaction specific data \n", - "rxn_to_pt = {}\n", - "rxn_transform = {\n", - " 'EX_ac_e': 'EX_ac_e',\n", - " 'EX_co2_e': 'EX_co2_e',\n", - " 'EX_o2_e': 'EX_o2_e',\n", - " 'BIOMASS_Ecoli_core_w_GAM':'BIOMASS_Ec_iML1515_core_75p37M'\n", - "}\n", - "for rxn_id, pt_id in rxn_transform.items():\n", - " rxn_to_pt[rxn_id] = pt_data[['EX_glc__D_e', pt_id]].dropna().rename(columns={pt_id: rxn_id})\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Simulate PAM" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "Optimal solution with objective value 0.574
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fluxesreduced_costs
PFK7.7871237.372575e-18
PFL10.3239681.626303e-19
PGI4.9577332.818926e-18
PGK-16.8315221.219727e-19
PGL4.9245113.794708e-19
.........
CE_NADH16_1.6.5.1120.9457641.364972e-03
CE_NADTRHD_1.6.1.10.000000-5.955446e-04
CE_NH4t_E1343.1321857.749636e-10
CE_O2t_E8410.4728827.749636e-10
CE_PDH_1.2.4.12.0767836.316039e-03
\n", - "

163 rows × 2 columns

\n", - "
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "with pa_model:\n", - " # change glucose uptake rate\n", - " pa_model.reactions.EX_glc__D_e.lower_bound = -6.0\n", - " # solve the model\n", - " sol_pam = pa_model.optimize()\n", - " # print(pa_model.summary())\n", - " # with pd.option_context('display.max_rows', None):\n", - " # print(sol_pam.fluxes)\n", - "pa_model.optimize()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Scan glucose uptake rates" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "glc_uptake_rates = np.linspace(0.5, 10, 20)\n", - "fluxes = []\n", - "concentrations = [0]\n", - "for glc in glc_uptake_rates:\n", - " with pa_model:\n", - " # change glucose uptake rate\n", - " pa_model.reactions.EX_glc__D_e.lower_bound = -glc\n", - " # disable pyruvate formate lyase (inhibited by oxygen)\n", - " pa_model.reactions.PFL.upper_bound = 0\n", - " # solve the model\n", - " sol_pam = pa_model.optimize()\n", - " # save data\n", - " fluxes.append(sol_pam.fluxes) # flux distributions\n", - " concentration = 0\n", - " for enz_var in pa_model.enzyme_variables:\n", - " concentration += enz_var.concentration\n", - " concentrations.append(concentration)\n", - "\n", - "# plot flux changes with glucose uptake\n", - "rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'BIOMASS_Ecoli_core_w_GAM']\n", - "fig, axs = plt.subplots(2,2, dpi=100)\n", - "for r, ax in zip(rxn_id, axs.flatten()):\n", - " # plot data\n", - " if r in rxn_to_pt.keys():\n", - " ax.scatter(abs(rxn_to_pt[r]['EX_glc__D_e']), abs(rxn_to_pt[r][r]),\n", - " color='firebrick', marker='o', s=30, linewidths=1.3,\n", - " facecolors=None, zorder=0,\n", - " label='Data')\n", - " \n", - " # plot simulation\n", - " ax.plot(glc_uptake_rates, [abs(f[r]) for f in fluxes],\n", - " label='Simulation', linewidth=2.5,\n", - " zorder=5)\n", - " \n", - " \n", - " # options\n", - " ax.set_xlabel('glc uptake rate [mmol/gDW/h]')\n", - " ax.set_ylabel('flux [mmol/gDW/h]')\n", - " ax.set_title(r)\n", - " # set grid\n", - " ax.grid(True, axis='both', linestyle='--', linewidth=0.5, alpha=0.6 )\n", - " ax.set_axisbelow(True)\n", - " # show legend\n", - " ax.legend(fontsize=8, edgecolor='white', facecolor='white', framealpha=1)\n", - "\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "PAMvenv", - "language": "python", - "name": "pamvenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/Scripts/mcpam_generation_uniprot_id.py b/Scripts/mcpam_generation_uniprot_id.py index b6b94f2..8438d03 100644 --- a/Scripts/mcpam_generation_uniprot_id.py +++ b/Scripts/mcpam_generation_uniprot_id.py @@ -209,6 +209,113 @@ def set_up_ecolicore_mcpam(total_protein: Union[bool, float] = True, return pamodel +def set_up_ecolicore_mcpam_new_surface_parameter(total_protein: Union[bool, float] = True, + active_enzymes: bool = True, + translational_enzymes: bool = True, + unused_enzymes: bool = True, + membrane_sector: bool = True, + max_area:float = 0.27, + sensitivity = True): + + config = Config() + config.reset() + + pam_info_file = os.path.join('Data', 'mcPAM_iML1515_EnzymaticData.xlsx') + + # some other constants + BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM' + TOTAL_PROTEIN_CONCENTRATION = 0.16995 # [g_prot/g_cdw] + + config.BIOMASS_REACTION = BIOMASS_REACTION + + # load the genome-scale information + model = cobra.io.load_json_model(os.path.join('Models', 'e_coli_core.json')) + + # load example data for the E.coli iML1515 model + if active_enzymes: + # load active enzyme sector information + enzyme_db = pd.read_excel(pam_info_file, sheet_name='mcPAM_data_core') + + for idx in enzyme_db.rxnID: + # transport reactions + if 'pp' in idx: + idx_new = idx.replace('pp', '') + if idx_new not in enzyme_db.rxnID: + enzyme_db = enzyme_db.replace(idx, idx_new) + if 'ex' in idx: + idx_new = idx.replace('ex', '') + if idx_new not in enzyme_db.rxnID: + enzyme_db = enzyme_db.replace(idx, idx_new) + + # create enzyme objects for each gene-associated reaction + rxn2protein, protein2gene = parse_reaction2protein(enzyme_db, model) + + active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene, + configuration=config) + + else: + active_enzyme_sector = None + + if translational_enzymes: + # translational protein sector parameter (substrate dependent) + id_list_tps = ['EX_glc__D_e'] + tps_0 = [0.04992] # g/gDW + tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable + molmass_tps = [405903.94] # g/mol + + # translational protein sector + translation_enzyme_sector = TransEnzymeSector( + id_list=id_list_tps, + tps_0=tps_0, + tps_mu=tps_mu, + mol_mass=molmass_tps, + ) + else: + translation_enzyme_sector = None + + if unused_enzymes: + id_list_ups = [BIOMASS_REACTION] + ups_0 = [0.0407] # g/gDW + ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate + molmass_ups = [405903.94] # g/mol + + unused_enzyme_sector = UnusedEnzymeSector( + id_list=id_list_ups, + ups_0=ups_0, + ups_mu=ups_mu, + mol_mass=molmass_ups, + ) + else: + unused_enzyme_sector = None + + if membrane_sector: + membrane_info = pd.read_excel(pam_info_file, sheet_name='Membrane') + active_enzyme_info = pd.read_excel(pam_info_file, sheet_name='mcPAM_data_core') + + area_avail_0 = 0.9812 + area_avail_mu = 8.4243 + alpha_numbers_dict = active_enzyme_info.set_index(keys='uniprotID').loc[:, 'alpha_numbers'].to_dict() + enzyme_location = active_enzyme_info.set_index(keys='uniprotID').loc[:, 'Location'].to_dict() + + membrane_sector = MembraneSector(area_avail_0=area_avail_0, + area_avail_mu=area_avail_mu, + alpha_numbers_dict=alpha_numbers_dict, + enzyme_location=enzyme_location, + max_area=max_area) + + else: + membrane_sector = None + + if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION + + pamodel = PAModel(id_or_model=model, p_tot=total_protein, + active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, + unused_sector=unused_enzyme_sector, sensitivity=sensitivity, configuration = config, + membrane_sector=membrane_sector + ) + + return pamodel + def set_up_ecoli_pam(total_protein: Union[bool, float] = True, active_enzymes: bool = True, translational_enzymes: bool = True, unused_enzymes: bool = True, sensitivity = True): diff --git a/Scripts/mcpam_simulations_analysis.py b/Scripts/mcpam_simulations_analysis.py index b4b1525..bcd484d 100644 --- a/Scripts/mcpam_simulations_analysis.py +++ b/Scripts/mcpam_simulations_analysis.py @@ -15,6 +15,7 @@ from src.PAModelpy.configuration import Config from Scripts.mcpam_generation_uniprot_id import (parse_reaction2protein, set_up_ecolicore_pam, set_up_ecolicore_mcpam, + set_up_ecolicore_mcpam_new_surface_parameter, set_up_ecoli_pam, set_up_ecoli_mcpam) def compare_mu_for_different_sensitivities_ecolicore_pam(): @@ -213,7 +214,131 @@ def run_pam_mcpam_core_with_optimized_kcats(sensitivity:bool=True, return pam, mcpam -def run_simulations_pam_mcpam(models, print_area:bool=False, type:str="full scale"): +def run_simulation_pam_mcpam(models, type:str="full scale"): + fontsize = 25 + labelsize = 15 + + # load phenotype data from excel file + pt_data = pd.read_excel(os.path.join('Data', 'Ecoli_phenotypes', 'Ecoli_phenotypes_py_rev.xls'), + sheet_name='Yields', index_col=None) + + # Define the biomass name based on the used model + if type == "full scale": + biomass_name = 'BIOMASS_Ec_iML1515_core_75p37M' + else: + biomass_name = 'BIOMASS_Ecoli_core_w_GAM' + + # extract reaction specific data + rxn_to_pt = {} + rxn_transform = { + 'EX_ac_e': 'EX_ac_e', + 'EX_co2_e': 'EX_co2_e', + 'EX_o2_e': 'EX_o2_e', + biomass_name: 'BIOMASS_Ec_iML1515_core_75p37M' + } + for rxn_id, pt_id in rxn_transform.items(): + rxn_to_pt[rxn_id] = pt_data[['EX_glc__D_e', pt_id]].dropna().rename(columns={pt_id: rxn_id}) + + glc_uptake_rates = np.linspace(0.5, 14, 25) + + # Initializing fluxes and concentrations for pam and mcpam + fluxes_dict = {} + concentrations_dict = {} + + for model, config in zip(models, ["PAM", "mcPAM"]): + + # disable pyruvate formate lyase (inhibited by oxygen) + model.change_reaction_bounds(rxn_id='PFL', upper_bound=0) + + fluxes_list = [] + concentrations_list = [0] + + if config == "PAM": # simulating pam + for glc in glc_uptake_rates: + with model: + # change glucose uptake rate + model.reactions.EX_glc__D_e.lower_bound = -glc + # disable pyruvate formate lyase (inhibited by oxygen) + model.reactions.PFL.upper_bound = 0 + # solve the model + sol_pam = model.optimize() + # save data + fluxes_list.append(sol_pam.fluxes) # flux distributions + concentration = 0 + for enz_var in model.enzyme_variables: + concentration += enz_var.concentration + concentrations_list.append(concentration) + + fluxes_dict[config] = fluxes_list + concentrations_dict[config] = concentrations_list + + else: # simulating mcpam + for glc in glc_uptake_rates: + with model: + # change glucose uptake rate + model.reactions.EX_glc__D_e.lower_bound = -glc + # disable pyruvate formate lyase (inhibited by oxygen) + model.reactions.PFL.upper_bound = 0 + # solve the model + sol_pam = model.optimize() + # save data + fluxes_list.append(sol_pam.fluxes) # flux distributions + concentration = 0 + for enz_var in model.enzyme_variables: + concentration += enz_var.concentration + concentrations_list.append(concentration) + + fluxes_dict[config] = fluxes_list + concentrations_dict[config] = concentrations_list + + + # dictionary of colorblind friendly color palette + sns.set_palette(("colorblind")) + + # plot flux changes with glucose uptake + rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', biomass_name] + ax_title = {'EX_ac_e': 'Acetate Secretion', + 'EX_co2_e': 'CO2 Secretion', + 'EX_o2_e': 'O2 uptake', + biomass_name: 'Biomass Production'} + # rxn_id = ['EX_ac_e', 'EX_co2_e', 'EX_o2_e', 'BIOMASS_Ec_iML1515_core_75p37M'] + fig, axs = plt.subplots(2, 2, dpi=90) + for r, ax in zip(rxn_id, axs.flatten()): + # plot data + if r in rxn_to_pt.keys(): + ax.scatter(abs(rxn_to_pt[r]['EX_glc__D_e']), abs(rxn_to_pt[r][r]), + color='firebrick', marker='o', s=30, linewidths=1.3, + facecolors=None, zorder=0, + label='Data') + + # plot simulation for pam core + for model in fluxes_dict.keys(): + if model == 'PAM': + ax.plot(glc_uptake_rates, [abs(f[r]) for f in fluxes_dict[model]], + label=f'{model}', linewidth=2.5, linestyle='--', + zorder=6) + else: + ax.plot(glc_uptake_rates, [abs(f[r]) for f in fluxes_dict[model]], + label=f'{model}', linewidth=2.5, + zorder=5) + + # options + ax.tick_params(axis='both', which='major', labelsize=labelsize) + ax.set_xlabel('glc uptake rate [mmol/gDW/h]', fontsize=fontsize) + ax.set_ylabel('flux [mmol/gDW/h]', fontsize=fontsize * 0.8) + ax.set_title(ax_title[r], fontsize=fontsize * 0.8, fontweight="bold") + # set grid + ax.grid(True, axis='both', linestyle='--', linewidth=0.5, alpha=0.6) + ax.set_axisbelow(True) + handles, labels = ax.get_legend_handles_labels() + fig.legend(handles, labels, loc='center', bbox_to_anchor=(0.5, 0.95), ncol=6, fontsize=fontsize * 0.65) + + # show legend + fig.subplots_adjust(top=0.85, wspace=0.5, hspace=0.5) + + plt.show() + +def run_simulations_pam_mcpam_w_different_areas(models, print_area:bool=False, type:str="full scale"): fontsize = 25 labelsize = 15 diff --git a/Scripts/toy_model_TS_generation.py b/Scripts/mcpam_toy_generation.py similarity index 91% rename from Scripts/toy_model_TS_generation.py rename to Scripts/mcpam_toy_generation.py index e6aea1b..a1179c4 100644 --- a/Scripts/toy_model_TS_generation.py +++ b/Scripts/mcpam_toy_generation.py @@ -15,7 +15,7 @@ from src.PAModelpy.configuration import Config -def build_toy_model(sensitivity:bool=True): +def build_toy_model(sensitivity:bool=True, membrane_sector: bool=False): config = Config() config.reset() config.BIOMASS_REACTION = 'R11' @@ -28,8 +28,9 @@ def build_toy_model(sensitivity:bool=True): # Building Active Enzyme Sector kcat_fwd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 1, 10e-2, 10e-2] kcat_rev = [kcat for kcat in kcat_fwd] - gpr_string = [['gene1'], ['gene2'],['gene3'],['gene4'],['gene5'],['gene6'],['gene7'],['gene8']] - pra_string = ['E2', 'E3', 'E4', 'E5', 'E6', 'E7', 'E8', 'E9'] + enz_complex_id = ['E1_E2_E3', 'E4', 'E5_E6', 'E7_E8_E9_E10', 'E11', 'E12', 'E13', 'E14'] + gpr_string = [['g1', 'g2', 'g3'], ['g4'],['g5', 'g6'],['g7', 'g8', 'g9', 'g10'],['g11', 'g12'],['g13'],['g14'],['g15']] + pra_string = ['g1 and g2 and g3', 'g4', 'g5 and g6', 'g7 and g8 and g9 and g10', 'g11 or g12', 'g13', 'g14', 'g15'] rxn2protein = {} protein2gene = {} @@ -37,7 +38,7 @@ def build_toy_model(sensitivity:bool=True): rxn_id = f'R{i+2}' # 1e-6 to correct for the unit transformation in the model (meant to make the calculations preciser for different unit dimensions) #dummy molmass - rxn2protein = {**rxn2protein, **{rxn_id: {f'E{i+2}':{'f': kcat_fwd[i]/(3600*1e-6), 'b': kcat_rev[i]/(3600*1e-6), + rxn2protein = {**rxn2protein, **{rxn_id: {f'{enz_complex_id[i]}':{'f': kcat_fwd[i]/(3600*1e-6), 'b': kcat_rev[i]/(3600*1e-6), 'molmass': 1e6, 'genes': gpr_string[i], 'protein_reaction_association': pra_string[i]}}}} protein2gene = {**protein2gene, **{f'E{i+2}': gpr_string[i]}} @@ -51,27 +52,32 @@ def build_toy_model(sensitivity:bool=True): unused_enzyme = UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=config) # Building Membrane Sector - alpha_numbers_dict = {"E1": 12, - "E2": 8, - "E3": 1, - "E4": 12, - "E5": 40, - "E7": 15, - "E8": 30} - - enzyme_location = {"E1": "Unknown", - "E2": "Cell membrane", - "E3": "Cytoplasm", - "E4": "Cytoplasm", - "E5": "Cell membrane", - "E7": "Cytoplasm", - "E8": "Cytoplasm"} - - membrane_sector = MembraneSector(area_avail_mu=0.1, area_avail_0=0.005, alpha_numbers_dict=alpha_numbers_dict, - enzyme_location=enzyme_location, max_area=0.132815) + alpha_numbers_dict = {"E1_E2_E3": 20, + "E4": 8, + "E5_E6": 1, + "E7_E8_E9_E10": 48, + "E11": 12, + "E12": 12, + "E13": 12, + "E14": 12} + + enzyme_location = {"E1_E2_E3": "Cell membrane", + "E4": "Cytosol", + "E5_E6": "Cytosol", + "E7_E8_E9_E10": "Cell membrane", + "E11": "Cytosol", + "E12": "Cytosol", + "E13": "Cytosol", + "E14": "Cell membrane"} + #mu = 0.1, 0=0.005 + if membrane_sector: + membrane_sector = MembraneSector(area_avail_mu=-0.1042, area_avail_0=0.1479, alpha_numbers_dict=alpha_numbers_dict, + enzyme_location=enzyme_location, max_area=1) + else: + membrane_sector = None # Building the toy_pam - model = load_json_model('Models/toy_model_TS_v1.json') + model = load_json_model('Models/toy_model.json') pamodel = PAModel(model, name='toy model MCA with enzyme constraints', sensitivity=sensitivity, active_sector=active_enzyme, diff --git a/Scripts/merge_mcpam_w_core_data_new_structure.py b/Scripts/merge_mcpam_w_core_data_new_structure.py new file mode 100644 index 0000000..8d487bc --- /dev/null +++ b/Scripts/merge_mcpam_w_core_data_new_structure.py @@ -0,0 +1,38 @@ +import pandas as pd +import os +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +data1_path = os.path.join('Data/mcPAM_iML1515_EnzymaticData.xlsx') +data2_path = os.path.join('Data/proteinAllocationModel_mc-core_EnzymaticData_241209_multi.xlsx') +data1 = pd.read_excel(data1_path, sheet_name='mcPAM_data_core') +data2 = pd.read_excel(data2_path, sheet_name="ActiveEnzymes") + +data1 = data1.drop_duplicates(subset=["rxnID"]) +data1 = data1[['rxnID', 'kcat_f', 'kcat_b']] + +# Unpivot the DataFrame +data1 = data1.melt( + id_vars=['rxnID'], # Columns to keep as is + value_vars=['kcat_f', 'kcat_b'], # Columns to unpivot + var_name='direction', # Name of the new column for direction + value_name='kcat' # Name of the new column for kcat values +) + +data1 = data1.dropna(subset=['kcat']).reset_index(drop=True) + +# Map directions to 'f' and 'b' +data1['direction'] = data1['direction'].map({'kcat_f': 'f', 'kcat_b': 'b'}) + +# Drop rows where kcat is NaN +data1 = data1.dropna(subset=['kcat']).reset_index(drop=True) + +merged_data = pd.merge(data2, data1, left_on=['rxn_id', 'direction'], right_on=['rxnID', 'direction'], how='right') +merged_data = merged_data.drop(columns=['rxnID', 'kcat_values']).rename(columns={'kcat': 'kcat_values'}) + +with pd.ExcelWriter(data2_path, engine='openpyxl', mode='a') as writer: + # Write the new DataFrame to a new sheet + merged_data.to_excel(writer, sheet_name='mcPAM_data_core', index=True) + + diff --git a/Scripts/pam_generation.py b/Scripts/pam_generation.py deleted file mode 100644 index 2951701..0000000 --- a/Scripts/pam_generation.py +++ /dev/null @@ -1,335 +0,0 @@ -import cobra -import pandas as pd -import os -from typing import Union - -# load PAMpy modules -from src.PAModelpy.PAModel import PAModel -from src.PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector -from src.PAModelpy.configuration import Config - -from src.PAModelpy import EnzymeVariable - -from Scripts.toy_ec_pam import build_toy_gem, build_active_enzyme_sector, build_translational_protein_sector, build_unused_protein_sector - -'Function library for making Protein Allocation Models as described in the publication' - - -def set_up_toy_pam(sensitivity =True): - config = Config() - #setting the configuration for the toy model - config.BIOMASS_REACTION = 'R7' - config.GLUCOSE_EXCHANGE_RXNID = 'R1' - config.CO2_EXHANGE_RXNID = 'R8' - config.ACETATE_EXCRETION_RXNID = 'R9' - - Etot = 0.6*1e-3 - model = build_toy_gem() - active_enzyme = build_active_enzyme_sector(config) - unused_enzyme = build_unused_protein_sector(config) - translation_enzyme = build_translational_protein_sector(config) - pamodel = PAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme, - translational_sector=translation_enzyme, - unused_sector=unused_enzyme, p_tot=Etot, sensitivity=sensitivity) - pamodel.objective = 'R7' - config.reset() - return pamodel - -def set_up_ecolicore_pam(total_protein:bool = True, active_enzymes: bool = True, translational_enzymes:bool = True, unused_enzymes:bool = True, sensitivity =True): - # Setting the relative paths - PAM_DATA_FILE_PATH = os.path.join('Data', 'proteinAllocationModel_iML1515_EnzymaticData_py.xls') - - # some other constants - BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM' - TOTAL_PROTEIN_CONCENTRATION = 0.16995 # [g_prot/g_cdw] - - # load the genome-scale information - model = cobra.io.load_json_model(os.path.join('Models', 'e_coli_core.json')) - - #load example data for the E.coli iML1515 model - if active_enzymes: - # load active enzyme sector information - enzyme_db = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name='ActiveEnzymes') - enzyme_db = enzyme_db.set_index('rxnID') - # correct reaction IDS - for idx in enzyme_db.index.to_list(): - # transprt reactions< - - if 'pp' in idx: - idx_new = idx.replace('pp', '') - if idx_new not in enzyme_db.index: - enzyme_db.rename(index={idx: idx_new}, inplace=True) - if 'ex' in idx: - idx_new = idx.replace('ex', '') - if idx_new not in enzyme_db.index: - enzyme_db.rename(index={idx: idx_new}, inplace=True) - - # replace NaN values with unique identifiers - # replace NaN enzyme ids with a dummy enzyme identifier - # select the NaN values - nan_values = enzyme_db['EC_nmbr'].isnull() - # make a list with unique ids - nan_ids = [f'E{i}' for i in range(nan_values.sum())] - # replace nan values by unique id - enzyme_db.loc[nan_values, 'EC_nmbr'] = nan_ids - - # create enzyme objects for each gene-associated reaction - kcats = {} - rxn2ec = {} - molmass = {} - for rxn in model.reactions: - if rxn.genes: - # correct transport reactions - if 't' in rxn.id: - rxn.id = rxn.id - # are enzyme information in the PAM database? - rev = 0 # denotes reversibility - if rxn.lower_bound >= 0: - # irreversible reaction (forward direction) - rev = 0 - rxn_id = rxn.id # save reaction ID for retrieveing molar masses/enzyme information later - if rxn.id in enzyme_db.index: - kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat']} - elif rxn.upper_bound <= 0: - # irreversible reaction (reverse direction) - rev = 1 - rxn_id = rxn.id + '_b' - if rxn_id in enzyme_db.index: - kcats[rxn.id] = {'b': enzyme_db.loc[rxn_id, 'kcat']} - else: - rev = 2 - # reversible reaction - rxn_id_f = rxn.id + '_f' - rxn_id_b = rxn.id + '_b' - if rxn_id_f in enzyme_db.index and rxn_id_b in enzyme_db.index: - rxn_id = rxn_id_f # save reaction ID for retrieveing molar masses/enzyme information later - kcats[rxn.id] = {'f': enzyme_db.loc[rxn_id_f, 'kcat'], - 'b': enzyme_db.loc[rxn_id_b, 'kcat']} - - else: - # try if only forward reaction is in database - rxn_id = rxn.id # save reaction ID for retrieveing molar masses/enzyme information later - kcats[rxn.id] = {'f': enzyme_db.loc[rxn.id, 'kcat'], - 'b': enzyme_db.loc[ - rxn.id, 'kcat'] / 2} # deduce backwards kcat from forward value - - # where enzyme information found? - if rxn.id in kcats.keys(): - # save molmass - molmass[rxn.id] = enzyme_db.loc[rxn_id, 'molMass'] - # save enzyme information - # is enzyme information NaN? - if pd.isna(enzyme_db.loc[rxn_id, 'EC_nmbr']): - rxn2ec[rxn.id] = '' - else: - rxn2ec[rxn.id] = enzyme_db.loc[rxn_id, 'EC_nmbr'] - - - else: - # no enzyme information found - print('No enzyme information found for reaction: ' + rxn.id) - # Create generic Enzyme with mean molar masses and kcat - if rev == 0: - kcats[rxn.id] = {'f': 22} - elif rev == 1: - kcats[rxn.id] = {'b': 22} - else: - kcats[rxn.id] = {'f': 22, 'b': 22} - - molmass[rxn.id] = 3.947778784340140e04 - - rxn2protein = {} - for rxn, ec in rxn2ec.items(): - ec_dict = {**kcats[rxn], **{'molmass': molmass[rxn]}} - # add enzyme to enzymes related to reaction if these are already stored - if rxn in rxn2protein.keys(): - rxn2protein[rxn] = {**rxn2protein[rxn], **{ec: ec_dict}} - # if not create new reaction entry - else: - rxn2protein[rxn] = {ec: ec_dict} - - # create active enzymes sector - active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein) - - else: - active_enzyme_sector = None - - if translational_enzymes: - # translational protein sector parameter (substrate dependent) - id_list_tps = ['EX_glc__D_e'] - tps_0 = [0.04992] # g/gDW - tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable - molmass_tps = [405903.94] # g/mol - - # translational protein sector - translation_enzyme_sector = TransEnzymeSector( - id_list=id_list_tps, - tps_0=tps_0, - tps_mu=tps_mu, - mol_mass=molmass_tps, - ) - else: - translation_enzyme_sector = None - - if unused_enzymes: - id_list_ups = [BIOMASS_REACTION] - ups_0 = [0.0407] # g/gDW - ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate - molmass_ups = [405903.94] # g/mol - - unused_enzyme_sector = UnusedEnzymeSector( - id_list=id_list_ups, - ups_0=ups_0, - ups_mu=ups_mu, - mol_mass=molmass_ups, - ) - else: - unused_enzyme_sector = None - - if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION - - pa_model = PAModel(id_or_model=model, p_tot=total_protein, sensitivity=sensitivity, - active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, unused_sector=unused_enzyme_sector) - return pa_model - -def set_up_ecoli_pam(total_protein: Union[bool, float] = True, active_enzymes: bool = True, - translational_enzymes: bool = True, unused_enzymes: bool = True, sensitivity = True): - - config = Config() - config.reset() - # Setting the relative paths - pam_info_file = os.path.join('Data', 'proteinAllocationModel_iML1515_EnzymaticData_py.xls') - - # some other constants - TOTAL_PROTEIN_CONCENTRATION = 0.258 # [g_prot/g_cdw] - - #setup the gem ecoli iML1515 model - model = cobra.io.read_sbml_model(os.path.join('Models', 'iML1515.xml')) - - #check if a different total protein concentration is given - if isinstance(total_protein, float): - TOTAL_PROTEIN_CONCENTRATION = total_protein - - # load example data for the E.coli iML1515 model - if active_enzymes: - # load active enzyme sector information - active_enzyme_info_old = pd.read_excel(pam_info_file, sheet_name='ActiveEnzymes') - - # replace NaN values with unique identifiers - # select the NaN values - nan_values = active_enzyme_info_old['EC_nmbr'].isnull() - # make a list with unique ids - nan_ids = [f'E{i}' for i in range(nan_values.sum())] - # replace nan values by unique id - active_enzyme_info_old.loc[nan_values, 'EC_nmbr'] = nan_ids - - # parse the enzyme information (kcat values, identifiers and molmasses) - kcats_dict = active_enzyme_info_old.set_index(keys='rxnID').loc[:, 'kcat'].to_dict() - ec_dict = active_enzyme_info_old.set_index(keys='rxnID').loc[:, 'EC_nmbr'].to_dict() - molmass_dict = mol_mass = active_enzyme_info_old.set_index(keys='rxnID').loc[:, 'molMass'].to_dict() - - kcats = {} - # save fwd and bckw kcats separately in the form of: {rxn_id: {'f': kcat_f, 'b': kcat_b}} - for rxn, kcat in kcats_dict.items(): - # reversible reaction - if rxn[-2:] == '_f' or rxn[-2:] == '_b': - direction = rxn[-1] - # check if the reaction already exists in the kcat dictionary - try: - kcats[rxn[:-2]][direction] = kcat - except: - kcats[rxn[:-2]] = {direction: kcat} - # irreversible reaction - else: - kcats[rxn] = {'f': kcat} - - rxn2ec = {} - # parse the enzyme identifiers for the reactions - for rxn, ec in ec_dict.items(): - if rxn[-2:] == '_f' or rxn[-2:] == '_b': - rxn = rxn[:-2] - for enz in str(ec).split(','): - rxn2ec[rxn] = enz.strip() - - molmass = {} - # parse the enzyme molmasses for the reactions - for rxn, mw in molmass_dict.items(): - if rxn[-2:] == '_f' or rxn[-2:] == '_b': - rxn = rxn[:-2] - molmass[rxn] = mw - - rxn2protein = {} - for rxn, ec in rxn2ec.items(): - ec_dict = {**kcats[rxn], **{'molmass': molmass[rxn]}} - # add enzyme to enzymes related to reaction if these are already stored - if rxn in rxn2protein.keys(): - rxn2protein[rxn] = {**rxn2protein[rxn], **{ec: ec_dict}} - # if not create new reaction entry - else: - rxn2protein[rxn] = {ec: ec_dict} - - active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, configuration = config) - - else: - active_enzyme_sector = None - - if translational_enzymes: - translational_info = pd.read_excel(pam_info_file, sheet_name='Translational') - translation_enzyme_sector = TransEnzymeSector( - id_list=[translational_info[translational_info.Parameter == 'id_list'].loc[0, 'Value']], - tps_0=[translational_info[translational_info.Parameter == 'tps_0'].loc[1, 'Value']], - tps_mu=[-translational_info[translational_info.Parameter == 'tps_mu'].loc[2, 'Value']], - mol_mass=[translational_info[translational_info.Parameter == 'mol_mass'].loc[3, 'Value']], - configuration = config) - else: - translation_enzyme_sector = None - - if unused_enzymes: - unused_protein_info = pd.read_excel(pam_info_file, sheet_name='ExcessEnzymes') - - ups_0 = unused_protein_info[unused_protein_info.Parameter == 'ups_0'].loc[2, 'Value'] - smax = unused_protein_info[unused_protein_info.Parameter == 's_max_uptake'].loc[1, 'Value'] - - unused_protein_sector = UnusedEnzymeSector( - id_list=[unused_protein_info[unused_protein_info.Parameter == 'id_list'].loc[0, 'Value']], - ups_mu=[ups_0 / smax], - ups_0=[ups_0], - mol_mass=[unused_protein_info[unused_protein_info.Parameter == 'mol_mass'].loc[3, 'Value']], - configuration = config) - else: - unused_protein_sector = None - - if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION - - pamodel = PAModel(id_or_model=model, p_tot=total_protein, - active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, - unused_sector=unused_protein_sector, sensitivity=sensitivity, configuration = config - ) - return pamodel - -def parse_coefficients(pamodel): - Ccsc = list() - - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - Ccsc += pamodel.capacity_sensitivity_coefficients[ - pamodel.capacity_sensitivity_coefficients['constraint'] == csc].coefficient.to_list() - - Cesc = pamodel.enzyme_sensitivity_coefficients.coefficient.to_list() - - return Ccsc, Cesc - -def parse_esc(pamodel): - return pamodel.enzyme_sensitivity_coefficients.coefficient.to_list() - -if __name__ == '__main__': - ecoli_pam = set_up_ecoli_pam(sensitivity=False) - # ecoli_pam.objective = ecoli_pam.BIOMASS_REACTION - ecoli_pam.change_reaction_bounds('EX_glc__D_e', -10, 0) - ecoli_pam.optimize() - print(ecoli_pam.objective.value) - import pickle - - with open('path_to_your_pickle_file.pkl', 'wb') as file: - p = pickle.dump(ecoli_pam, file) - with open('path_to_your_pickle_file.pkl', 'rb') as file: - ob = pickle.load(file) \ No newline at end of file diff --git a/Scripts/pam_generation_uniprot_id.py b/Scripts/pam_generation_uniprot_id.py deleted file mode 100644 index b4db582..0000000 --- a/Scripts/pam_generation_uniprot_id.py +++ /dev/null @@ -1,569 +0,0 @@ -import cobra -import pandas as pd -import numpy as np -import os -from typing import Union - -# load PAMpy modules -from src.PAModelpy.PAModel import PAModel -from src.PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector -from src.PAModelpy.MembraneSector import MembraneSector -from src.PAModelpy.configuration import Config -from src.PAModelpy import CatalyticEvent, EnzymeVariable - -from Scripts.toy_ec_pam import build_toy_gem, build_active_enzyme_sector, build_translational_protein_sector, build_unused_protein_sector -import ast - -'Function library for making Protein Allocation Models as described in the publication' - - -def set_up_toy_pam(sensitivity =True): - config = Config() - #setting the configuration for the toy model - config.BIOMASS_REACTION = 'R7' - config.GLUCOSE_EXCHANGE_RXNID = 'R1' - config.CO2_EXHANGE_RXNID = 'R8' - config.ACETATE_EXCRETION_RXNID = 'R9' - - Etot = 0.6*1e-3 - model = build_toy_gem() - active_enzyme = build_active_enzyme_sector(config) - unused_enzyme = build_unused_protein_sector(config) - translation_enzyme = build_translational_protein_sector(config) - pamodel = PAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme, - translational_sector=translation_enzyme, - unused_sector=unused_enzyme, p_tot=Etot, sensitivity=sensitivity) - pamodel.objective = 'R7' - config.reset() - return pamodel - -def set_up_ecolicore_pam(total_protein:bool = True, - active_enzymes: bool = True, - translational_enzymes:bool = True, - unused_enzymes:bool = True, - sensitivity:bool =True): - # Setting the relative paths - PAM_DATA_FILE_PATH = os.path.join('Data', 'proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx') - - config = Config() - config.reset() - - # some other constants - BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM' - TOTAL_PROTEIN_CONCENTRATION = 0.16995 # [g_prot/g_cdw] - - # load the genome-scale information - model = cobra.io.load_json_model(os.path.join('Models', 'e_coli_core.json')) - - #load example data for the E.coli iML1515 model - if active_enzymes: - # load active enzyme sector information - enzyme_db = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name='ActiveEnzymes') - # create enzyme objects for each gene-associated reaction - rxn2protein, protein2gene = parse_reaction2protein(enzyme_db, model) - - active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene, - configuration=config) - - else: - active_enzyme_sector = None - - if translational_enzymes: - # translational protein sector parameter (substrate dependent) - id_list_tps = ['EX_glc__D_e'] - tps_0 = [0.04992] # g/gDW - tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable - molmass_tps = [405903.94] # g/mol - - # translational protein sector - translation_enzyme_sector = TransEnzymeSector( - id_list=id_list_tps, - tps_0=tps_0, - tps_mu=tps_mu, - mol_mass=molmass_tps, - ) - else: - translation_enzyme_sector = None - - if unused_enzymes: - id_list_ups = [BIOMASS_REACTION] - ups_0 = [0.0407] # g/gDW - ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate - molmass_ups = [405903.94] # g/mol - - unused_enzyme_sector = UnusedEnzymeSector( - id_list=id_list_ups, - ups_0=ups_0, - ups_mu=ups_mu, - mol_mass=molmass_ups, - ) - else: - unused_enzyme_sector = None - - if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION - - pa_model = PAModel(id_or_model=model, p_tot=total_protein, sensitivity=sensitivity, - active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, unused_sector=unused_enzyme_sector) - return pa_model - -def set_up_ecolicore_mcpam(total_protein: Union[bool, float] = True, - active_enzymes: bool = True, - translational_enzymes: bool = True, - unused_enzymes: bool = True, - membrane_sector: bool = True, - max_area:float = 0.03, - sensitivity = True): - - config = Config() - config.reset() - - pam_info_file = os.path.join('Data', 'mcPAM_iML1515_EnzymaticData.xlsx') - - # some other constants - BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM' - TOTAL_PROTEIN_CONCENTRATION = 0.16995 # [g_prot/g_cdw] - - config.BIOMASS_REACTION = BIOMASS_REACTION - - # load the genome-scale information - model = cobra.io.load_json_model(os.path.join('Models', 'e_coli_core.json')) - - # load example data for the E.coli iML1515 model - if active_enzymes: - # load active enzyme sector information - enzyme_db = pd.read_excel(pam_info_file, sheet_name='mcPAM_data_parametrized') - - for idx in enzyme_db.rxnID: - # transport reactions - if 'pp' in idx: - idx_new = idx.replace('pp', '') - if idx_new not in enzyme_db.rxnID: - enzyme_db = enzyme_db.replace(idx, idx_new) - if 'ex' in idx: - idx_new = idx.replace('ex', '') - if idx_new not in enzyme_db.rxnID: - enzyme_db = enzyme_db.replace(idx, idx_new) - - # create enzyme objects for each gene-associated reaction - rxn2protein, protein2gene = parse_reaction2protein(enzyme_db, model) - - active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene, - configuration=config) - - else: - active_enzyme_sector = None - - if translational_enzymes: - # translational protein sector parameter (substrate dependent) - id_list_tps = ['EX_glc__D_e'] - tps_0 = [0.04992] # g/gDW - tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable - molmass_tps = [405903.94] # g/mol - - # translational protein sector - translation_enzyme_sector = TransEnzymeSector( - id_list=id_list_tps, - tps_0=tps_0, - tps_mu=tps_mu, - mol_mass=molmass_tps, - ) - else: - translation_enzyme_sector = None - - if unused_enzymes: - id_list_ups = [BIOMASS_REACTION] - ups_0 = [0.0407] # g/gDW - ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate - molmass_ups = [405903.94] # g/mol - - unused_enzyme_sector = UnusedEnzymeSector( - id_list=id_list_ups, - ups_0=ups_0, - ups_mu=ups_mu, - mol_mass=molmass_ups, - ) - else: - unused_enzyme_sector = None - - if membrane_sector: - membrane_info = pd.read_excel(pam_info_file, sheet_name='Membrane') - active_enzyme_info = pd.read_excel(pam_info_file, sheet_name='mcPAM_data_parametrized') - - area_avail_0 = membrane_info[membrane_info.Parameter == 'area_avail_0'].loc[1,'Value'] - area_avail_mu = membrane_info[membrane_info.Parameter == 'area_avail_mu'].loc[2,'Value'] - alpha_numbers_dict = active_enzyme_info.set_index(keys='uniprotID').loc[:, 'alpha_numbers'].to_dict() - enzyme_location = active_enzyme_info.set_index(keys='uniprotID').loc[:, 'Location'].to_dict() - - membrane_sector = MembraneSector(area_avail_0=area_avail_0, - area_avail_mu=area_avail_mu, - alpha_numbers_dict=alpha_numbers_dict, - enzyme_location=enzyme_location, - max_area=max_area) - - else: - membrane_sector = None - - if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION - - pamodel = PAModel(id_or_model=model, p_tot=total_protein, - active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, - unused_sector=unused_enzyme_sector, sensitivity=sensitivity, configuration = config, - membrane_sector=membrane_sector - ) - return pamodel - -def set_up_ecoli_pam(total_protein: Union[bool, float] = True, active_enzymes: bool = True, - translational_enzymes: bool = True, unused_enzymes: bool = True, sensitivity = True): - - config = Config() - config.reset() - - pam_info_file = os.path.join('Data', 'proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx') - - # some other constants - TOTAL_PROTEIN_CONCENTRATION = 0.258 # [g_prot/g_cdw] - - #setup the gem ecoli iML1515 model - model = cobra.io.read_sbml_model(os.path.join('Models', 'iML1515.xml')) - - #check if a different total protein concentration is given - if isinstance(total_protein, float): - TOTAL_PROTEIN_CONCENTRATION = total_protein - - # load example data for the E.coli iML1515 model - if active_enzymes: - # load active enzyme sector information - enzyme_db = pd.read_excel(pam_info_file, sheet_name='ActiveEnzymes') - - # create enzyme objects for each gene-associated reaction - rxn2protein, protein2gene = parse_reaction2protein(enzyme_db, model) - - active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene, - configuration=config) - - else: - active_enzyme_sector = None - - if translational_enzymes: - translational_info = pd.read_excel(pam_info_file, sheet_name='Translational') - translation_enzyme_sector = TransEnzymeSector( - id_list=[translational_info[translational_info.Parameter == 'id_list'].loc[0, 'Value']], - tps_0=[translational_info[translational_info.Parameter == 'tps_0'].loc[1, 'Value']], - tps_mu=[-translational_info[translational_info.Parameter == 'tps_mu'].loc[2, 'Value']], - mol_mass=[translational_info[translational_info.Parameter == 'mol_mass'].loc[3, 'Value']], - configuration = config) - else: - translation_enzyme_sector = None - - if unused_enzymes: - unused_protein_info = pd.read_excel(pam_info_file, sheet_name='ExcessEnzymes') - - ups_0 = unused_protein_info[unused_protein_info.Parameter == 'ups_0'].loc[2, 'Value'] - smax = unused_protein_info[unused_protein_info.Parameter == 's_max_uptake'].loc[1, 'Value'] - - unused_protein_sector = UnusedEnzymeSector( - id_list=[unused_protein_info[unused_protein_info.Parameter == 'id_list'].loc[0, 'Value']], - ups_mu=[ups_0 / smax], - ups_0=[ups_0], - mol_mass=[unused_protein_info[unused_protein_info.Parameter == 'mol_mass'].loc[3, 'Value']], - configuration = config) - else: - unused_protein_sector = None - - if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION - - pamodel = PAModel(id_or_model=model, p_tot=total_protein, - active_sector=active_enzyme_sector, translational_sector=translation_enzyme_sector, - unused_sector=unused_protein_sector, sensitivity=sensitivity, configuration = config - ) - return pamodel - - -def _parse_enzyme_information_from_file(file_path:str): - # load active enzyme sector information - enzyme_db = pd.read_excel(file_path, sheet_name='enzyme-gene-reaction') - # enzyme_db = enzyme_db.set_index('rxnID') - # correct reaction IDS - for idx in enzyme_db.rxnID.to_list(): - # transport reactions - if 'pp' in idx: - idx_new = idx.replace('pp', '') - if idx_new not in enzyme_db.index: - enzyme_db.rename(index={idx: idx_new}, inplace=True) - if 'ex' in idx: - idx_new = idx.replace('ex', '') - if idx_new not in enzyme_db.index: - enzyme_db.rename(index={idx: idx_new}, inplace=True) - - # replace NaN values with unique identifiers - # replace NaN enzyme ids with a dummy enzyme identifier - # select the NaN values - nan_values = enzyme_db['EC_nmbr'].isnull() - # make a list with unique ids - nan_ids = [f'E{i}' for i in range(nan_values.sum())] - # replace nan values by unique id - enzyme_db.loc[nan_values, 'EC_nmbr'] = nan_ids - - return enzyme_db - - -def parse_reaction2protein(enzyme_db: pd.DataFrame, model:cobra.Model) -> dict: - # Initialize dictionaries - rxn2protein = {} - protein2gpr = {} - - # replace NaN values with unique identifiers - # select the NaN values - nan_values = enzyme_db['uniprotID'].isnull() - # make a list with unique ids - nan_ids = [f'E{i}' for i in range(nan_values.sum())] - # replace nan values by unique id - enzyme_db.loc[nan_values, 'uniprotID'] = nan_ids - - protein2gene, gene2protein = _get_genes_for_proteins(enzyme_db, model) - - # Iterate over each row in the DataFrame - for index, row in enzyme_db.iterrows(): - # Parse data from the row - rxn_id = row['rxnID'] - if rxn_id not in model.reactions: continue - # only parse those reactions which are in the model - kcat_f_b = [row['kcat_f'], row['kcat_b']] - kcat_f_b = [kcat if not np.isnan(kcat) else 0 for kcat in kcat_f_b] - if all([np.isnan(kcat) for kcat in kcat_f_b]): continue - - rxn = model.reactions.get_by_id(rxn_id) - # get the identifiers and replace nan values by dummy placeholders - enzyme_id = row['uniprotID'] - gene_id = row['m_gene'] - - # check if there are genes associates with the reaction - if len(rxn.genes) > 0 or isinstance(gene_id, str): - if not isinstance(enzyme_id, str): - enzyme_id = 'Enzyme_' + rxn_id - row['molMass'] = 39959.4825 # default molmass - if not isinstance(gene_id, str): - gene_id = [gene.id for gene in rxn.genes][0] # TODO - - # get the gene-protein-reaction-associations for this specific enzyme - gr, pr = parse_gpr_information_for_rxn2protein(row['m_gene_reaction_rule'], - gene2protein, protein2gene,enzyme_id) - - if pr is None: pr = [[enzyme_id]] - - if enzyme_id not in protein2gpr: - protein2gpr[enzyme_id] = gr - else: - protein2gpr[enzyme_id].append(gr) - - # Create rxn2protein dictionary - if rxn_id not in rxn2protein: - rxn2protein[rxn_id] = {} - if enzyme_id not in rxn2protein[rxn_id]: - rxn2protein[rxn_id][enzyme_id] = { - 'f': kcat_f_b[0], # Forward kcat - 'b': kcat_f_b[1], # Backward kcat - 'molmass': row['molMass'], - 'genes': gr, - 'protein_reaction_association': pr - } - else: - rxn2protein[rxn_id][enzyme_id]['genes'].append(gene_id) - - # if no enzyme info is found, add dummy enzyme with median kcat and molmass - for rxn in model.reactions: - if rxn.id not in rxn2protein.keys() and 'EX'.lower() not in rxn.id.lower() and 'BIOMASS' not in rxn.id and len( - rxn._genes) > 0 and list(rxn._genes)[0].id != 's0001': - rev = _check_reaction_reversibility(rxn) - if rev == 0: - kcat_dict = {'f': 22} - elif rev == 1: - kcat_dict = {'b': 22} - else: - kcat_dict = {'f': 22, 'b': 22} - # no enzyme information found - print('No enzyme information found for reaction: ' + rxn.id) - enzyme_id = 'Enzyme_' + rxn.id - gpr_info = parse_gpr_information_for_protein2genes(rxn.gpr) - - rxn2protein[rxn.id] = {enzyme_id: { - **kcat_dict, - 'molmass': 3.947778784340140e04, - 'genes': gpr_info - }} - # add geneinfo for unknown enzymes - protein2gpr[enzyme_id] = gpr_info - - return rxn2protein, protein2gpr - -def _get_genes_for_proteins(enzyme_db: pd.DataFrame, model) -> dict: - protein2gene = {} - gene2protein = {} - for index, row in enzyme_db.iterrows(): - # Parse data from the row - rxn_id = row['rxnID'] - if rxn_id not in model.reactions:continue - rxn = model.reactions.get_by_id(rxn_id) - # get the identifiers and replace nan values by dummy placeholders - enzyme_id = row['uniprotID'] - gene_id = row['m_gene'] - - # check if there are genes associates with the reaction - if len(rxn.genes) > 0 or isinstance(gene_id, str): - if not isinstance(enzyme_id, str): - enzyme_id = 'Enzyme_' + rxn_id - row['molMass'] = 39959.4825 # default molmass - if not isinstance(gene_id, str): - gene_id = [gene.id for gene in rxn.genes][0] # TODO - - gene2protein[gene_id] = enzyme_id - - # Create gene-protein-reaction associations - if enzyme_id not in protein2gene: - protein2gene[enzyme_id] = [gene_id] - elif enzyme_id in protein2gene: - # assume that there is a single protein if the previous relation was not assigned a gene - if 'gene_' in protein2gene[enzyme_id][0]: - protein2gene[enzyme_id] = [gene_id] - else: - protein2gene[enzyme_id].append(gene_id) - - return protein2gene, gene2protein - - -def _check_rxn_identifier_format(rxn_id:str) -> str: - if 'pp' in rxn_id: - idx_new = rxn_id.replace('pp', '') - elif 'ex' in rxn_id: - idx_new = rxn_id.replace('ex', '') - else: - idx_new = rxn_id - return idx_new - -def _get_fwd_bckw_kcat(rxn_id: str, kcat:float, model:PAModel) -> Union[list, None]: - #skip exchange and biomass reaction - if 'EX' in rxn_id or 'BIOMASS' in rxn_id: - return None - - # Extract the base identifier without any suffixes - base_id = rxn_id.split('_')[0] - - # Iterate over each identifier in the input - if base_id in model.reactions: - # Determine the form of the identifier - if rxn_id.endswith('_f'): - kcat_fwd = kcat - kcat_rev = 0 - elif rxn_id.endswith('_b'): - kcat_fwd = 0 - kcat_rev = kcat - # identifier has no suffix - elif base_id == rxn_id: - kcat_fwd = kcat - kcat_rev = kcat - else: - return None - elif rxn_id in model.reactions: - kcat_fwd = kcat - kcat_rev = kcat - else: - return None - return [kcat_fwd, kcat_rev] - -def _check_reaction_reversibility(reaction): - if reaction.lower_bound >= 0: - # irreversible reaction (forward direction) - rev = 0 - elif reaction.upper_bound <= 0: - # irreversible reaction (reverse direction) - rev = 1 - else: - rev = 2 - # reversible r - return rev - -def parse_gpr_information_for_protein2genes(gpr_info:str): - #filter out nan entries - if isinstance(gpr_info, str): - gpr_list = parse_gpr(gpr_info) - # Extracting the inner lists and removing parentheses - # gpr_list = [[[item.strip("(')")] + sublist[1:] for item in sublist[0].split(" or ")] for sublist in nested_list] - return gpr_list - else: - return [['gene_dummy']] - -def parse_gpr_information_for_rxn2protein(gpr_info:str, gene2protein: dict,protein2gene:dict, enzyme_id:str): - #filter out nan entries - if isinstance(gpr_info, str): - gpr_list = parse_gpr(gpr_info) - # Extracting the inner lists and removing parentheses - gene = protein2gene[enzyme_id] - if isinstance(protein2gene[enzyme_id], list): - gene = gene[0] - gpr_list = filter_sublists(gpr_list, gene) - #convert the genes to the associated proteins - enzyme_relations = [] - for sublist in gpr_list: - enz_sublist = [] - for item in sublist: - if item in gene2protein.keys(): - enz_sublist.append(gene2protein[item]) - enzyme_relations += [enz_sublist] - enzyme_relations = filter_sublists(enzyme_relations, enzyme_id) - return gpr_list, enzyme_relations - else: - return [['gene_dummy']], None - -def parse_gpr(gpr_info): - # Split the string by 'or' first - or_groups = gpr_info.split(' or ') - parsed_gpr = [] - - for group in or_groups: - # Within each 'or' group, split by 'and' - and_genes = group.split(' and ') - # Clean up each gene string - and_genes = [gene.strip(' ()') for gene in and_genes] - parsed_gpr.append(and_genes) - - return parsed_gpr - -def filter_sublists(nested_list, target_string): - """ - Filters out all sublists from a nested list that contain the target string. - - Args: - nested_list (list of list of str): The nested list to filter. - target_string (str): The string to filter out sublists that contain it. - - Returns: - list of list of str: A new nested list with the filtered sublists. - """ - return [sublist for sublist in nested_list if target_string in sublist] - -if __name__ == '__main__': - VALID_DATA_PATH = os.path.join('Data', 'Ecoli_phenotypes', 'Ecoli_phenotypes_py_rev.xls') - valid_data_df = pd.read_excel(VALID_DATA_PATH, sheet_name='Yields').sort_values('BIOMASS_Ec_iML1515_core_75p37M', - ascending = False) - max_mu = valid_data_df.at[0,'BIOMASS_Ec_iML1515_core_75p37M'] - - init_kcat = 11 - - for i in range(1,4,2): - pam = set_up_ecoli_pam(sensitivity=False) - pam.change_reaction_bounds('EX_glc__D_e', -1e3, 0) - for enzyme in pam.enzymes: - kcats = enzyme.rxn2kcat.copy() - for rxn, kcat_dict in kcats.items(): - if all([val == 0 for val in kcat_dict.values()]): - continue - for dir, kcat in kcat_dict.items(): - if kcat == 11: - kcats[rxn][dir] = init_kcat*i - pam.change_kcat_value(enzyme.id, kcats) - pam.optimize() - if pam.objective.value >= max_mu*0.9: - print(init_kcat*i, pam.objective.value) - break - else: - print(i, pam.objective.value) \ No newline at end of file diff --git a/Scripts/protein_costs_analysis.py b/Scripts/protein_costs_analysis.py deleted file mode 100644 index 63a1da8..0000000 --- a/Scripts/protein_costs_analysis.py +++ /dev/null @@ -1,22 +0,0 @@ -import os -import pandas as pd - -PAM_DATA_FILE_PATH = os.path.join('Data', 'proteinAllocationModel_iML1515_EnzymaticData_py.xls') -RESULT_DATA_FILE = os.path.join('Results', 'protein_costs_glycolysis_tca.xlsx') - -active_enzyme_info = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name='ActiveEnzymes') - -tca_glycolysis_reactions = ['GLCptspp','PGI','PFK','FBA','TPI','GAPD','PGK','PGM','ENO','PYK','PDH','ATPS4rpp', - 'CYTBO34pp','NADH16pp','CS','ACONTa','ACONTb','ICDHyr','AKGDH','SUCOAS','FUM','MDH', - 'SUCDi','NADTRHD','ACKr', 'ACt2rpp','PTAr', 'ACACT1r','ACACT2r','ACACT3r','ACACT4r', - 'ACACT7r','ACACT8r'] - -protein_efficiency_df = active_enzyme_info.filter(['rxnID','molMass','kcat']) -protein_efficiency_df = protein_efficiency_df.assign(rxnName = lambda x: x.rxnID.str.rstrip('_fb')) -protein_efficiency_glyc_tca = protein_efficiency_df[protein_efficiency_df.rxnName.isin(tca_glycolysis_reactions)] -protein_efficiency_glyc_tca = protein_efficiency_glyc_tca.assign(kcat = lambda x:x['kcat']*3600, - relEfficiency = lambda x: x['molMass']/x['kcat'] - ).sort_values(by = 'relEfficiency', ascending=False) -print(protein_efficiency_glyc_tca) - -protein_efficiency_glyc_tca.to_excel(RESULT_DATA_FILE, sheet_name='protein_costs') \ No newline at end of file diff --git a/Scripts/sensitivity_analysis.py b/Scripts/sensitivity_analysis.py deleted file mode 100644 index 1795361..0000000 --- a/Scripts/sensitivity_analysis.py +++ /dev/null @@ -1,388 +0,0 @@ -from matplotlib import pyplot as plt -import matplotlib.gridspec as gridspec -import matplotlib.colors as mcolors - -import pandas as pd -import os -import sys -import numpy as np - -sys.path.append('C:\\Users\\claud\\Documents\\iamb-student-folders\\iamb-folder-template\\mcPAM_package') - -from src.PAModelpy.configuration import Config -if os.path.split(os.getcwd())[1] == 'Figures': - os.chdir(os.path.split(os.getcwd())[0]) -from Scripts.mcpam_generation_uniprot_id import set_up_ecolicore_pam, set_up_ecoli_mcpam -from Scripts.mcpam_simulations_analysis import run_pam_mcpam_core_with_optimized_kcats - -Config.BIOMASS_REACTION = 'BIOMASS_Ec_iML1515_core_75p37M' -DATA_DIR = os.path.join('Data') # os.path.join(os.path.split(os.getcwd())[0], 'Data') -glc_uptake_rates = list(np.linspace(1, 10, 10)) - -### 1 Useful functions -def calculate_sensitivities(pamodel): - glc_uptake_rates = np.linspace(1, 10, 10) - Ccsc = [] - Cesc = [] - y_axis = [] - fluxes = [] - - # disable pyruvate formate lyase (inhibited by oxygen) - pamodel.change_reaction_bounds(rxn_id='PFL', upper_bound=0) - - for glc in glc_uptake_rates: - print('glucose uptake rate ', glc, ' mmol/gcdw/h') - with pamodel: - # change glucose uptake rate - pamodel.change_reaction_bounds(rxn_id='EX_glc__D_e', - lower_bound=-glc, upper_bound=-glc) - # solve the model - # pamodel.objective = 'EX_ac_e' - sol_pam = pamodel.optimize() - fluxes.append(sol_pam.fluxes) - if pamodel.solver.status == 'optimal': y_axis += [glc] - # save data - Ccsc_new = list() - - if pamodel.solver.status == 'optimal': - capacity_coeff = pamodel.capacity_sensitivity_coefficients - # for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: # for PAM without membrane - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector', 'membrane']: - Ccsc_new += capacity_coeff[capacity_coeff['constraint'] == csc].coefficient.to_list() - - Ccsc += [Ccsc_new] - - enzyme_coeff = pamodel.enzyme_sensitivity_coefficients - Cesc += [enzyme_coeff.coefficient.to_list()] - - print('Sum of capacity sensitivity coefficients: \t \t \t \t \t \t', round(sum(Ccsc_new), 6)) - print('Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t', round(sum(Cesc[-1]), 6), '\n') - - return {'Ccsc': Ccsc, 'Cesc': Cesc, 'y_axis': y_axis, 'fluxes': fluxes, 'capacity coefficients': capacity_coeff, - 'enzyme coefficients': enzyme_coeff} - - -# %% -def parse_x_axis_heatmap(capacity_coeff, enzyme_coeff): - x_axis_csc = [] - - # for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: # for PAM without membrane - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector', 'membrane']: - if csc == 'flux_ub' or csc == 'flux_lb': - x_axis_csc += [coef + '_' + csc for coef in - capacity_coeff[capacity_coeff['constraint'] == csc].rxn_id.to_list()] - else: - x_axis_csc += [coef + '_' + csc for coef in capacity_coeff[ - capacity_coeff['constraint'] == csc].enzyme_id.to_list()] - - x_axis_esc = enzyme_coeff.enzyme_id.to_list() - return x_axis_csc, x_axis_esc - - -# %% -def make_heatmap_subfigure(results, csc_matrix, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc, - ylabels=True, xlabels=False, cbar=True, title=None, fontsize=5, - vmin=-1.5, vmax=1.5, annotate=None, phenotype_data=None, cmap=None - # cmap = plt.cm.get_cmap('viridis') - ): - # fig = plt.figure() - # adjust labels for better readibility - x_csc = adjust_heatmap_labels(x_csc_nonzero_pam) - x_esc = adjust_heatmap_labels(x_esc_top5_pam) - - if cmap is None: - # Create separate colormaps for positive and negative values and a color for zero - colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128)) - colors_pos = plt.cm.OrRd(np.linspace(0.1, 1, 128)) # plt.cm.Reds(np.linspace(0, 0.5, 128)) - colors_zero = np.array([[1, 1, 1, 1]]) # gray for zero - - # Combine them into a single colormap - colors = np.vstack((colors_neg, colors_zero, colors_pos)) - combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap') - - # Create a norm that handles the zero color properly - bounds = np.linspace(vmin, vmax, len(colors)) - norm = mcolors.BoundaryNorm(bounds, combined_cmap.N) - - if cbar: - gs = gridspec.GridSpecFromSubplotSpec(3, 2, width_ratios=[len(yaxis), 1], - height_ratios=[1, len(x_csc), len(x_esc)], hspace=0, - subplot_spec=grdspc) - else: - gs = gridspec.GridSpecFromSubplotSpec(3, 1, width_ratios=[len(yaxis)], - height_ratios=[1, len(x_csc), len(x_esc)], hspace=0, - subplot_spec=grdspc) - - acetate_ax = fig.add_subplot(gs[0, 0]) # acetate production - csc_ax = fig.add_subplot(gs[1, 0]) # CSC heatmap - esc_ax = fig.add_subplot(gs[2, 0], sharex=csc_ax) # ESC heatmap - - if cbar: - cbar_ax = fig.add_subplot(gs[1:, 1]) # colorbar - - # add annotation for subfigure (A or B) - if annotate is not None: - acetate_ax.annotate(annotate, xy=(2, 1), xycoords='data', - xytext=(-0.05, 1.5), textcoords='axes fraction', - va='top', ha='left', fontsize=fontsize * 1.5, weight='bold') - - glc_fluxes = [-sim.EX_glc__D_e for sim in results['fluxes']] - - # add arrow indicating growth regime - # 0. remove the box to improve readability of the text - acetate_ax.spines['top'].set_visible(False) - acetate_ax.spines['right'].set_visible(False) - - # 1. Find the start of the overflow regime (which is when acetate is being produced) - for i, ac in enumerate([sim.EX_ac_e for sim in results['fluxes']]): - if ac > 0.001: - glc_onset = glc_fluxes[i] - 1 - break - # 2. determine the dx covered by respiration - dx_respiration = glc_onset - glc_fluxes[0] - # 3 create respiration arrow - # forward arrow - acetate_ax.arrow( - glc_fluxes[0], 11, dx_respiration, 0, - linewidth=2, color='purple', label='Respiration', length_includes_head=True, head_width=3, head_length=0.5 - ) - # reverse arrow - acetate_ax.arrow( - glc_onset, 11, -dx_respiration, 0, head_starts_at_zero=True, - linewidth=2, color='purple', label='Respiration', length_includes_head=True, head_width=3, head_length=0.5 - ) - # annotate - acetate_ax.annotate('Respiration', - xy=(dx_respiration / 3, 15), - xytext=(10, -10), fontsize=fontsize, - textcoords='offset points', color='purple') - # remove the box - acetate_ax.spines['top'].set_visible(False) - acetate_ax.spines['right'].set_visible(False) - - # 4. create overflow arrow - # forward arrow - acetate_ax.arrow( - glc_onset, 11, 10 - glc_onset, 0, - linewidth=2, color='black', label='Overflow', length_includes_head=True, head_width=3, head_length=0.5 - ) - # reverse arrow - acetate_ax.arrow( - 10, 11, -(10 - glc_onset), 0, - linewidth=2, color='black', label='Overflow', length_includes_head=True, head_width=3, head_length=0.5 - ) - # annotate - acetate_ax.annotate('Overflow', fontsize=fontsize, - xy=((10 - glc_onset - 2) / 2 + glc_onset, 15), - xytext=(10, -10), - textcoords='offset points', color='black') - - # acetate graph - acetate_ax.plot([-sim.EX_glc__D_e for sim in results['fluxes']], [sim.EX_ac_e for sim in results['fluxes']], - linewidth=4, color='darkblue') - acetate_ax.tick_params(axis='y', labelsize=fontsize) - acetate_ax.set_xlim([0, 10.5]) - acetate_ax.set_ylim([-0.5, 15]) - acetate_ax.xaxis.set_visible(False) - if ylabels: - acetate_ax.set_ylabel(r'Acetate' '\n' '[$mmol_{ac}/g_{CDW}/h$]', fontsize=25, rotation=0, position=(1,-5)) - - # add phenotype data if this is given - if phenotype_data is not None: - acetate_ax.scatter(phenotype_data['EX_glc__D_e'], phenotype_data['EX_ac_e'], - color='purple', marker='o', s=40, linewidths=1.3, - facecolors=None, zorder=0, - label='Data') - - if title is not None: acetate_ax.set_title(title, fontsize=fontsize * 1.5) - - # CAC heatmap - im_csc = csc_ax.imshow(csc_matrix, aspect="auto", cmap=combined_cmap, norm=norm) - csc_ax.set_yticks(np.arange(len(x_csc)), labels=x_csc, fontsize=fontsize) - csc_ax.xaxis.set_visible(False) - if ylabels: - csc_ax.set_ylabel('CSC', fontsize=fontsize * 1.25) - - # Make line between CSC and ESC data more clear - axis = 'bottom' - csc_ax.spines[axis].set_linewidth(10) - csc_ax.spines[axis].set_color("black") - csc_ax.spines[axis].set_zorder(0) - - # ESC heatmap - im_esc = esc_ax.imshow(esc_matrix, aspect="auto", cmap=combined_cmap, norm=norm) - esc_ax.set_yticks(np.arange(len(x_esc)), labels=x_esc, fontsize=fontsize) - esc_ax.set_xticks(np.arange(len(yaxis)), labels=yaxis, fontsize=fontsize, rotation=45, ha='right') - if ylabels: - esc_ax.set_ylabel('ESC', fontsize=fontsize * 1.25) - if xlabels: - esc_ax.set_xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize=fontsize * 1.25) - - # colorbar - if cbar: - cbar_ax.xaxis.set_visible(False) - make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm, - vmin=vmin, vmax=vmax, fontsize=fontsize * 1.25) - fig.set_figwidth(24) - fig.set_figheight(7) - fig.align_labels() - return fig - - -# %% - -def make_scaled_colorbar(ax, fig, cmap, norm, vmin, vmax, - fontsize=16, cbarlabel='Sensitivity Coefficient'): - sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) - sm.set_array([]) - - cbar = fig.colorbar(sm, ax=ax, cax=ax, shrink=1, fraction=1) - - # Adjust the tick intervals - tick_locations = np.linspace(vmin, vmax, num=5) # Adjust num to the desired number of ticks - cbar.set_ticks(tick_locations) - cbar.set_ticklabels([f"{tick:.1f}" for tick in tick_locations]) # Optional: customize tick labels - - # Setting the fontsize of the colorbar - cbar.set_label(cbarlabel, fontsize=fontsize) - cbar.ax.tick_params(labelsize=fontsize) - cbar.ax.yaxis.get_offset_text().set(size=fontsize) - - -# %% -# adjust labels for better readibility -def adjust_heatmap_labels(labels): - new_labels = labels.copy() - - for i, label in enumerate(labels): - if 'EX_glc__D_e' in label or label[:-3] == 'EX_glc__D_e': - if label[-1] == 'B': - new_labels[i] = 'EX_glc_' + label[-2:] - else: - new_labels[i] = 'EX_glc_lb' - if label == 'TotalProteinConstraint_proteome': - new_labels[i] = 'Protein pool' - - if label == 'MembraneSector_membrane': - new_labels[i] = 'Membrane sector' - - if label[-1].isdigit() and len(label) > 3: # all enzyme ids start with a digit - rxn_ids = mcpam.get_reactions_with_enzyme_id(label) - - id = rxn_ids[0].split('_') - rxn_name = id[1] - new_labels[i] = '\n'.join([part for part in rxn_name.split(' ')]) - return new_labels - - -# %% -def find_nonzero_sensitivities(Cv, x_axis): - indices = [] - for row in Cv: - for index, coeff in enumerate(row): - if abs(coeff) > 0 and index not in indices: - indices.append(index) - - coeff_nonzero = [] - for row in Cv: - coeff_nonzero.append([coeff for i, coeff in enumerate(row) if i in indices]) - x_coeff_nonzero = [coeff for i, coeff in enumerate(x_axis) if i in indices] - - return coeff_nonzero, x_coeff_nonzero - - -# %% -def find_top5_sensitivities(Cv, x_axis, yaxis, threshold=0.01): - # top 5 enzymes per simulation - Cv_df = pd.DataFrame(Cv, columns=x_axis, index=yaxis) - largest = list() - for i, row in Cv_df.iterrows(): - top5 = abs(row).nlargest() - if top5.iloc[0]: - largest += [index for index, value in top5.items() if abs(value) > threshold] - # remove duplicates - largest_list = list(set(largest)) - - # extract non duplicate top5 enzymes - top5_df = Cv_df[largest_list].T.drop_duplicates().sort_index() - largest_list = top5_df.index.values - - top5_matrix = [list(row) for i, row in top5_df.iterrows()] - return top5_matrix, largest_list - -### PAM simulations -#### 3.1 Build PAModel - -pam, mcpam = run_pam_mcpam_core_with_optimized_kcats(type="full scale") - -#### 3.2 Run simulations for glucose uptake of 0-10 mmol/gcdw/h -# %% -results_pam = calculate_sensitivities(mcpam) -x_axis_csc_pam, x_axis_esc_pam = parse_x_axis_heatmap(results_pam['capacity coefficients'], - results_pam['enzyme coefficients']) -# %% -# get nonzero sensitivities -csc_nonzero_pam, x_csc_nonzero_pam = find_nonzero_sensitivities(results_pam['Ccsc'], x_axis=x_axis_csc_pam) -esc_nonzero_pam, x_esc_nonzero_pam = find_nonzero_sensitivities(results_pam['Cesc'], x_axis=x_axis_esc_pam) -csc_nonzero_pam_t = np.transpose(np.array(csc_nonzero_pam)) -esc_nonzero_pam_t = np.transpose(np.array(esc_nonzero_pam)) - -# get top5 nonzero sensitivities -csc_top5_pam, x_csc_top5_pam = find_top5_sensitivities(results_pam['Ccsc'], x_axis=x_axis_csc_pam, - yaxis=glc_uptake_rates) -esc_top5_pam, x_esc_top5_pam = find_top5_sensitivities(results_pam['Cesc'], x_axis=x_axis_esc_pam, - yaxis=glc_uptake_rates) -csc_top5_pam_t = np.transpose(np.array(csc_top5_pam)) -esc_top5_pam_t = np.transpose(np.array(esc_top5_pam)) - -### 4 Create plot - -#### 4.1 Load phenotypic data - -# load phenotype data from excel file -pt_data = pd.read_excel(os.path.join(DATA_DIR, 'Ecoli_phenotypes', 'Ecoli_phenotypes_py_rev.xls'), sheet_name='Yields', - index_col=None) -pt_data['EX_glc__D_e'] = -pt_data['EX_glc__D_e'] - -# create 2 plots: supplements and main text -fontsize = 28 -width = 50 -height = 10 -# select colormap -cmap = None # plt.cm.get_cmap('magma') - -# gridspec inside gridspec -fig = plt.figure(layout='constrained') - -gs0 = gridspec.GridSpec(1, 1, figure=fig) -gs_pam = gs0[0] - -fig_pam = make_heatmap_subfigure(results=results_pam, csc_matrix=csc_nonzero_pam_t, esc_matrix=esc_top5_pam, - ylabels=True, xlabels=True, x_csc=x_csc_nonzero_pam, x_esc=x_esc_top5_pam, - yaxis=glc_uptake_rates, fig=fig, grdspc=gs_pam, - phenotype_data=pt_data, fontsize=fontsize, cmap=cmap) -fig_pam.subplots_adjust(left=0.3) -# set common x axis title -# fig_pam.xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize = fontsize*1.25) - -# # add image -# -# ax_fig = fig.add_subplot(gs_figure) -# ax_fig.imshow(sensitivities_mapped) -# ax_fig.annotate('B', xy=(2, 1), xycoords='data', -# xytext=(-0.05, 1.30), textcoords='axes fraction', -# va='top', ha='left', fontsize=fontsize * 1.5, weight='bold') -# ax_fig.axis('off') -# ax_fig.set_xticks([]) -# ax_fig.set_yticks([]) - -plt.plasma() -fig.subplots_adjust(left=0.3) -fig.set_figwidth(width) -fig.set_figheight(height) -fig.align_labels() - -plt.show() - -# fig.savefig('Figures/Figure_sensitivities_pam_uniprotid_(2).png', dpi=200, bbox_inches='tight') - diff --git a/Scripts/sensitivity_analysis_thesis.py b/Scripts/sensitivity_analysis_thesis.py deleted file mode 100644 index c020f93..0000000 --- a/Scripts/sensitivity_analysis_thesis.py +++ /dev/null @@ -1,611 +0,0 @@ -from matplotlib import pyplot as plt -import matplotlib.gridspec as gridspec -import matplotlib.colors as mcolors -import seaborn as sns - -import pandas as pd -import os -import sys -import numpy as np - -sys.path.append('C:\\Users\\claud\\Documents\\iamb-student-folders\\iamb-folder-template\\mcPAM_package') - -from src.PAModelpy.configuration import Config - -if os.path.split(os.getcwd())[1] == 'Figures': - os.chdir(os.path.split(os.getcwd())[0]) -from Scripts.mcpam_generation_uniprot_id import set_up_ecolicore_mcpam, set_up_ecoli_mcpam - -Config.BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM' -DATA_DIR = os.path.join('Data') # os.path.join(os.path.split(os.getcwd())[0], 'Data') -glc_uptake_rates = list(np.linspace(1, 10, 10)) - -### 1 Useful functions -def calculate_sensitivities(pamodel): - glc_uptake_rates = np.linspace(1, 10, 10) - Ccsc = [] - Cesc = [] - y_axis = [] - fluxes = [] - - # disable pyruvate formate lyase (inhibited by oxygen) - pamodel.change_reaction_bounds(rxn_id='PFL', upper_bound=0) - - for glc in glc_uptake_rates: - print('glucose uptake rate ', glc, ' mmol/gcdw/h') - with pamodel: - # change glucose uptake rate - pamodel.change_reaction_bounds(rxn_id='EX_glc__D_e', - lower_bound=-glc, upper_bound=-glc) - # solve the model - sol_pam = pamodel.optimize() - fluxes.append(sol_pam.fluxes) - if pamodel.solver.status == 'optimal': y_axis += [glc] - # save data - Ccsc_new = list() - - if pamodel.solver.status == 'optimal': - capacity_sensitivity_coefficients = pamodel.capacity_sensitivity_coefficients - # for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: # for PAM without membrane - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector', 'membrane']: - Ccsc_new += capacity_sensitivity_coefficients[capacity_sensitivity_coefficients['constraint'] == csc].coefficient.to_list() - - Ccsc += [Ccsc_new] - - enzyme_coeff = pamodel.enzyme_sensitivity_coefficients - Cesc += [enzyme_coeff.coefficient.to_list()] - - print('Sum of capacity sensitivity coefficients: \t \t \t \t \t \t', round(sum(Ccsc_new), 6)) - print('Sum of enzyme sensitivity coefficients: \t \t \t \t \t \t', round(sum(Cesc[-1]), 6), '\n') - - return {'Ccsc': Ccsc, 'Cesc': Cesc, 'y_axis': y_axis, 'fluxes': fluxes, 'capacity coefficients': capacity_sensitivity_coefficients, - 'enzyme coefficients': enzyme_coeff} - - -# %% -def parse_x_axis_heatmap(capacity_coeff, enzyme_coeff): - x_axis_csc = [] - - # for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: # for PAM without membrane - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector', 'membrane']: - if csc == 'flux_ub' or csc == 'flux_lb': - x_axis_csc += [coef + '_' + csc for coef in - capacity_coeff[capacity_coeff['constraint'] == csc].rxn_id.to_list()] - else: - x_axis_csc += [coef + '_' + csc for coef in capacity_coeff[ - capacity_coeff['constraint'] == csc].enzyme_id.to_list()] - - x_axis_esc = enzyme_coeff.enzyme_id.to_list() - return x_axis_csc, x_axis_esc - - -# %% -def make_heatmap_subfigure(results, csc_matrix, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc, - ylabels=True, xlabels=False, cbar=True, title=None, fontsize=16, - vmin=-1.5, vmax=1.5, annotate=None, phenotype_data=None, cmap=None - # cmap = plt.cm.get_cmap('viridis') - ): - # fig = plt.figure() - if cmap is None: - # Create separate colormaps for positive and negative values and a color for zero - colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128)) - # colors_pos = plt.cm.PuOr(np.linspace(0.5, 1, 128))#plt.cm.Reds(np.linspace(0, 0.5, 128)) - # colors_pos = plt.cm.PuOr(np.linspace(0.5, 0, 64)) # Use part of the PuOr colormap - # colors_pos = np.vstack((colors_pos, plt.cm.PuOr(np.linspace(0.5, 1, 64)))) - # colors_pos = lighten_colormap(plt.cm.plasma)(np.linspace(0, 0.5, 64)) # Use part of the PuOr colormap - # colors_pos = np.vstack((colors_pos, plt.cm.plasma(np.linspace(0.5, 1, 64)))) - colors_pos = plt.cm.OrRd(np.linspace(0.1, 1, 128)) # plt.cm.Reds(np.linspace(0, 0.5, 128)) - - colors_zero = np.array([[1, 1, 1, 1]]) # gray for zero - - # Combine them into a single colormap - colors = np.vstack((colors_neg, colors_zero, colors_pos)) - combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap') - - # Create a norm that handles the zero color properly - bounds = np.linspace(vmin, vmax, len(colors)) - norm = mcolors.BoundaryNorm(bounds, combined_cmap.N) - - if cbar: - gs = gridspec.GridSpecFromSubplotSpec(3, 2, width_ratios=[len(yaxis), 1], - height_ratios=[1, len(x_csc), len(x_esc)], hspace=0, - subplot_spec=grdspc) - else: - gs = gridspec.GridSpecFromSubplotSpec(3, 1, width_ratios=[len(yaxis)], - height_ratios=[1, len(x_csc), len(x_esc)], hspace=0, - subplot_spec=grdspc) - - esc_ax = fig.add_subplot(gs[2, 0]) # ESC heatmap - acetate_ax = fig.add_subplot(gs[0, 0]) # acetate production - csc_ax = fig.add_subplot(gs[1, 0], sharex=esc_ax) # CSC heatmap - if cbar: - cbar_ax = fig.add_subplot(gs[1:, 1]) # colorbar - - # add annotation for subfigure (A or B) - if annotate is not None: - acetate_ax.annotate(annotate, xy=(2, 1), xycoords='data', - xytext=(-0.05, 1.5), textcoords='axes fraction', - va='top', ha='left', fontsize=fontsize * 1.5, weight='bold') - - glc_fluxes = [-sim.EX_glc__D_e for sim in results['fluxes']] - - # add arrow indicating growth regime - # 0. remove the box to improve readability of the text - acetate_ax.spines['top'].set_visible(False) - acetate_ax.spines['right'].set_visible(False) - - # 1. Find the start of the overflow regime (which is when acetate is being produced) - for i, ac in enumerate([sim.EX_ac_e for sim in results['fluxes']]): - if ac > 0.01: - glc_onset = glc_fluxes[i] - break - # 2. determine the dx covered by respiration - dx_respiration = glc_onset - glc_fluxes[0] - # 3 create respiration arrow - # forward arrow - acetate_ax.arrow( - glc_fluxes[0], 11, dx_respiration, 0, - linewidth=2, color='purple', label='Respiration', length_includes_head=True, head_width=3, head_length=0.5 - ) - # reverse arrow - acetate_ax.arrow( - glc_onset, 11, -dx_respiration, 0, head_starts_at_zero=True, - linewidth=2, color='purple', label='Respiration', length_includes_head=True, head_width=3, head_length=0.5 - ) - # annotate - acetate_ax.annotate('Respiration', - xy=(dx_respiration / 3, 15), - xytext=(10, -10), fontsize=fontsize, - textcoords='offset points', color='purple') - # remove the box - acetate_ax.spines['top'].set_visible(False) - acetate_ax.spines['right'].set_visible(False) - - # 4. create overflow arrow - # forward arrow - acetate_ax.arrow( - glc_onset, 11, 10 - glc_onset, 0, - linewidth=2, color='black', label='Overflow', length_includes_head=True, head_width=3, head_length=0.5 - ) - # reverse arrow - acetate_ax.arrow( - 10, 11, -(10 - glc_onset), 0, - linewidth=2, color='black', label='Overflow', length_includes_head=True, head_width=3, head_length=0.5 - ) - # annotate - acetate_ax.annotate('Overflow', fontsize=fontsize, - xy=((10 - glc_onset - 2) / 2 + glc_onset, 15), - xytext=(10, -10), - textcoords='offset points', color='black') - - # acetate graph - acetate_ax.plot([-sim.EX_glc__D_e for sim in results['fluxes']], [sim.EX_ac_e for sim in results['fluxes']], - linewidth=4, color='darkblue') - acetate_ax.tick_params(axis='y', labelsize=fontsize) - acetate_ax.set_xlim([0, 10.5]) - acetate_ax.set_ylim([-0.5, 15]) - acetate_ax.xaxis.set_visible(False) - if ylabels: - acetate_ax.set_ylabel(r'Acetate' '\n' '[$mmol_{ac}/g_{CDW}/h$]', fontsize=fontsize, rotation=0) - - # add phenotype data if this is given - if phenotype_data is not None: - acetate_ax.scatter(phenotype_data['EX_glc__D_e'], phenotype_data['EX_ac_e'], - color='purple', marker='o', s=40, linewidths=1.3, - facecolors=None, zorder=0, - label='Data') - - if title is not None: acetate_ax.set_title(title, fontsize=fontsize * 1.5) - - # CAC heatmap - im_csc = csc_ax.imshow(csc_matrix, aspect="auto", cmap=combined_cmap, norm=norm) - csc_ax.set_yticks(np.arange(len(x_csc)), labels=x_csc, fontsize=fontsize) - csc_ax.xaxis.set_visible(False) - if ylabels: - csc_ax.set_ylabel('CSC', fontsize=fontsize * 1.25) - - # Make line between CSC and ESC data more clear - axis = 'bottom' - csc_ax.spines[axis].set_linewidth(10) - csc_ax.spines[axis].set_color("black") - csc_ax.spines[axis].set_zorder(0) - - # ESC heatmap - im_esc = esc_ax.imshow(esc_matrix, aspect="auto", cmap=combined_cmap, norm=norm) - esc_ax.set_yticks(np.arange(len(x_esc)), labels=x_esc, fontsize=fontsize) - esc_ax.set_xticks(np.arange(len(yaxis)), labels=yaxis, fontsize=fontsize, rotation=45, ha='right') - if ylabels: - esc_ax.set_ylabel('ESC', fontsize=fontsize * 1.25) - if xlabels: - esc_ax.set_xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize=fontsize * 1.25) - - # colorbar - if cbar: - cbar_ax.xaxis.set_visible(False) - make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm, - vmin=vmin, vmax=vmax, fontsize=fontsize * 1.25) - fig.set_figwidth(24) - fig.set_figheight(7) - fig.align_labels() - return fig - -def make_heatmap_subfigure_acetate_csc(keys, results, csc_matrix, x_csc, x_esc, yaxis, fig, grdspc, - ylabels=True, xlabels=False, cbar=True, title=None, fontsize=16, - vmin=-1.5, vmax=1.5, annotate=None, phenotype_data=None, cmap=None - # cmap = plt.cm.get_cmap('viridis') - ): - - # adjust labels for better readibility - for key in keys: - x_csc[key] = adjust_heatmap_labels(x_csc_nonzero_pam[key]) - x_esc[key] = adjust_heatmap_labels(x_esc_top5_pam[key]) - - if cmap is None: - # Create separate colormaps for positive and negative values and a color for zero - colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128)) - colors_pos = plt.cm.OrRd(np.linspace(0.1, 1, 128)) # plt.cm.Reds(np.linspace(0, 0.5, 128)) - colors_zero = np.array([[1, 1, 1, 1]]) # gray for zero - - # Combine them into a single colormap - colors = np.vstack((colors_neg, colors_zero, colors_pos)) - combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap') - - # Create a norm that handles the zero color properly - bounds = np.linspace(vmin, vmax, len(colors)) - norm = mcolors.BoundaryNorm(bounds, combined_cmap.N) - - if cbar: - gs = gridspec.GridSpecFromSubplotSpec(4, 2, width_ratios=[len(yaxis), 0.2], - subplot_spec=grdspc) - else: - gs = gridspec.GridSpecFromSubplotSpec(4, 1, width_ratios=[len(yaxis)], - subplot_spec=grdspc) - - acetate_ax = fig.add_subplot(gs[0, 0]) # acetate production - csc_ax = {} - i = 1 - for area, csc in csc_matrix.items(): - csc_ax[area] = fig.add_subplot(gs[i, 0]) - i += 1 - - if cbar: - cbar_ax = fig.add_subplot(gs[1:, 1]) # colorbar - - results_for_plotting = list(results.values())[0] - glc_fluxes = [-sim.EX_glc__D_e for sim in results_for_plotting['fluxes']] - - # add arrow indicating growth regime - # 0. remove the box to improve readability of the text - acetate_ax.spines['top'].set_visible(False) - acetate_ax.spines['right'].set_visible(False) - - # Setting colormap to colorblind friendly color palette - sns.set_palette(("colorblind")) - - # acetate graph - i = 1 - for area, result in results.items(): - acetate_ax.plot([-sim.EX_glc__D_e for sim in result['fluxes']], [sim.EX_ac_e for sim in result['fluxes']], - linewidth=4, label=f'mcPAM {i}% area') - acetate_ax.tick_params(axis='y', labelsize=fontsize) - acetate_ax.set_xlim([0, 10.5]) - acetate_ax.set_ylim([-0.5, 10]) - acetate_ax.xaxis.set_visible(False) - i += 1 - - if ylabels: - acetate_ax.set_ylabel(r'Acetate' '\n' '[$mmol_{ac}/g_{CDW}/h$]', fontsize=fontsize, rotation=0) - - # add phenotype data if this is given - if phenotype_data is not None: - acetate_ax.scatter(phenotype_data['EX_glc__D_e'], phenotype_data['EX_ac_e'], - color='purple', marker='o', s=40, linewidths=1.3, - facecolors=None, zorder=0, - label='Data') - - handles, labels = acetate_ax.get_legend_handles_labels() - legend_ax = fig.add_subplot(gs[0, 1]) - legend_ax.axis("off") # Turn off axes for the legend display - legend_ax.legend(handles, labels, loc='center', fontsize=15) - - # acetate_ax.legend(fontsize=14, bbox_to_anchor=(1.05, 1)) - if title is not None: acetate_ax.set_title(title, fontsize=fontsize * 1.5) - - # CSC heatmap - i = 1 - for area, csc in csc_matrix.items(): - im_csc = csc_ax[area].imshow(csc, aspect="auto", cmap=combined_cmap, norm=norm) - csc_ax[area].set_yticks(np.arange(len(x_csc[area])), labels=x_csc[area], fontsize=20) - if i == 3: - csc_ax[area].set_xticks(np.arange(len(yaxis)), labels=yaxis, fontsize=fontsize, rotation=45, ha='right') - csc_ax[area].set_xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize=22) - else: - csc_ax[area].xaxis.set_visible(False) - if ylabels: - csc_ax[area].set_ylabel(f'{i}% area', fontsize=22) - i += 1 - - # Make line between the CSCs of different area clearer - axis = 'bottom' - csc_ax[area].spines[axis].set_linewidth(10) - csc_ax[area].spines[axis].set_color("black") - csc_ax[area].spines[axis].set_zorder(0) - - # colorbar - if cbar: - cbar_ax.xaxis.set_visible(False) - make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm, - vmin=vmin, vmax=vmax, fontsize=fontsize * 1.25) - fig.text(0.02, 0.5, 'CSC', rotation='vertical', fontsize=30, fontweight="bold") - fig.set_figwidth(24) - fig.set_figheight(7) - fig.align_labels() - return fig - -def make_heatmap_subfigure_esc(keys, results, esc_matrix, x_csc, x_esc, yaxis, fig, grdspc, - ylabels=True, xlabels=False, cbar=True, title=None, fontsize=16, - vmin=-1.5, vmax=1.5, annotate=None, phenotype_data=None, cmap=None - # cmap = plt.cm.get_cmap('viridis') - ): - - # adjust labels for better readibility - for key in keys: - x_csc[key] = adjust_heatmap_labels(x_csc_nonzero_pam[key]) - x_esc[key] = adjust_heatmap_labels(x_esc_top5_pam[key]) - - if cmap is None: - # Create separate colormaps for positive and negative values and a color for zero - colors_neg = plt.cm.Blues(np.linspace(1, 0.3, 128)) - colors_pos = plt.cm.OrRd(np.linspace(0.1, 1, 128)) # plt.cm.Reds(np.linspace(0, 0.5, 128)) - colors_zero = np.array([[1, 1, 1, 1]]) # gray for zero - - # Combine them into a single colormap - colors = np.vstack((colors_neg, colors_zero, colors_pos)) - combined_cmap = mcolors.ListedColormap(colors, name='custom_cmap') - - # Create a norm that handles the zero color properly - bounds = np.linspace(vmin, vmax, len(colors)) - norm = mcolors.BoundaryNorm(bounds, combined_cmap.N) - - if cbar: - gs = gridspec.GridSpecFromSubplotSpec(3, 2, width_ratios=[len(yaxis), 0.2], - height_ratios=[1, 1, 1.5], - subplot_spec=grdspc) - else: - gs = gridspec.GridSpecFromSubplotSpec(3, 1, width_ratios=[len(yaxis)], - height_ratios=[1, 1, 1.5], - subplot_spec=grdspc) - - esc_ax = {} - i = 0 - for area, esc in esc_matrix.items(): - esc_ax[area] = fig.add_subplot(gs[i, 0]) - i += 1 - - if cbar: - cbar_ax = fig.add_subplot(gs[:, 1]) # colorbar - - # ESC heatmap - i = 0 - for area, esc in esc_matrix.items(): - im_csc = esc_ax[area].imshow(esc, aspect="auto", cmap=combined_cmap, norm=norm) - esc_ax[area].set_yticks(np.arange(len(x_esc[area])), labels=x_esc[area], fontsize=20) - if i == 2: - esc_ax[area].set_xticks(np.arange(len(yaxis)), labels=yaxis, fontsize=fontsize, rotation=45, ha='right') - esc_ax[area].set_xlabel('Glucose uptake rate [$mmol_{glc}/g_{CDW}/h$]', fontsize=26) - else: - esc_ax[area].xaxis.set_visible(False) - if ylabels: - esc_ax[area].set_ylabel(f'{i+1}% area', fontsize=22, labelpad=20) - i += 1 - - # Make line between the CSCs of different area clearer - axis = 'bottom' - esc_ax[area].spines[axis].set_linewidth(10) - esc_ax[area].spines[axis].set_color("black") - esc_ax[area].spines[axis].set_zorder(0) - - # colorbar - if cbar: - cbar_ax.xaxis.set_visible(False) - make_scaled_colorbar(ax=cbar_ax, fig=fig, cmap=combined_cmap, norm=norm, - vmin=vmin, vmax=vmax, fontsize=fontsize * 1.25) - fig.text(0.02, 0.5, 'ESC', rotation='vertical', fontsize=30, fontweight="bold") - fig.subplots_adjust(left=0.2, right=0.9, top=0.9, bottom=0.2, wspace=0.3, hspace=0.3) - fig.set_figwidth(20) - fig.set_figheight(7) - fig.align_labels() - return fig - -def make_scaled_colorbar(ax, fig, cmap, norm, vmin, vmax, - fontsize=16, cbarlabel='Sensitivity Coefficient'): - sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) - sm.set_array([]) - - cbar = fig.colorbar(sm, ax=ax, cax=ax, shrink=0.2, fraction=1) - - # Adjust the tick intervals - tick_locations = np.linspace(vmin, vmax, num=5) # Adjust num to the desired number of ticks - cbar.set_ticks(tick_locations) - cbar.set_ticklabels([f"{tick:.1f}" for tick in tick_locations]) # Optional: customize tick labels - - # Setting the fontsize of the colorbar - cbar.set_label(cbarlabel, fontsize=fontsize) - cbar.ax.tick_params(labelsize=fontsize) - cbar.ax.yaxis.get_offset_text().set(size=fontsize) - - -# %% -# adjust labels for better readibility -def adjust_heatmap_labels(labels): - new_labels = labels.copy() - - for i, label in enumerate(labels): - if 'EX_glc__D_e' in label or label[:-3] == 'EX_glc__D_e': - if label[-1] == 'B': - new_labels[i] = 'EX_glc_' + label[-2:] - else: - new_labels[i] = 'Glucose uptake lb' - - if label == 'ATPM_flux_lb': - new_labels[i] = "ATP maintanance lb" - - if label == 'TotalProteinConstraint_proteome': - new_labels[i] = 'Protein pool' - - if label == 'MembraneSector_membrane': - new_labels[i] = 'Membrane sector' - - if label[-1].isdigit() and len(label) > 3: # all enzyme ids start with a digit - - rxn_ids = mcpam_core.get_reactions_with_enzyme_id(label) - id = rxn_ids[0].split('_') - rxn_name = id[1] - if rxn_name in new_labels: - new_labels[i] = rxn_name + f'*' - else: - new_labels[i] = '\n'.join([part for part in rxn_name.split(' ')]) - - # if label[-1].isdigit() and len(label) == 3: - # rxn_ids = mcpam_core.get_reactions_with_enzyme_id(label) - # rxn_name = mcpam_core.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0] - # new_labels[i] = '\n'.join([part for part in rxn_name.split(' ')]) - - # if len(rxn_ids)>2: - # new_labels[i] = pamodel.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0] - # else: - - # new_labels[i] = ',\n'.join([pamodel.reactions.get_by_id(rxn_ids[-1]).name.split('(')[0]for rxn in rxn_ids]) - return new_labels - - -# %% -def find_nonzero_sensitivities(Cv, x_axis): - indices = [] - for row in Cv: - for index, coeff in enumerate(row): - if abs(coeff) > 0 and index not in indices: - indices.append(index) - - coeff_nonzero = [] - for row in Cv: - coeff_nonzero.append([coeff for i, coeff in enumerate(row) if i in indices]) - x_coeff_nonzero = [coeff for i, coeff in enumerate(x_axis) if i in indices] - - return coeff_nonzero, x_coeff_nonzero - - -# %% -def find_top5_sensitivities(Cv, x_axis, yaxis, threshold=0.01): - # top 5 enzymes per simulation - Cv_df = pd.DataFrame(Cv, columns=x_axis, index=yaxis) - largest = list() - for i, row in Cv_df.iterrows(): - top5 = abs(row).nlargest() - if top5.iloc[0]: - largest += [index for index, value in top5.items() if abs(value) > threshold] - # remove duplicates - largest_list = list(set(largest)) - - # extract non duplicate top5 enzymes - top5_df = Cv_df[largest_list].T.drop_duplicates().sort_index() - largest_list = top5_df.index.values - - top5_matrix = [list(row) for i, row in top5_df.iterrows()] - return top5_matrix, largest_list - -### PAM simulations -#### 3.1 Build the mcPAModel -mcpam_core = set_up_ecolicore_mcpam() - -#### 3.2 Run simulations for glucose uptake of 0-10 mmol/gcdw/h for different available active enzymes area -results_pam = {} -x_axis_csc_pam = {} -x_axis_esc_pam = {} -max_area_list = np.linspace(0.011, 0.029, 3) -keys = [f'Sensitivity mcPAM with {area*100}% available area for active enzymes' for area in max_area_list] - -for area, key in zip(max_area_list, keys): - mcpam_core.sectors.get_by_id("MembraneSector").change_available_membrane_area(area, mcpam_core) - print(f'Starting simulation for mcPAM with {area*100}% available area for active enzymes') - results_pam[key] = calculate_sensitivities(mcpam_core) - -for result, area, key in zip(results_pam.values(), max_area_list, keys): - x_axis_csc_pam[key], x_axis_esc_pam[key] = parse_x_axis_heatmap(result['capacity coefficients'], - result['enzyme coefficients']) - -# get nonzero sensitivities -csc_nonzero_pam_t = {} -x_csc_nonzero_pam = {} -esc_nonzero_pam_t = {} -x_esc_nonzero_pam = {} - -for result, area, key in zip(results_pam.values(), max_area_list, keys): - csc_nonzero_pam, x_csc_nonzero_pam[key] = find_nonzero_sensitivities(results_pam[key]['Ccsc'], x_axis=x_axis_csc_pam[key]) - esc_nonzero_pam, x_esc_nonzero_pam[key] = find_nonzero_sensitivities(results_pam[key]['Cesc'], x_axis=x_axis_esc_pam[key]) - csc_nonzero_pam_t[key] = np.transpose(np.array(csc_nonzero_pam)) - esc_nonzero_pam_t[key] = np.transpose(np.array(esc_nonzero_pam)) - - -# get top5 nonzero sensitivities -csc_top5_pam = {} -x_csc_top5_pam = {} -esc_top5_pam = {} -x_esc_top5_pam = {} - -for result, area, key in zip(results_pam.values(), max_area_list, keys): - csc_top5_pam[key], x_csc_top5_pam[key] = find_top5_sensitivities(results_pam[key]['Ccsc'], x_axis=x_axis_csc_pam[key], - yaxis=glc_uptake_rates) - esc_top5_pam[key], x_esc_top5_pam[key] = find_top5_sensitivities(results_pam[key]['Cesc'], x_axis=x_axis_esc_pam[key], - yaxis=glc_uptake_rates) - # csc_top5_pam_t = np.transpose(np.array(csc_top5_pam)) - # esc_top5_pam_t = np.transpose(np.array(esc_top5_pam)) - -### 4 Create plot - -#### 4.1 Load phenotypic data - -# load phenotype data from excel file -pt_data = pd.read_excel(os.path.join(DATA_DIR, 'Ecoli_phenotypes', 'Ecoli_phenotypes_py_rev.xls'), sheet_name='Yields', - index_col=None) -pt_data['EX_glc__D_e'] = -pt_data['EX_glc__D_e'] - -# create 2 plots: supplements and main text -fontsize = 28 -width = 50 -height = 10 -# select colormap -cmap = None # plt.cm.get_cmap('magma') - -# gridspec inside gridspec -fig = plt.figure() - -gs0 = gridspec.GridSpec(1, 1, figure=fig) -gs_pam = gs0[0] - -# # adjust labels for better readibility -# x_csc_label_pam = adjust_heatmap_labels(x_csc_nonzero_pam) -# x_esc_label_pam = adjust_heatmap_labels(x_esc_top5_pam) - -# Make figure acetate and csc -fig.set_layout_engine(layout='constrained') -fig_pam = make_heatmap_subfigure_acetate_csc(keys=keys, results=results_pam, csc_matrix=csc_nonzero_pam_t, - ylabels=True, xlabels=True, x_csc=x_csc_nonzero_pam, x_esc=x_esc_top5_pam, - yaxis=glc_uptake_rates, fig=fig, grdspc=gs_pam, - phenotype_data=pt_data, fontsize=fontsize, cmap=cmap) - -# # Make figure esc -# fig_pam = make_heatmap_subfigure_esc(keys=keys, results=results_pam, esc_matrix=esc_top5_pam, -# ylabels=True, xlabels=True, x_csc=x_csc_nonzero_pam, x_esc=x_esc_top5_pam, -# yaxis=glc_uptake_rates, fig=fig, grdspc=gs_pam, -# phenotype_data=pt_data, fontsize=fontsize, cmap=cmap) - -plt.plasma() -fig.set_figwidth(width) -fig.set_figheight(height) -fig.align_labels() - -plt.show() - -# fig.savefig('Figures/Figure_sensitivities_pam_uniprotid_(2).png', dpi=200, bbox_inches='tight') - diff --git a/Scripts/toy_ec_pam.py b/Scripts/toy_ec_pam.py deleted file mode 100644 index fa3f691..0000000 --- a/Scripts/toy_ec_pam.py +++ /dev/null @@ -1,194 +0,0 @@ -from cobra import Configuration -from cobra import Model, Reaction, Metabolite - -import numpy as np - -#importing the tools from the PAModelpy package -from src.PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector -from src.PAModelpy.PAModel import PAModel -from src.PAModelpy.configuration import Config - -Config.BIOMASS_REACTION = 'R7' -Config.GLUCOSE_EXCHANGE_RXNID = 'R1' -Config.CO2_EXHANGE_RXNID = 'R8' -Config.ACETATE_EXCRETION_RXNID = 'R9' - -#need to have gurobipy installed - - -#global variables: -global metabolites, n, m, Etot -#global variables: -global metabolites, n, m, Etot -metabolites = ['Substrate', 'ATP', 'CO2', 'Precursor', 'Biomass', 'Byproduct', 'Intermediate'] -n = 9 -m = 7 -Etot = 0.6*1e-3 #will be adjusted in the model with 1e3 - -#functions: -def build_toy_gem(): - ''' - Rebuild the toymodel as in the MATLAB script. - sub int byp atp co2 pre bio -R1 = [ 1, 0, 0, 0, 0, 0, 0]; -R2 = [-1, 1, 0, 0, 1, 0, 0]; -R3 = [ 0, -1, 1, 1, 0, 0, 0]; -R3r= -R3; -R4 = [ 0, -1, 0, 2, 1, 0, 0]; -R5 = [ 0, -1, 0, 0, 0, 1, 0]; -R6 = [ 0, 0, 0, -1, 0, -1, 1]; -R7 = [ 0, 0, 0, 0, 0, 0, -1]; -R8 = [ 0, 0, 0, 0, -1, 0, 0]; -R9 = [ 0, 0, -1, 0, 0, 0, 0]; -S = [R1;R2;R3;R3r;R4;R5;R6;R7;R8;R9]'; - - :return: Cobrapy model instance as model - ''' - #set up model basics - model = Model('toy_model') - cobra_config = Configuration() - # cobra_config.solver = 'gurobi' - for i in range(1, n + 1): - rxn = Reaction('R' + str(i)) - lower_bound = 0 - upper_bound = 1e6 - #force flux through the system - if i == 1: - lower_bound = 1 - #reversible reactions 3, 5 and 9 - if i ==3 or i==5 or 1==9: - lower_bound = -1e6 - #constrain nutrient (substrate or byproduct) uptake rate - if i != 1 or i != 9: - upper_bound = 100 - else: - upper_bound = 10 - - rxn.lower_bound = lower_bound - rxn.upper_bound = upper_bound - model.add_reactions([rxn]) - - - # add metabolites to the reactions: - # R1: - r1 = model.reactions.get_by_id('R1') - r1.add_metabolites({Metabolite('Substrate'): 1}) - # R2: - r2 = model.reactions.get_by_id('R2') - r2.add_metabolites({Metabolite('Substrate'): -1, Metabolite('Intermediate'): 1, Metabolite('ATP'): 0.5}) - # R3: - r3 = model.reactions.get_by_id('R3') - r3.add_metabolites({Metabolite('Intermediate'): -1, Metabolite('Byproduct'):1, Metabolite('ATP'):1}) - # R4: - r4 = model.reactions.get_by_id('R4') - r4.add_metabolites({Metabolite('Intermediate'): -1, Metabolite('ATP'): 2, Metabolite('CO2'):1}) - # R5: - r5 = model.reactions.get_by_id('R5') - r5.add_metabolites({Metabolite('Intermediate'): -1, Metabolite('Precursor'): 1}) - # R6: - r6 = model.reactions.get_by_id('R6') - r6.add_metabolites({Metabolite('ATP'): -1, Metabolite('Precursor'): -1, Metabolite('Biomass'): 1}) - # Exchange reactions - # R7: - r7 = model.reactions.get_by_id('R7') - r7.add_metabolites({Metabolite('Biomass'): -1}) - # R8: - r8 = model.reactions.get_by_id('R8') - r8.add_metabolites({Metabolite('CO2'): -1}) - # R9: - r9 = model.reactions.get_by_id('R9') - r9.add_metabolites({Metabolite('Byproduct'): -1}) - - return model - -def build_active_enzyme_sector(Config): - kcat_fwd = [1, 0.5, 1, 1, 0.5 ,0.45, 1.5] # High turnover for exchange reactions - kcat_rev = [kcat for kcat in kcat_fwd] - protein2gene = {} - rxn2kcat = {} - for i in range(n-3): # all reactions have an enzyme, except excretion reactions - rxn_id = f'R{i+1}' - # 1e-6 to correct for the unit transformation in the model (meant to make the calculations preciser for different unit dimensions) - #dummy molmass like in MATLAB script - rxn2kcat = {**rxn2kcat, **{rxn_id: - {f'E{i+1}': - {'f': kcat_fwd[i]/(3600*1e-6), - 'b': kcat_rev[i]/(3600*1e-6), - 'molmass': 1e6, - 'protein_reaction_association':[[f'E{i+1}']]}}}} - protein2gene = {**protein2gene,**{f'E{i+1}':[[f'g{i+1}']]}} - - return ActiveEnzymeSector(rxn2protein = rxn2kcat, protein2gene = protein2gene, configuration=Config) - -def build_unused_protein_sector(Config): - return UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=Config) - -def build_translational_protein_sector(Config): - return TransEnzymeSector(id_list = ['R7'], tps_mu=[0.01*1e-3], tps_0=[0.01*1e-3], mol_mass= [1], configuration=Config) -def run_simulations(pamodel, substrate_rates): - substrate_axis = list() - Ccsc = list() - Cesc = list() - x_axis_csc = list() - mu_list = list() - - for substrate in substrate_rates: - pamodel.change_reaction_bounds(rxn_id='R1', - lower_bound=0, upper_bound=substrate) - pamodel.optimize() - if pamodel.solver.status == 'optimal' and model.objective.value>0: - print('Running simulations with ', substrate, 'mmol/g_cdw/h of substrate going into the system') - substrate_axis += [substrate] - mu_list += [pamodel.objective.value] - - Ccsc_new = list() - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - Ccsc_new += pamodel.capacity_sensitivity_coefficients[pamodel.capacity_sensitivity_coefficients['constraint'] == csc].coefficient.to_list() - Ccsc += [Ccsc_new] - - Cesc += [pamodel.enzyme_sensitivity_coefficients.coefficient.to_list()] - - print('Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t ', round(sum(Ccsc_new),6)) - print('Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t ', round(sum(Cesc[-1]), 6), '\n') - - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - if csc == 'flux_ub' or csc == 'flux_lb': - x_axis_csc += [rid +'_' + csc for rid in pamodel.capacity_sensitivity_coefficients[pamodel.capacity_sensitivity_coefficients['constraint'] == csc].rxn_id.to_list()] - else: - x_axis_csc += [rid +'_' + csc for rid in pamodel.capacity_sensitivity_coefficients[pamodel.capacity_sensitivity_coefficients['constraint'] == csc].enzyme_id.to_list()] - - x_axis_esc = pamodel.enzyme_sensitivity_coefficients.enzyme_id.to_list() - - return {'substrate_axis': substrate_axis, 'mu_list': mu_list, - 'Ccsc':Ccsc, 'Cesc':Cesc, - 'x_axis_csc': x_axis_csc,'x_axis_esc': x_axis_esc} - -def print_heatmap(xaxis, matrix, yaxis = None): - import plotly.express - - if yaxis is None: - yaxis = list() - for i in range(1, n + 1): - yaxis += [f'R{i}'] - fig = plotly.express.imshow(matrix, aspect="auto", - x = xaxis, y = yaxis, - labels = dict(x = 'sensitivity coefficients', y='substrate uptake')) - fig.show() - -if __name__ == "__main__": - model = build_toy_gem() - active_enzyme = build_active_enzyme_sector(Config) - unused_enzyme = build_unused_protein_sector(Config) - translation_enzyme = build_translational_protein_sector(Config) - pamodel = PAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme, - translational_sector = translation_enzyme, - unused_sector = unused_enzyme, p_tot=Etot, configuration=Config) - #optimize biomass formation - pamodel.objective={pamodel.reactions.get_by_id('R7') :1} - - substrate_rates = np.arange(1e-3, 1e-1, 1e-3) - simulation_results = run_simulations(pamodel, substrate_rates) - - - print_heatmap(simulation_results['x_axis_csc'], simulation_results['Ccsc'], yaxis=simulation_results['substrate_axis']) - print_heatmap(simulation_results['x_axis_esc'], simulation_results['Cesc'], yaxis=simulation_results['substrate_axis']) diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index 269cadc..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,20 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -BUILDDIR = build - -# Put it first so that "make" without argument is like "make help". -help: - @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) - -.PHONY: help Makefile - -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -%: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) \ No newline at end of file diff --git a/docs/PAModelpy_UML.svg b/docs/PAModelpy_UML.svg deleted file mode 100644 index 8e96599..0000000 --- a/docs/PAModelpy_UML.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - -cobra.Model- name : str- reactions : DictList- metabolites : DictList- genes : DictList- variables : dict- constraints : dictPAModelTOTAL_PROTEIN_CONSTRAINT_ID : str- p_tot : float- enzymes : DictList- catalytic_events : DictList- enzyme_variables : DictList- sectors : DictList- capacity_sensitivity_coefficients :                                            pd.DataFrame- enzyme_sensitivity_coefficients :                                             pd.DataFrame- configuration : Configuration+ add_enzymes(list[Enzyme])+ add_catalytic_events(list[CatalyticEvent])+ add_sectors(list[Sector])+ add_total_protein_constraint()+ change_reaction_bounds()+ change_kcat_value()+ determine_sensitivity_coefficients()+ pfba()<interface>Sector#__copy__#__deepcopy__<interface>EnzymeSector- id : str- id_list : list- mol_mass : dict- model : PAModel- slope : float- intercept : floatTransEnzymeSector- tps_0 : float- tps_mu : float+ set_slope()+ set_intercept()ActiveEnzymeSector- rxn2protein : dict- protein2gene : dict+ add(PAModel)
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UnusedEnzymeSector- ups_0 : float- ups_mu : float+ set_slope()+ set_intercept()CustomEnzymeSector- cps_0 : float- cps_mu : float+ set_slope()+ set_intercept()cobra.reaction<interface>cobra.ObjectEnzyme- id : str- rxn2kcat : dict- molmass : float- lower_bound : float- upper_bound : float- enzyme_variable : EnzymeVariable- catalytic_events : DictList- genes : DictList- transcripts : DictList- concentration+ add_genes+ change_kcat_valuesEnzymeVariable+ change_kcat_values- kcats : dict- molmass : float- catalytic_events : DictList- rxn_ids : list- reactions : DictList- variables : dict- concentration+ add_reactionsEnzymeComplex- enzymes : DictList- add_enzymesCatalyticEvent- id : str- rxn: Reacion- kcats : dict- enzymes : DictList- enzyme_variables : DictList- concentration+ add_enzymes+ remove_enzymes+ change_kcat_values
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 \ No newline at end of file diff --git a/docs/api_reference/CatalyticEvent.md b/docs/api_reference/CatalyticEvent.md deleted file mode 100644 index 029dad7..0000000 --- a/docs/api_reference/CatalyticEvent.md +++ /dev/null @@ -1,182 +0,0 @@ -# CatalyticEvent - -CatalyticEvent object which relates Reaction variables to the EnzymeVariable and Enzyme objects. -It contains multiple functions which enable easy mapping and handling of one Event of catalysis -(e.g. one conversion of substrate to product, can be catalyzed by multiple enzymes) - -## CatalyticEvent Objects - -```python -class CatalyticEvent(Object) -``` - -CatalyticEvent is a class for holding information regarding the -catalysis of a Reaction in a cobra.Model object. It serves as an interface -between the metabolic reaction and the associated enzyme constraints and variables. - -**Notes**: - - There are three different scenarios: - - Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object - - Isozymes: multiple enzymes independently associated with a single catalytic event - - Other: a single enzyme is associated with a single catalytic event - - -**Arguments**: - -- `kcats2enzymes` _dict_ - A dictionary with enzyme, kcat key, value pairs to connect the enzyme with the associated reaction. - The kcat is another dictionary with 'f' and 'b' for the forward and backward reactions, respectively. -- `id` _str, optional_ - The identifier to associate with this catalytic event (default None). -- `rxn_id` _str, optional_ - The reaction with which this catalytic event is associated. -- `name` _str, optional_ - A human-readable name for the reaction (default ""). - -#### kcat\_values - -```python -@property -def kcat_values() -``` - -returns a dictionary with kcat values and enzymes - -#### flux - -```python -@property -def flux() -> float -``` - -Get the flux value in the most recent solution. - -Flux is the primal value of the corresponding variable in the model. - -**Returns**: - -- `flux` _float_ - Flux is the primal value of the corresponding variable in the model. - - -**Warnings**: - - * Accessing reaction fluxes through a `Solution` object is the safer, - preferred, and only guaranteed to be correct way. You can see how to - do so easily in the examples. - * Reaction flux is retrieved from the currently defined - `self._model.solver`. The solver status is checked but there are no - guarantees that the current solver state is the one you are looking - for. - * If you modify the underlying model after an optimization, you will - retrieve the old optimization values. - - -**Raises**: - -- `RuntimeError` - If the underlying model was never optimized beforehand or the - reaction is not part of a model. -- `OptimizationError` - If the solver status is anything other than `optimal`. -- `AssertionError` - If the flux value is not within the bounds. - - -**Examples**: - - ``` - >>> from cobra.io import load_model - >>> model = load_model("textbook") - >>> solution = model.optimize() - >>> model.variables.PFK.flux - 7.477381962160283 - >>> solution.fluxes.PFK - 7.4773819621602833 - ``` - -#### concentration - -```python -@property -def concentration() -> float -``` - -Get the enzyme concentration value of the most recent solution. -The enzyme concentration equals the flux value. - -**Returns**: - -- `float` - Enzyme concentration in [mmol/gDW]. - -#### add\_enzymes - -```python -def add_enzymes(enzyme_kcat_dict: dict) -``` - -Add enzymes to the catalytic event and create bindings to the related model. -The enzymes in the enzyme_kcat_dict are individual isozymes. Enzyme complexes -should be added as an EnzymeComplex object with a single kcat value. - -**Arguments**: - -- `enzyme_kcat_dict` - Dict - A nested dictionary with enzyme, kcat key, value pairs to connect the - enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` - for the forward and backward reactions respectively. - -#### remove\_enzymes - -```python -def remove_enzymes(enzyme_list: list) -``` - -Remove enzymes from the catalytic event and remove the catalytic event from the -constraint expressions related to the enzyme. - -**Arguments**: - -- `enzyme_list` - List[Union[str, PAModelpy.Package.Enzyme]] - A list with PAModelpy.Package.Enzyme objects to be removed. If a list of identifiers (str) - is provided, the corresponding enzyme will be obtained from the CatalyticEvent.enzymes attribute. - -#### change\_kcat\_values - -```python -def change_kcat_values(enzyme_kcat_dict: dict) -``` - -changes kcat values for the enzyme variable - -**Arguments**: - -- `enzyme_kcat_dict` - nested Dict - A Dict with enzyme_id, kcat key, value pairs to connect the - enzyme with the associated reaction the kcat is another dict with 'f' and 'b' - for the forward and backward reactions respectively. - -#### \_\_copy\_\_ - -```python -def __copy__() -> "CatalyticEvent" -``` - -Copy the CatalyticEvent. - -**Returns**: - - CatalyticEvent: - A new CatalyticEvent that is a copy of the original CatalyticEvent. - -#### \_\_deepcopy\_\_ - -```python -def __deepcopy__(memo: dict) -> "CatalyticEvent" -``` - -Copy the CatalyticEvent with memo. - -**Arguments**: - -- `memo` _dict_ - Automatically passed parameter. - - -**Returns**: - - CatalyticEvent: - A new CatalyticEvent that is a copy of the original CatalyticEvent with memo. - diff --git a/docs/api_reference/Constraints.md b/docs/api_reference/Constraints.md deleted file mode 100644 index 126e3fb..0000000 --- a/docs/api_reference/Constraints.md +++ /dev/null @@ -1,21 +0,0 @@ ---- -sidebar_label: Constraints -title: Constraints ---- - -## Constraint Objects - -```python -class Constraint(Metabolite) -``` - -Class for information about a Constraint in a protein Sector. - -Constraint is a class for holding information similar to -a metabolite in a cobra.Reaction object. - -**Arguments**: - -- `id` _str_ - The identifier to associate with the constraint. -- `name` _str_ - A human-readable name. - diff --git a/docs/api_reference/Enzyme.md b/docs/api_reference/Enzyme.md deleted file mode 100644 index 57acbca..0000000 --- a/docs/api_reference/Enzyme.md +++ /dev/null @@ -1,530 +0,0 @@ -# Enzyme classes - -Classes related to enzymes: -- Enzyme: Constraints relating enzymes to reactions. Including upper and lower bound enzyme constraints -- EnzymeComplex: Constraints relating enzyme complexes to reactions. Including upper and lower bound enzyme constraints -- EnzymeVariable: Variable related to an enzyme. The value of this variable represent the concentration. - -## Enzyme Objects - -```python -class Enzyme(Object) -``` - -Upper level Enzyme object containing information about the enzyme and links to the EnzymeVariables for each reaction the enzyme catalyzes. - -**Arguments**: - - - id (str): Identifier for the enzyme (e.g., Uniprot ID). - - rxn2kcat (Dict): Dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, - e.g. `{'PGI': {'f': 30, 'b': 0.1}}` - - upper_bound (float): Upper bound for the enzyme variable (default 1000.0). - - lower_bound (float): Lower bound for the enzyme variable (default 0). - - name (str): Name of the enzyme (default None). - - molmass (float): Molar mass of the enzyme (default 3.947778784340140e04). - - -**Notes**: - - - This class is used to generate enzyme instances from kcat values and contains information about the forward as well as the backward catalysis. - - The enzyme is linked to individual cobra.Reaction variables with CatalyticEvent objects. - - There are two scenarios: - - Promiscuous enzymes: a single enzyme can catalyze multiple reactions. - - Other: a single enzyme catalyzes a single reaction. - -#### DEFAULT\_ENZYME\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - -#### kcat\_values - -```python -@property -def kcat_values() -``` - -Returns a dictionary with kcat values for each associated reaction. - -**Returns**: - -- `dict` - A dictionary containing kcat values for associated reactions. - -#### concentration - -```python -@property -def concentration(units: str = "mmol/gDW", - return_units: bool = False) -> float -``` - -Returns the enzyme's total concentration considering any associated reactions. - -**Arguments**: - -- `units` _str, optional_ - Units in which the concentration is calculated (default is 'mmol/gDW'), other option is 'g/gDW'. -- `return_units` _bool, optional_ - Determines whether the units should be returned as well. - - -**Returns**: - -- `float` - Enzyme concentration as a float. - -#### add\_catalytic\_event - -```python -def add_catalytic_event(ce: CatalyticEvent, kcats: Dict) -``` - -Adds a catalytic event associated with a reaction to an enzyme. - -**Arguments**: - -- `ce` _PAModelpy.Variables.CatalyticEvent_ - A CatalyticEvent object to which the enzyme should be added. -- `kcats` _dict_ - A dictionary containing direction and kcat key-value pairs. - - -**Returns**: - -- `NoneType` - None - -#### add\_genes - -```python -def add_genes(gene_list: list, - gene_length: list, - relation: str = 'OR') -> None -``` - -Add genes to the enzyme and the model related to the enzyme if applicable - -**Arguments**: - -- `gene_list` _list_ - A list of gene identifiers to be added. -- `gene_length` _list_ - A list of lengths corresponding to each gene. -- `relation` _str, optional_ - The relationship between genes in gene_list. - Defaults to 'OR'. Possible values: 'OR' or 'AND'. - - -**Raises**: - -- `ValueError` - If an invalid relation is provided. - - -**Notes**: - - If relation is 'OR', each gene in gene_list will be treated as coding for an individual isozyme - If relation is 'AND', all genes in gene_list will be treated as coding for peptides in an enzyme complex - -#### create\_catalytic\_event - -```python -def create_catalytic_event(rxn_id: str, kcats: Dict) -``` - -Creates enzyme variables that link to reactions. - -**Arguments**: - -- `rxn_id` _str_ - ID of the associated reaction in the model. -- `kcats` _Dict_ - A dictionary containing kcat values for the forward and backward reaction. - - -**Returns**: - -- `Variables.CatalyticEvent` - Enzyme variable object of type Variables.CatalyticEvent. - -#### create\_enzyme\_variable - -```python -def create_enzyme_variable() -``` - -Creates enzyme variables that link enzyme to reactions. - -#### change\_kcat\_values - -```python -def change_kcat_values(rxn2kcat: Dict) -``` - -Changes the kcat values for the enzyme and updates the enzyme variable (enzymatic reaction) accordingly. - -**Arguments**: - -- `rxn2kcat` _Dict_ - A dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, - e.g. `{'PGI': {'f': 30, 'b': 0.1}}` - -#### get\_kcat\_values - -```python -def get_kcat_values(rxn_ids: Union[str, list] = None) -> Dict -``` - -Returns the kcat values for a specific enzyme and all enzyme-associated reactions. - -**Arguments**: - -- `rxn_ids` _Union[str, list], optional_ - ID of the reactions for which the kcat values should be returned. It can be a single reaction ID (str) or a list of reaction IDs. - - -**Returns**: - -- `Dict` - A dictionary containing kcat values for the forward (f) and backward (b) reactions. - -#### remove\_catalytic\_event - -```python -def remove_catalytic_event(catalytic_event: Union[CatalyticEvent, str]) -``` - -Function to remove a catalytic event from an enzyme. - -**Arguments**: - -- `catalytic_event` _Union[CatalyticEvent, str]_ - CatalyticEvent or str, catalytic event or identifier to remove. - -#### \_\_copy\_\_ - -```python -def __copy__() -> "Enzyme" -``` - -Copy the enzyme variable. - -**Returns**: - -- `PAModelpy.Enzyme.Enzyme` - A new enzyme that is a copy of the original enzyme. - -#### \_\_deepcopy\_\_ - -```python -def __deepcopy__(memo: dict) -> "Enzyme" -``` - -Copy the enzyme variable with memo. - -**Arguments**: - -- `memo` _dict_ - Automatically passed parameter. - - -**Returns**: - -- `PAModelpy.Enzyme.Enzyme` - A new enzyme that is a copy of the original enzyme with memo. - -## EnzymeComplex Objects - -```python -class EnzymeComplex(Enzyme) -``` - -Upper-level EnzymeComplex object containing information about the enzymes in a complex -and a link to the enzyme variables (CatalyticEvents) for each reaction the enzyme complex catalyzes. - -This class is used to generate enzyme instances from kcat values and contains -information about the forward as well as the backward catalysis. - -**Arguments**: - -- `id` _str_ - Identifier for the enzyme complex (e.g., Uniprot ID). -- `enzymes` _DictList of cobra.core.Enzyme_ - Enzyme objects associated with the enzyme complex. -- `rxn2kcat` _Dict_ - Dictionary with reaction ID, kcat value pairs for the forward (f) and backward (b) reaction, - e.g. `{'PGI': {'f': 30, 'b': 0.1}}` -- `upper_bound` _float, optional_ - Upper bound for the enzyme variable (default 1000.0). -- `name` _str, optional_ - Name of the enzyme (default None). -- `molmass` _float, optional_ - Molar mass of the enzyme (default 3.947778784340140e04). - -#### DEFAULT\_ENZYME\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - -## EnzymeVariable Objects - -```python -class EnzymeVariable(Reaction) -``` - -EnzymeVariable is a class for holding information regarding the variable representing an enzyme in the model. -For each reaction, the enzyme variables are summarized in a CatalyticEvent. - -There are three different scenarios: -- Enzyme complex: multiple enzymes together are associated with an EnzymeComplex object. -- Isozymes: multiple enzymes independently associated with a single catalytic event. -- Other: a single enzyme is associated with a single catalytic event. - -**Arguments**: - -- `kcats2rxns` _Dict_ - A dictionary with reaction_id, kcat key, value pairs to connect the enzyme with the associated reaction. - The kcat is another dictionary with `f` and `b` for the forward and backward reactions, respectively. -- `id` _str, optional_ - The identifier to associate with this enzyme (default None). -- `name` _str, optional_ - A human-readable name for the enzyme (default ""). -- `subsystem` _str, optional_ - Subsystem where the enzyme is meant to function (default ""). -- `lower_bound` _float_ - The lower flux bound (default 0.0). -- `upper_bound` _float, optional_ - The upper flux bound (default None). -- `**kwargs` - Additional keyword arguments are passed on to the parent class. - -#### DEFAULT\_ENZYME\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - -#### kcat\_values - -```python -@property -def kcat_values() -``` - -Returns a dictionary with kcat values and reactions. - -**Returns**: - -- `dict` - A dictionary containing kcat values and their associated reactions. - -#### flux - -```python -@property -def flux() -> float -``` - -Get the flux value in the most recent solution. - -Flux is the primal value of the corresponding variable in the model. - -**Returns**: - -- `float` - Flux, which is the primal value of the corresponding variable in the model. - - -**Raises**: - -- `RuntimeError` - If the underlying model was never optimized beforehand or the reaction is not part of a model. -- `OptimizationError` - If the solver status is anything other than 'optimal'. -- `AssertionError` - If the flux value is not within the bounds. - - -**Warnings**: - - - Accessing reaction fluxes through a `Solution` object is the safer, preferred, and only guaranteed to be correct way. - - Reaction flux is retrieved from the currently defined `self._model.solver`. The solver status is checked but there are no guarantees that the current solver state is the one you are looking for. - - If you modify the underlying model after an optimization, you will retrieve the old optimization values. - - -**Examples**: - - ``` - >>> from cobra.io import load_model - >>> model = load_model("textbook") - >>> solution = model.optimize() - >>> model.variables.PFK.flux - 7.477381962160283 - >>> solution.fluxes.PFK - 7.4773819621602833 - ``` - -#### concentration - -```python -@property -def concentration() -> float -``` - -Get the enzyme concentration value of the most recent solution. - -The enzyme concentration equals the flux value. - -**Returns**: - -- `float` - Enzyme concentration in mmol/gDW. - -#### concentration - -```python -@concentration.setter -def concentration(conc: Union[float, int]) -> None -``` - -Sets the concentration of the enzyme by creating or updating a constraint -that enforces the concentration to be equal to the sum of the forward and reverse -variable primals. - -**Arguments**: - - conc : float, int - The concentration value to be set for the enzyme. This value will be used - as both the lower and upper bound for the constraint, effectively fixing the - concentration to this value. - - Notes - ----- - - If a concentration constraint for the enzyme does not already exist in the model, - this function creates a new constraint named '<enzyme_id>_conc'. - - The concentration constraint is defined as: - concentration = forward_variable.primal + reverse_variable.primal - - If the constraint already exists, the linear coefficients for the forward and reverse - variables are updated to ensure the constraint remains valid. - - Raises - ------ - ValueError - If `conc` is not a valid numerical value. - -#### set\_forward\_concentration - -```python -def set_forward_concentration(conc: Union[float, int]) -> None -``` - -Sets the concentration of the enzyme by creating or updating a constraint -that enforces the concentration to be equal to the sum of only the forward -variable primals. This forces a reaction to be active in the forward direction. -It used the concentration setter functionality and subsequently sets the -coefficient for the reverse variable in the constraint to 0. - -**Arguments**: - - conc : float, int - The concentration value to be set for the enzyme. This value will be used - as both the lower and upper bound for the constraint, effectively fixing the - concentration to this value. - - Notes - ----- - - If a concentration constraint for the enzyme does not already exist in the model, - this function creates a new constraint named '<enzyme_id>_conc'. - - The concentration constraint is defined as: - concentration = forward_variable.primal - - Raises - ------ - ValueError - If `conc` is not a valid numerical value. - -#### set\_reverse\_concentration - -```python -def set_reverse_concentration(conc: Union[float, int]) -> None -``` - -Sets the concentration of the enzyme by creating or updating a constraint -that enforces the concentration to be equal to the sum of only the reverse -variable primals. This forces a reaction to be active in the reverse direction. -It used the concentration setter functionality and subsequently sets the -coefficient for the forward variable in the constraint to 0. - -**Arguments**: - - conc : float, int - The concentration value to be set for the enzyme. This value will be used - as both the lower and upper bound for the constraint, effectively fixing the - concentration to this value. - - Notes - ----- - - If a concentration constraint for the enzyme does not already exist in the model, - this function creates a new constraint named '<enzyme_id>_conc'. - - The concentration constraint is defined as: - concentration = reverse_variable.primal - - Raises - ------ - ValueError - If `conc` is not a valid numerical value. - -#### add\_catalytic\_events - -```python -def add_catalytic_events(catalytic_events: list, kcats: list) -``` - -Adding a catalytic event to an enzyme variable - -**Arguments**: - -- `catalytic_events` _list_ - A list of catalytic events to add. -- `kcats` _list_ - A list with dictionaries containing direction and kcat key-value pairs. - -#### add\_reactions - -```python -def add_reactions(reaction_kcat_dict: dict) -``` - -Add reactions to the enzyme variable and create bindings to the related model. -If there are multiple reactions related to a single enzyme, this is an isozyme. - -**Arguments**: - -- `reaction_kcat_dict` _dict_ - A nested dictionary with the reaction_id, kcat key, value pairs to connect the - enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` for the forward and - backward reactions, respectively. - -#### remove\_catalytic\_event - -```python -def remove_catalytic_event(catalytic_event: Union[CatalyticEvent, str]) -``` - -Remove a catalytic event from an enzyme. - -**Arguments**: - -- `catalytic_event` _Union[CatalyticEvent, str]_ - CatalyticEvent or str, catalytic event or identifier to remove. - -#### remove\_reactions - -```python -def remove_reactions(reaction_list: list) -``` - -Remove reactions from the enzyme variable and remove the reactions from the -constraint expressions related to the enzyme. - -**Arguments**: - -- `reaction_list` _list_ - A list with Cobra.Reaction objects which should be removed. If a list of identifiers (str) - is provided, the corresponding enzyme will be obtained from the EnzymeVariables.reaction attribute. - -#### change\_kcat\_values - -```python -def change_kcat_values(reaction_kcat_dict: dict) -``` - -Changes kcat values for the enzyme variable. - -**Arguments**: - -- `reaction_kcat_dict` _dict_ - A nested dictionary with Cobra.Reaction, kcat key, value pairs to connect the - enzyme with the associated reaction. The kcat is another dictionary with `f` and `b` for the forward and - backward reactions, respectively. - -#### \_\_copy\_\_ - -```python -def __copy__() -> "PAModelpy.Enzyme.EnzymeVariable" -``` - -Copy the enzyme variable. - -**Returns**: - -- `PAModelpy.Enzyme.EnzymeVariable` - A new enzyme variable that is a copy of the original enzyme variable. - -#### \_\_deepcopy\_\_ - -```python -def __deepcopy__(memo: dict) -> "PAModelpy.Enzyme.EnzymeVariable" -``` - -Copy the enzyme variable with memo. - -**Arguments**: - -- `memo` _dict_ - Automatically passed parameter. - - -**Returns**: - -- `PAModelpy.Enzyme.EnzymeVariable` - A new enzyme variable that is a copy of the original enzyme variable with memo. - diff --git a/docs/api_reference/EnzymeSectors.md b/docs/api_reference/EnzymeSectors.md deleted file mode 100644 index ed7d89c..0000000 --- a/docs/api_reference/EnzymeSectors.md +++ /dev/null @@ -1,175 +0,0 @@ -# EnzymeSectors - -## Sector Objects - -```python -class Sector(Object) -``` - -#### \_\_copy\_\_ - -```python -def __copy__() -> "Sector" -``` - -Copy the Sector. - -**Returns**: - -- `Sector` - A new Sector that is a copy of the original Sector. - -#### \_\_deepcopy\_\_ - -```python -def __deepcopy__(memo: dict) -> "Sector" -``` - -Copy the Sector with memo. - -**Arguments**: - -- `memo` _dict_ - Automatically passed parameter. - - -**Returns**: - -- `Sector` - A new Sector that is a copy of the original Sector with memo. - -## EnzymeSector Objects - -```python -class EnzymeSector(Sector) -``` - -#### DEFAULT\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - -## ActiveEnzymeSector Objects - -```python -class ActiveEnzymeSector(Sector) -``` - -#### DEFAULT\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - -#### \_\_init\_\_ - -```python -def __init__(rxn2protein: dict, - protein2gene: dict = {}, - configuration: Config = Config) -``` - -_summary_ - -**Arguments**: - -- `rxn2protein` _dict_ - A dictionary with reaction ID, enzymes_dict key, value pairs for each reaction in the active_enzyme sector. - The enzymes_dict contains the enzyme identifiers of the enzymes related to the specific reaction as keys, and a dict - with information about the enzyme as values. The information included in this dict includes the turnover number for - the forward and backward reaction (1/s), molar mass of the enzyme (mol/g), gene identifiers related to the enzyme, - and with which other enzymes it forms a complex. -- `protein2gene` - dict - enzyme_id, gene_list key, value pairs for each enzyme.The gene_list value is a list of lists which indicates - 'and' or 'or' relationships between the genes which code for the enzyme(complex). - - -**Example**: - - ``` - For the Parameter rxn2protein a dictionary may look like this: - { - 'R1': - {E1: - {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': [G1, G2], - 'protein_reaction_association': [[E1, E2]] - }, - E2: - {'f': forward kcat, 'b': backward kcat, 'molmass': molar mass, 'genes': [G3, G4], - 'protein_reaction_association': [[E1, E2]] - } - } - - For the Parameter protein2gene a dictionary may look like this: - {E1: - [[gene1], [gene2, gene3]], - E2: - [[gene4]] - } - where the gene-protein-reaction associations are the following: - E1: gene1 or (gene2 and gene3) - E2: gene4 - ``` - -**Arguments**: - - rxn2protein : nested dict - reaction id, enzymes_dict key, value pairs for each reaction in the active_enzyme sector. - The enzymes_dict contains the enzyme identifiers of the enzymes which are related to the specific reaction - as keys and a dict with information about the enzyme as values. The information included in this dict is: - turnover number for forward and backward reaction [1/s], molar mass of the enzyme [mol/g], gene identifiers - related to the enzyme, with which other enzymes it forms a complex. - -- `protein2gene` - dict - enzyme_id, gene_list key, value pairs for each enzyme.The gene_list value is a list of lists which indicates - 'and' or 'or' relationships between the genes which code for the enzyme(complex). - -- `configuration` - Config object, optional - Information about general configuration of the model including identifier conventions. - Default as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. - -#### check\_kcat\_values - -```python -def check_kcat_values(model, reaction, enzyme_dict) -``` - -Function to check if the kcat values provided for an enzyme are consistent with the direction of the reaction - -**Arguments**: - -- `model` _cobra.Model or PAModel_ - Model to which the kcat values should be added. -- `reaction` _cobra.Reaction_ - Reaction that is catalyzed with the enzyme related to the kcats. -- `enzyme_dict` _dict_ - A dictionary with the turnover values for the forward and/or backward reaction for different enzymes [/s]. - - -**Example**: - - Example dictionary for the `enzyme_dict` parameter - ``` - {'E1': {'f': forward kcat, 'b': backward kcat}} - ``` - -## TransEnzymeSector Objects - -```python -class TransEnzymeSector(EnzymeSector) -``` - -#### DEFAULT\_MOL\_MASS - -default E. coli ribosome molar mass [g/mol] - -## UnusedEnzymeSector Objects - -```python -class UnusedEnzymeSector(EnzymeSector) -``` - -#### DEFAULT\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - -## CustomSector Objects - -```python -class CustomSector(EnzymeSector) -``` - -#### DEFAULT\_ENZYME\_MOL\_MASS - -mean enzymes mass E.coli [g/mol] - diff --git a/docs/api_reference/PAMValidator.md b/docs/api_reference/PAMValidator.md deleted file mode 100644 index f5cbfe4..0000000 --- a/docs/api_reference/PAMValidator.md +++ /dev/null @@ -1,93 +0,0 @@ -# PAMValidator - -## PAMValidator Objects - -```python -class PAMValidator(object) -``` - -#### MW\_GLC - -g/mol - -#### GRADIENT\_MAX - -mmol/gdw/h - -#### GRADIENT\_STEP - -mmol/gdw/h - -#### GRADIENT\_MIN - -mmol/gdw/h - -#### run\_simulations\_glc\_o2\_gradient - -```python -def run_simulations_glc_o2_gradient( - oxygen_gradient: list, - params_to_save: Union[str, list] = "R_TranslationalProteinSector") -``` - -Function to run simulations of different oxygen gradients for a range of growth rates. - -This will simulate growth for the entire range of glucose concentrations for each oxygen uptake rate as given by the input. - -**Arguments**: - -- `oxygen_gradient` _list_ - List of upper bounds for the oxygen uptake reaction to loop over. -- `params_to_save` _optional_ - string or list, which parameter(s) to save for further analysis (default: translational protein sector constraint). - - -**Returns**: - -- `results` _list of dataframes_ - Saves the growth rate, glucose uptake rate, and the user-defined parameters for each oxygen uptake rate in separate dataframes. - -#### run\_simulations\_ups - -```python -def run_simulations_ups( - ups_gradient: list, - params_to_save: Union[str, list] = "R_TranslationalProteinSector") -``` - -Function to run simulations with increasing unused enzyme sectors proportions for a range of growth rates. - -This will simulate growth for the entire range of glucose concentrations for a range of fractions of ups_0 as given by the input. - -**Arguments**: - -- `ups_gradient` _list_ - List of upper bounds for the oxygen uptake reaction to loop over. -- `params_to_save` _optional_ - string or list, which parameter(s) to save for further analysis (default: translational protein sector constraint). - - -**Returns**: - -- `results` _list of dataframes_ - Saves the growth rate, glucose uptake rate, and the user-defined parameters for each oxygen uptake rate in separate dataframes. - -#### custom\_plot - -```python -def custom_plot(rxn_ids: list, - valid_dataframe: pd.DataFrame = None, - xaxis: str = None, - c_uptake_rxn: str = GLUCOSE_EXCHANGE_RXNID) -``` - -Function to plot the results of custom reactions. - -**Arguments**: - -- `rxn_ids` _list of str_ - Reaction identifiers of the reactions to be plotted. -- `valid_dataframe` _pandas.DataFrame, optional_ - A DataFrame with experimental data to validate the results with. - The columns should be the same as the rxn_id of the reaction to be plotted and the reaction which should be plotted - on the x-axis (by default the glucose exchange reaction `EX_glc__D_e_b`). If the DataFrame is not provided, - only the simulation results will be plotted. -- `xaxis` _str, optional_ - The reaction identifier of the reaction which should be plotted on the x-axis (default: `EX_glc__D_e_b`). - - -**Returns**: - - Prints scatter plots of the model simulations vs. experimental data points (if provided). - diff --git a/docs/api_reference/PAModel.md b/docs/api_reference/PAModel.md deleted file mode 100644 index 4f7047a..0000000 --- a/docs/api_reference/PAModel.md +++ /dev/null @@ -1,740 +0,0 @@ -# PAModel - -## PAModel Objects - -```python -class PAModel(Model) -``` - -Class representation for a cobra model extended with enzyme kinetics as published in Alter et al. (2021). - -**Arguments**: - -- `id_or_model` _str or Model_ - String to use as model id, or actual model to base new model on. - If a string, it is used as input to load a model from. If a model, a new model object is instantiated with - the same properties as the original model (default None). -- `name` _str, optional_ - Human-readable string to be model description (default None). -- `p_tot` _float, optional_ - Total protein concentration (condition-dependent) (unit g_prot/g_cdw) (default 0.285). -- `senstitivity` _bool_ - Boolean value whether or not a sensitivity analysis should be performed during each simulation. - This sensitivity analysis will indicate to which extent individual constraints contribute to the objective value. - Enzyme sectors (EnzymeSector objects, optional): Information about the different enzyme sectors, including: - - Active_enzyme: Metabolic active proteins. - - Transl_enzyme: Enzymes related to translation. - - Unused_enzymes: Excess enzymes. - - Custom_enzymes (list): Custom enzyme sectors. -- `configuration` _Config object, optional_ - Information about the general configuration of the model, including - identifier conventions. Default as defined in the `PAModelpy.configuration` script for the E.coli iML1515 model. - - -**Attributes**: - -- `p_tot` _float_ - The fraction of biomass allocated to proteins (units: g_prot/g_cdw). -- `reactions` _DictList_ - A DictList where the key is the reaction identifier and the value is a Reaction. -- `metabolites` _DictList_ - A DictList where the key is the metabolite identifier and the value is a Metabolite. -- `genes` _DictList_ - A DictList where the key is the gene identifier and the value is a Gene. -- `name`0 _DictList_ - A DictList where the key is the group identifier and the value is a Group. -- `name`1 _DictList_ - A DictList where the key is the enzyme identifier and the value is an Enzyme. -- `name`2 _DictList_ - A DictList where the key is the enzyme variable identifier and the value is an EnzymeVariable. -- `name`3 _DictList_ - A DictList where the key is the catalytic event identifier and the value is a CatalyticEvent. -- `name`4 _dict_ - A dictionary containing sector-specific constraints. -- `name`5 _DictList_ - A DictList where the key is the sector identifier and the value is an EnzymeSector. - -#### P\_TOT\_DEFAULT - -g_protein/g_cdw - -#### \_\_init\_\_ - -```python -def __init__(id_or_model: Union[str, "Model", None] = None, - name: Optional[str] = None, - p_tot: Optional[float] = Config.P_TOT_DEFAULT, - sensitivity: bool = True, - active_sector: Optional[ActiveEnzymeSector] = None, - translational_sector: Optional[TransEnzymeSector] = None, - unused_sector: Optional[UnusedEnzymeSector] = None, - custom_sectors: Optional[CustomSector] = [None], - configuration=Config) -``` - -Constants - -#### add\_enzymes - -```python -def add_enzymes(enzyme_list: list) -> None -``` - -Add new enzymes to a model. -Adapted from Cobra.core.model.add_reactions and Cobra.core.model.add_metabolites. - -This function will add a DictList of enzymes to the model object and add new variables accordingly. -For each enzyme-associated reaction, a constraint in each direction is added to the model. -The change is reverted upon exit when using the model as a context. - -**Arguments**: - -- `enzyme_list` _list or Enzyme_ - A list of `Enzyme` objects. If it isn't an iterable container, the enzyme will - be placed into a list. - -#### add\_sectors - -```python -def add_sectors(sectors: List = None) -``` - -Adds sector variables to the model and adds these to the total protein constraint. - -**Arguments**: - -- `sectors` _list_ - A list of PAModelpy.EnzymeSectors to add to the model. - -#### add\_sector - -```python -def add_sector(sector) -``` - -Adds the sector variable for a specific sector to the model and adds this to the total protein constraint. -Also stores the sector variables in the model attributes. - -**Arguments**: - -- `sector` _PAModelpy.EnzymeSector_ - The specific EnzymeSector to add to the model. - -#### add\_catalytic\_events - -```python -def add_catalytic_events(catalytic_events: Optional[Iterable]) -``` - -Add a new CatalyticEvent to the model. -Will add a list of CatalyticEvent variables to the model object using the function defined in the CatalyticEvent object. - -**Arguments**: - -- `catalytic_events` _list or variables.CatalyticEvent_ - A list of `variables.CatalyticEvent` objects. If it isn't - an iterable container, the catalytic event will be placed into a list. - -#### add\_enzyme\_constraints - -```python -def add_enzyme_constraints(constraint_list: Optional[list]) -``` - -Add new enzyme constraints to a model. -Will add a list of constraints to the model object and add new constraints accordingly. -The change is reverted upon exit when using the model as a context. - -**Arguments**: - -- `constraint_list` _list, str, or constraints.Constraint_ - A list of `constraints.Constraint` objects. If it isn't - an iterable container, the constraint will be placed into a list. Also, a string with the constraint id - can be provided. A constraint will be created before adding it to the model. - -#### add\_sector\_constraints - -```python -def add_sector_constraints(constraint_list: Optional[list]) -``` - -Add a new constraint related to a sector to a model. -Will add a list of constraints to the model object and add new constraints accordingly. -The change is reverted upon exit when using the model as a context. - -**Arguments**: - -- `constraint_list` _list or constraints.Constraint_ - A list of `constraints.Constraint` objects. If it isn't an iterable - container, the constraint will be placed into a list. - -#### add\_total\_protein\_constraint - -```python -def add_total_protein_constraint(p_tot: Optional[float] = P_TOT_DEFAULT) -``` - -Function which adds the total protein constraint to the model. -This limits the amount of available enzymes and thus the resulting fluxes. - -**Notes**: - - The constraint expression looks like this: -- ``Etot` - sum(E) + E_translprot + E_unusedprot == p_tot - E_trsn_0 - E_ue_0` - - -**Arguments**: - -- `p_tot` _float, optional_ - Fraction of biomass which consists of protein (g_protein/g_cdw). - Default is 0.258 (E.coli). - -#### add\_reactions - -```python -def add_reactions(reaction_list: Iterable[Reaction]) -> None -``` - -Add reactions to the model. -This method is superimposed upon the cobra.Model.add_reactions() function. -As a new feature, it will add constraints to determine the lower and upper bound if a sensitivity analysis should -be performed (which is determined by the model attribute: PAModel.sensitivity). -Reactions with identifiers identical to a reaction already in the model are ignored. -The change is reverted upon exit when using the model as a context. - -**Arguments**: - -- `reaction_list` _list_ - A list of `cobra.Reaction` objects. - -#### add\_lb\_ub\_constraints - -```python -def add_lb_ub_constraints() -``` - -Makes additional constraints for the reaction lower bounds and upperbounds. -By adding these constraints the shadow prices of the reaction bounds can be -calculated and used in sensitivity analysis - -#### make\_lb\_ub\_constraint - -```python -@staticmethod -def make_lb_ub_constraint(m: Optional[Model], rxn: Reaction, - lower_bound: float, upper_bound: float) -``` - -Adding variables and constraints for the lower and upper bounds of a reaction to a model. -When solving the model, shadow prices for the lower and upper bounds will be calculated. -This allows for the calculation of sensitivity coefficients. The constraints are formulated as follows: - -**Notes**: - - Constraints are formulated as follows: - - `R_ub: R_fwd - R_rev <= UB` - - `R_lb: -(R_fwd - R_rev) <= -LB` - - -**Arguments**: - -- `m` _cobra.Model or PAModelpy.PAModel_ - The model to which the upper and lower bound constraints and variables - should be added. -- `rxn` _cobra.Reaction_ - The reaction for which upper and lower bound constraints should be generated. -- `lower_bound` _float_ - The value of the lower bound. -- `upper_bound` _float_ - The value of the upper bound. - - -**Returns**: - -- `m` _cobra.Model or PAModelpy.PAModel_ - The model with additional constraints and variables for the reactions. - -#### make\_enzyme\_min\_max\_constraint - -```python -@staticmethod -def make_enzyme_min_max_constraint(m: Optional[Model], enz: Enzyme, - lower_bound: float, upper_bound: float) -``` - -Adding variables and constraints for the lower and upperbounds of an Enzyme to a model. -When solving the model, shadow prices for the lower and upperbounds will be calculated. -This allows for the calculation of sensitivity coefficients. The constraints are formulated as follows: - -**Notes**: - - The constraints are formulated as follows: - - `enz_max : E <= Emax` - - `enz_min : -E <= -Emin` - - -**Arguments**: - -- `m` _cobra.Model or PAModelpy.PAModel_ - The model to which the upper and lower bound constraints and variables - should be added. -- `rxn` _PAModelpy.Enzyme_ - The enzyme for which minimal and maximal concentration constraints should be generated. -- `lower_bound` _float_ - The value of the lower bound. -- `upper_bound` _float_ - The value of the upper bound. - - -**Returns**: - -- `m` _cobra.Model or PAModelpy.PAModel_ - The model with additional constraints and variables for the enzyme's - concentration. - -#### parse\_shadow\_prices - -```python -@staticmethod -def parse_shadow_prices(shadow_prices) -``` - -Parse the shadow prices to a DataFrame where each constraint corresponds to a row, and shadow prices and directions are columns. - -#### calculate\_csc - -```python -def calculate_csc(obj_value, mu, mu_ub, mu_lb, mu_ec_f, mu_ec_b) -``` - -Calculate the capacity sensitivity coefficient for all inequality constraints in the model. -The sum of all capacity sensitivity coefficients should equal 1 for growth maximization. - -Capacity Sensitivity Coefficient Calculation: -Capacity Sensitivity Coefficient = constraint_UB * shadowprice / obj_value - -**Arguments**: - -- `obj_value` _float_ - The objective value of the model. -- `mu` _DataFrame_ - Shadow prices for all constraints. -- `mu_ub` _DataFrame_ - Shadow prices for the reaction upper bound (UB) constraints. -- `mu_lb` _DataFrame_ - Shadow prices for the reaction lower bound (LB) constraints. -- `mu_ec_f` _DataFrame_ - Shadow prices for the constraints related to enzymatic catalysis of the forward reaction. -- `mu_ec_b` _DataFrame_ - Shadow prices for the constraints related to enzymatic catalysis of the backward reaction. - - Results will be saved in the self.capacity_sensitivity_coefficients attribute as a dataframe - - -**Arguments**: - -- `obj_value` - Float: optimal objective value, commonly maximal growth rate under specific conditions -- `mu` - DataFrame: shadowprices for all constraints -- `mu_ub` - DataFrame: Shadowprices for the reaction UB constraints -- `mu_lb` - DataFrame: Shadowprices for the reaction LB constraints -- `mu_ec_f` - DataFrame: Shadowprices for the constraint related to an enzymatic catalysis of the forward reaction -- `mu_ec_b` - DataFrame: Shadowprices for the constraint related to an enzymatic catalysis of the backward reaction - -#### calculate\_csc\_for\_molecule - -```python -def calculate_csc_for_molecule(molecule: Union[Enzyme], mu_min: pd.DataFrame, - mu_max: pd.DataFrame, obj_value: float, - constraint_type: str, - associated_reactions: str) -``` - -Calculate the capacity sensitivity coefficients (CSCs) for constraints related to a biomolecule, -such as enzymes. These coefficients reflect the effect of infitesmal changes in the constraint bounds -on the objective function. - -The coefficients and associated reactions will be saved in the capacity_sensitivity_coefficients dataframe. - -**Arguments**: - -- `enzyme:Enzyme` - enzyme object to calculate CSC for -- `mu_min` - DataFrame: Shadowprices for the constraint related to a lower bound/minimum -- `mu_max` - DataFrame: Shadowprices for the constraint related to an upper bound/maximum -- `obj_value` - float: optimal objective value, commonly maximal growth rate under specific conditions - -#### calculate\_enzyme\_csc - -```python -def calculate_enzyme_csc(enzyme: Enzyme, mu_ec_f: pd.DataFrame, - mu_ec_b: pd.DataFrame, obj_value: float) -``` - -Calculate the capacity sensitivity coefficients (CSCs) for constraints related to enzyme. These coefficients -reflect the effect of infitesmal changes in the constraint bounds on the objective function. The coefficients -and associated reactions will be saved in the capacity_sensitivity_coefficients dataframe. - -The function makes use of the abstracted function calculate_csc_for_molecule - -**Arguments**: - -- `enzyme:Enzyme` - enzyme object to calculate CSC for -- `mu_ec_f` - DataFrame: Shadowprices for the constraint related to an enzymatic catalysis of the forward reaction -- `mu_ec_b` - DataFrame: Shadowprices for the constraint related to an enzymatic catalysis of the backward reaction -- `obj_value` - float: optimal objective value, commonly maximal growth rate under specific conditions - -#### calculate\_esc - -```python -def calculate_esc(obj_value, mu_ec_f, mu_ec_b) -``` - -Calculate enzyme sensitivity coefficients for the enzyme variables using their primal values, -the objective value, and shadow prices according to the following relations: - -Enzyme Sensitivity Coefficient Calculation: -esc = enzyme_variable.primal * constraint.shadowprice / obj_value - -**Arguments**: - -- `obj_value` _float_ - The objective value from the most recent optimal solution. -- `mu_ec_f` _pd.DataFrame_ - Shadow prices for maximizing enzyme concentrations (forward variables). -- `mu_ec_b` _pd.DataFrame_ - Shadow prices for minimizing enzyme concentrations (reverse variables). - - -**Returns**: - - None - - Fills the `PAModel.enzyme_sensitivity_coefficients` dataframe with the calculated enzyme sensitivity coefficients. - -#### calculate\_sum\_of\_enzymes - -```python -def calculate_sum_of_enzymes() -``` - -Calculate the sum of all enzyme variables for a feasible solution. - -**Returns**: - -- `float` - The sum of all enzyme variables in milligrams per gram of cell dry weight per hour (mg/gCDW/h). - -#### change\_total\_protein\_constraint - -```python -def change_total_protein_constraint(p_tot) -``` - -Change the fraction of biomass that is allocated to active proteins. - -**Arguments**: - -- `p_tot` _float_ - The new proteome fraction in grams of protein per gram of cell dry weight (g_protein/g_cdw). - -#### change\_reaction\_bounds - -```python -def change_reaction_bounds(rxn_id: str, - lower_bound: float = None, - upper_bound: float = None) -``` - -Change the reaction bounds. If a sensitivity analysis is required, the bounds of the upper and lower bound -constraints are adjusted. - -**Arguments**: - -- `rxn_id` _str_ - The string representing the reaction identifier to change. -- `lower_bound` _float, optional_ - The new value for the lower bound of the reaction (default is None). -- `upper_bound` _float, optional_ - The new value for the upper bound of the reaction (default is None). - -#### get\_reaction\_bounds - -```python -def get_reaction_bounds(rxn_id: str) -> Tuple[Union[float, int]] -``` - -Get the reaction bounds. If there should be a sensitivity analysis, the bounds of the upper and lower bound -constraints returned - -**Arguments**: - -- `rxn_id` - str - string of reaction id to return - -#### change\_enzyme\_bounds - -```python -def change_enzyme_bounds(enzyme_id: str, - lower_bound: float = None, - upper_bound: float = None) -``` - -Change the enzyme bounds. If the model should be primed for performing a sensitivity analysis, -the upper bound of the minimum and maximum enzyme concentration constraints are adjusted. - -**Arguments**: - -- `enzyme_id` _str_ - The string representing the enzyme identifier to change. -- `lower_bound` _float, optional_ - The new value for the minimal enzyme concentration (default is None). -- `upper_bound` _float, optional_ - The new value for the maximal enzyme concentration (default is None). - -#### get\_enzymes\_with\_reaction\_id - -```python -def get_enzymes_with_reaction_id(rxn_id: str) -> DictList -``` - -Return Enzyme objects associated with the reaction identifier through CatalyticEvent objects. - -**Arguments**: - -- `rxn_id` _str_ - The reaction identifier. - - -**Returns**: - -- `DictList` - A DictList of Enzyme objects associated with the reaction. - -#### get\_reactions\_with\_enzyme\_id - -```python -def get_reactions_with_enzyme_id(enz_id: str) -``` - -Return a list of reaction identifiers associated with the enzyme identifier (EC number) through CatalyticEvent objects. - -**Arguments**: - -- `enz_id` _str_ - The enzyme identifier (EC number). - - -**Returns**: - -- `List[str]` - A list of reaction identifiers associated with the enzyme. - -#### change\_kcat\_value - -```python -def change_kcat_value(enzyme_id: str, kcats: dict) -``` - -Change the turnover number (kcat) of the enzyme for a specific reaction. - -**Arguments**: - -- `enzyme_id` _str_ - The enzyme identifier. -- `kcats` _dict_ - A dictionary with reaction identifiers as keys and kcat values as values. - Each kcat value should be a nested dictionary with `f` (forward) and `b` (backward) as keys, - and the corresponding kcat values as values. - - -**Example**: - - Example dictionary for the `kcat` parameter - ``` - {'R1': {'f': 10.0, 'b': 5.0}, 'R2': {'f': 7.0, 'b': 3.0}} - ``` - -#### remove\_enzymes - -```python -def remove_enzymes( - enzymes: Union[str, Enzyme, List[Union[str, Enzyme]]]) -> None -``` - -Remove enzymes from the model. - -**Arguments**: - -- `enzymes` _list, reaction, or str_ - A list with enzymes (`Enzyme`), or their IDs, to remove. - Enzymes will be placed in a list. Strings will be placed in a list - and used to find the enzymes in the model. - -**Notes**: - - The change is reverted upon exit when using the model as a context. - -#### remove\_enzyme\_reaction\_association - -```python -def remove_enzyme_reaction_association(enzyme: Union[str, Enzyme], - reaction: Union[str, Reaction]) -> None -``` - -Remove an enzyme-reaction association from the model. Adapted from the cobra.core.remove_reactions() function. -If the reaction is not catalyzed by any enzyme anymore, the reaction ub will become 0 - -**Arguments**: - - enzyme : Enzyme or str - An enzyme, or the enzyme id for which the association should be removed to remove. - reaction : Reaction or str - A reaction, or the reaction id for which the association should be removed to remove. - -#### remove\_reactions - -```python -def remove_reactions(reactions: Union[str, Reaction, List[Union[str, - Reaction]]], - remove_orphans: bool = False) -> None -``` - -Remove reactions from the model. Inherited from the cobrapy.core.remove_reactions() function. - -**Arguments**: - -- `reactions` _list, reaction, or str_ - A list with reactions (`cobra.Reaction`), or their IDs, to remove. - Reactions will be placed in a list. Strings will be placed in a list - and used to find the reactions in the model. -- `remove_orphans` _bool, optional_ - Remove orphaned genes and metabolites from the model as well (default False). - -**Notes**: - - The change is reverted upon exit when using the model as a context. Also removes associated CatalyticEvents if they exist. - -#### remove\_catalytic\_events - -```python -def remove_catalytic_events(catalytic_events: Union[ - str, CatalyticEvent, List[Union[str, CatalyticEvent]]], - remove_orphans: bool = False) -> None -``` - -Remove catalytic events from the model. - -**Arguments**: - -- `reactions` _list, reaction, or str_ - A list with reactions (`cobra.Reaction`), or their IDs, to remove. - Reactions will be placed in a list. Strings will be placed in a list - and used to find the reactions in the model. -- `remove_orphans` _bool, optional_ - Remove orphaned genes and metabolites from the model as well (default False). - - -**Notes**: - - The change is reverted upon exit when using the model as a context. - -#### remove\_sectors - -```python -def remove_sectors( - sectors: Union[ - str, - Sector, - ActiveEnzymeSector, - List[Union[str, Sector, ActiveEnzymeSector]], - ] -) -> None -``` - -Remove sections from the model. - -Also removes associated CatalyticEvents if they exist. - -**Arguments**: - -- `sectors` _list, sector, or str_ - A list with sector (`PAModelpy.Sector` or `PAModelpy.ActiveEnzymeSector`), - or their IDs, to remove. A single sector will be placed in a list. - Strings will be placed in a list and used to find the sector in the model. - -#### remove\_active\_enzymes\_sector - -```python -def remove_active_enzymes_sector(sector: ActiveEnzymeSector) -> None -``` - -Remove an active enzyme sector from the model. - -This function performs the following steps: -1. Removes all enzymes associated with the sector. -2. Removes all catalytic events associated with the sector. -3. If a total protein constraint exists, it removes this constraint. -4. Deletes the sector constraint from the model's easy lookup. -5. Removes the sector from the model and disconnects its link to the model. - -**Arguments**: - -- `sector` _ActiveEnzymeSector_ - The active enzyme sector to be removed. - - -**Returns**: - - None - -#### remove\_linear\_sector - -```python -def remove_linear_sector( - sector: Union[UnusedEnzymeSector, TransEnzymeSector, - CustomSector]) -> None -``` - -Remove a linear sector from the model. - -This function performs the following steps: -1. If a total protein constraint exists, it adjusts the constraint to remove the sector's contribution. -2. Removes the associated constraints and variables. -3. Deletes the sector constraint from the model's easy lookup. -4. Removes the sector from the model and disconnects its link to the model. - -**Arguments**: - -- `sector` _Union[UnusedEnzymeSector, TransEnzymeSector, CustomSector]_ - The linear sector to be removed. - - -**Returns**: - - None - -#### test - -```python -def test(glc_flux: Union[int, float] = 10) -``` - -Test the proteome allocation model. - -**Arguments**: - -- `glc_flux` _float, optional_ - The glucose flux which limits the growth rate (units: mmol_glc/g_cdw/h, default=10). - -#### pfba - -```python -def pfba(fraction_of_optimum: float = 1.0, - proteins: bool = False, - reactions: bool = True, - exclude: List["str"] = [], - objective: Union[Dict, "Objective", None] = None) -``` - -Perform pFBA (parsimonious Enzyme Usage Flux Balance Analysis) with a custom objective including: -- All reactions -- All proteins -- All proteins and all reactions. - -pFBA [1] adds the minimization of all fluxes to the objective of the model. This approach is motivated by the idea that high fluxes have a higher enzyme turnover, and since producing enzymes is costly, the cell will try to minimize overall flux while still maximizing the original objective function, e.g., the growth rate. - -**Arguments**: - -- `fraction_of_optimum` _float, optional_ - The fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than the maximal value times the `fraction_of_optimum` (default 1.0). -- `objective` _dict or cobra.Model.objective, optional_ - A desired objective to use during optimization in addition to the pFBA objective. Dictionaries (reaction as the key, coefficient as the value) can be used for linear objectives (default None). -- `proteins` _bool, optional_ - Determines whether to include enzyme variables in the pFBA objective. -- `reactions` _bool, optional_ - Determines whether to include reaction variables in the pFBA objective. -- `exclude` _list of reaction ids, optional_ - Reactions to exclude from the minimization objective. - - -**References**: - - - [1] Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., Palsson, B. O. (2010). Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology, 6, 390. doi:10.1038/msb.2010.47 - -#### reset\_objective - -```python -def reset_objective() -``` - -Reseting the objective to the standard biomass maximization objective after pFBA - -#### optimize - -```python -def optimize(objective_sense: Optional[str] = None, - raise_error: bool = False) -> "Solution" -``` - -Optimize the model using flux balance analysis. Inherits from the cobra.Model.optimize() function and performs a sensitivity analysis after optimization if this is desired (by setting the PAModel.sensitivity attribute to True). - -**Arguments**: - -- `objective_sense` _`{None, 'maximize', 'minimize'}`, optional_ - Whether fluxes should be maximized or minimized. In case of None, the previous direction is used (default None). -- `raise_error` _bool_ - If true, raise an OptimizationError if solver status is not optimal (default False). - - -**Returns**: - - Solution - - -**Notes**: - - Only the most commonly used parameters are presented here. Additional parameters for cobra.solver may be available and specified with the appropriate keyword argument. - -#### copy - -```python -def copy() -> "PAModel" -``` - -Provide a partial 'deepcopy' of the Model. - -Adjusted from cobra.Model.copy(). - -All the Metabolite, Gene, Reaction, Enzyme, EnzymeVariable, Sector, and CatalyticEvent objects are created anew but in a faster fashion than deepcopy. - -**Returns**: - -- `PAModelpy.PAModel` - A new model copy. - diff --git a/docs/api_reference/_category_.json b/docs/api_reference/_category_.json deleted file mode 100644 index 3c28986..0000000 --- a/docs/api_reference/_category_.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "position": 3, - "label": "API Reference", - "collapsible": true, - "collapsed": true, - "className": "red", - "link": { - "type": "generated-index", - "title": "API Reference" - } - } \ No newline at end of file diff --git a/docs/api_reference/configuration.md b/docs/api_reference/configuration.md deleted file mode 100644 index c135b1d..0000000 --- a/docs/api_reference/configuration.md +++ /dev/null @@ -1,33 +0,0 @@ -# Configuration - -## Config Objects - -```python -class Config() -``` - -Object with information about model defaults which are used throughout the package: -- TOTAL_PROTEIN_CONSTRAINT_ID: str, `TotalProteinConstraint` -- P_TOT_DEFAULT: float, 0.258 g_p/g_cdw -- CO2_EXHANGE_RXNID: str, `EX_co2_e` -- GLUCOSE_EXCHANGE_RXNID: str, `EX_glc__D_e` -- BIOMASS_REACTION: str, `BIOMASS_Ec_iML1515_core_75p37M` -- OXYGEN_UPTAKE_RXNID: str, `EX_o2_e` -- ACETATE_EXCRETION_RXNID: str, `EX_ac_e` -- PHYS_RXN_IDS: List of str, `[BIOMASS_REACTION, GLUCOSE_EXCHANGE_RXNID, ACETATE_EXCRETION_RXNID, CO2_EXHANGE_RXNID, OXYGEN_UPTAKE_RXNID, - 'PGI', 'G6PDH2r', 'EDA', 'CS', 'ICL', 'PPC', 'ME1', 'ME2']` - -Defaults are configured for the iML1515 E.coli model - -#### P\_TOT\_DEFAULT - -g_protein/g_cdw - -#### reset - -```python -def reset() -``` - -Reset the config object to the standard settings for E.coli iML1515. - diff --git a/docs/api_reference/sidebar.json b/docs/api_reference/sidebar.json deleted file mode 100644 index 42da33b..0000000 --- a/docs/api_reference/sidebar.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "items": [ - "docs/api_reference/CatalyticEvent", - "docs/api_reference/Constraints", - "docs/api_reference/Enzyme", - "docs/api_reference/EnzymeSectors", - "docs/api_reference/PAMValidator", - "docs/api_reference/PAModel", - "docs/api_reference/configuration" - ], - "label": "Reference", - "type": "category" -} \ No newline at end of file diff --git a/docs/assets/toy-model.png b/docs/assets/toy-model.png deleted file mode 100644 index b0a9224..0000000 Binary files a/docs/assets/toy-model.png and /dev/null differ diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 16fed4e..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,46 +0,0 @@ - -# Set the source directory for your Sphinx documentation files -source_suffix = ['.rst', '.md'] # If using Markdown files with MyST - -# -- Project information - -project = 'PAModelpy' -copyright = '2024, iAMB, RWTH Aachen University' -author = 'Samira van den Bogaard' - -release = '0.4.2' -version = '0.4.2' - -# -- General configuration - -extensions = [ - 'sphinx.ext.duration', - 'sphinx.ext.doctest', - 'sphinx.ext.autodoc', - 'sphinx.ext.autosummary', - 'sphinx.ext.intersphinx', - 'myst_parser', # Add this for Markdown support -] - -intersphinx_mapping = { - 'python': ('https://docs.python.org/3/', None), - 'sphinx': ('https://www.sphinx-doc.org/en/master/', None), -} -intersphinx_disabled_domains = ['std'] - -templates_path = ['_templates'] - -# -- Options for HTML output - -html_theme = 'sphinx_rtd_theme' - -# -- Options for EPUB output -epub_show_urls = 'footnote' - -# Enable specific MyST features -myst_enable_extensions = [ - "colon_fence", # Support for ::: directives - "deflist", # Support for definition lists - "html_admonition", # HTML-style admonitions - "html_image", # Use HTML-style tags -] \ No newline at end of file diff --git a/docs/example.md b/docs/example.md deleted file mode 100644 index 5820301..0000000 --- a/docs/example.md +++ /dev/null @@ -1,503 +0,0 @@ -# Example usage of PAModelpy - -## Example 1: setting up an *Escherichia coli* Protein Allocation model (PAM) -*Escherichia coli* (*E.coli*) is a commonly used model organism in Microbiology. When this microorganism is grown -on increasing glucose concentration, it shifts from a purely respiratory metabolism to a respiro-fermentative metabolic -phenotype. This phenomenon is called 'overflow metabolism'. Interestingly, overflow metabolism cannot be simulated using -normal genome-scale models without additional constraints. With properly parametrized protein-constrained models however, -we are able to simulate this metabolic phenotype. In this example, we'll set-up the *E.coli* PAM, and we'll study the -predicted metabolic phenotypes for a range of glucose uptake rates. - -For this entire tutorial, you'll need to load the following packages: - -```python -#importing the packages -import os -from cobra.io import read_sbml_model -import pandas as pd - -#load PAMpy modules -from PAModelpy.EnzymeSectors import ActiveEnzymeSector, TransEnzymeSector, UnusedEnzymeSector -from PAModelpy.PAModel import PAModel -from PAModelpy.PAMValidator import PAMValidator -from PAModelpy.configuration import Config - -from PAModelpy.utils import (merge_enzyme_complexes, parse_reaction2protein, _get_genes_for_proteins) -``` - - -### Step 1: Initiate the protein sectors -Each protein-allocation model has three sectors: active enzyme sector (enzymes catalyzing the metabolic reactions), -translational protein sectors (i.e. ribosomal proteins required for translation) and unused proteins (idle proteins -which help the cell adapt to new conditions). The total of these three sectors is limited by an upperbound. This -upperbound is determined by the sum of all non-maintenance enzymes, which is assumed to be constant for prokaryotes. -For examples on how to parametrize these sectors, refer to `Scripts/create_ecolicore_pam_inclUE.ipynb`. - -- **Important note**: This tutorial aims to teach you how to build a PAM from scratch, so that you can adapt the sectors -where needed and you get to understand the logic of the PAModelpy package. However, in the `utils` toolbox, there is a -dedicated method for building a pam with the following syntax: -```python -def set_up_pam(pam_info_file:str = '', - model:Union[str, cobra.Model] = 'Models/iML1515.xml', - config:Config = None, - total_protein: Union[bool, float] = True, - active_enzymes: bool = True, - translational_enzymes: bool = True, - unused_enzymes: bool = True, - sensitivity:bool = True, - enzyme_db:pd.DataFrame = None, - adjust_reaction_ids:bool = False) -> PAModel -``` - -You can call this function as follows: - -```python -from PAModelpy.utils import set_up_pam -import os - -pam = set_up_pam(os.path.join('Data', 'proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx'), - os.path.join('Models', 'iML1515.xml')) -``` -More information can be found in the :ref:`PAM_setup_guide` - -#### 1.1: Active enzyme sector -The active enzyme sector will be build using information about which enzymes catalyzes a specific reaction, -the turnover rate of the catalysis and the molar mass of the enzyme. In this example we'll use the parameters as -published by [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20), which can be found in the `Data` folder of the PAModelpy repository - -First, we'll define the paths we'll download the data - -```python -protein_sector_info_path = 'Data/proteinAllocationModel_iML1515_EnzymaticData_py_uniprot.xlsx' -active_enzyme_data = pd.read_excel(protein_sector_info_path, sheet_name='ActiveEnzymes')) -``` - -The data is now in a dataframe with the following columns: -` -rxn_id - gpr - gene - enzyme_id - molMass -` -We need to collect the data from this table and put it in the correct structure to be parsed into the ActiveEnzymeSector -object. The main input in this object is the rxn2protein dictionary, where all the information about protein-reaction -associations required to build the protein-reaction relations in the model. It has the following format: - -```json -{ - "R1": { - "E1": { - "f": "forward kcat", - "b": "backward kcat", - "molmass": "molar mass", - "protein_reaction_relation": [["E1"]] - }, - "E2": { - "f": "forward kcat", - "b": "backward kcat", - "molmass": "molar mass", - "protein_reaction_relation": [["E1"]] - } - } -} -``` -If you have a information about the gene-protein-reaction associations (e.g. 'AND'/'OR' relations between different -peptides/proteins for one or more reactions), this information can be added in the `protein_reaction_relation` entry -of the reaction2protein dictionary. This entry is a list of lists, in which each sublist represent one functional -enzyme (complex). This means if E1 and E2 catalyze the same reaction, the `protein_reaction_relation` becomes `[['E1','E2']]` -for an enzyme complex ('AND' relation), and `[['E1']['E2']]` for isozymes ('OR' relation). In this example we will use -the peptide ids as defined by [UniProt](https://www.uniprot.org/). The [paper introducting sEnz](https://doi.org/10.1093/bioinformatics/btae691) -uses a different system, based on EC numbers. How to build those PAMs can be found in the scripts associated to the -publication and in the `Script/pam_generation.py` file. Now we will use gene-protein-reaction relations obtained from a -genome-scale model and uniprot to include different enzyme relations. - -Fortunately, in the `utils` directory, there are functions available which help you parse the information to the -reaction2protein and protein2gene dictionaries. This also includes a gapfilling step which assigns a 'dummy' identifier to -each reaction which is not associated with an enzyme id. - -Before we start we have to ensure each row contains one 'catalytical unit', e.g. a single enzyme or enzyme complex which is -able to catalyze a reaction. This means that we have to parse the dataframe. For this, we can use some tools provided in the `utils` directory. - -```python -#load the genome-scale information -model = read_sbml_model(os.path.join('Models', 'iML1515.xml')) - -#get the mapping between the protein and genes -protein2gene, gene2protein = _get_genes_for_proteins(active_enzyme_data, model) -#parse the dataframe -active_enzyme_per_cu = merge_enzyme_complexes(active_enzyme_data, gene2protein) - -reaction2protein, protein2gpr = parse_reaction2protein(active_enzyme_data, model) - -#Use the mapping between reactions and proteins to generate the active enzymes sector -active_enzyme_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene) -``` - -#### 1.2: Translational protein sector -The translational sector requires less parameters. We only need to define the reaction to which this section is proportional, -(for example the biomass pseudoreaction or substrate uptake rate), defined by `id_list`, and the slope (`tps_mu`) and -intercept (`tps_0`) of this relation. - -```python -translational_info = pd.read_excel(protein_sector_info_path, sheet_name='Translational') -id_list = [translational_info[translational_info.Parameter == 'id_list'].loc[0,'Value']] -translation_enzyme_sector = TransEnzymeSector(id_list=id_list, - tps_0=[translational_info[translational_info.Parameter == 'tps_0'].loc[1,'Value']], - tps_mu=[translational_info[translational_info.Parameter == 'tps_mu'].loc[2,'Value']], - mol_mass=[translational_info[translational_info.Parameter == 'mol_mass'].loc[3,'Value']]) -``` - -#### 1.3 Unused enzyme sector -The unused enzyme sector is defined in a very similar way as the translational protein sector. We assume that this -section is absent when the microbe is growing at it's highest growth rate. We'll use this assumption to define the slope: -```python -unused_protein_info = pd.read_excel(pam_info_file, sheet_name='ExcessEnzymes') - -ups_0 = unused_protein_info[unused_protein_info.Parameter == 'ups_0'].loc[2,'Value'] -smax = unused_protein_info[unused_protein_info.Parameter == 's_max_uptake'].loc[1,'Value'] -id_list =[unused_protein_info[unused_protein_info.Parameter == 'id_list'].loc[0,'Value']] - - -unused_protein_sector = UnusedEnzymeSector(id_list=id_list, - ups_mu=[ups_0/smax], - ups_0=[ups_0], - mol_mass=[unused_protein_info[unused_protein_info.Parameter == 'mol_mass'].loc[3,'Value']]) -``` - -### Step 2: Building the model -Now we are ready to build the model! We'll need to determine a maximal protein concentration. Following [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20), -let's take 0.258 mmol/gcdw/h. -As a basis, we'll use the iML1515 model, created by [Monk et al. (2017)](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521705/). - -NB: for the more advanced users who want to run the sensitivity analysis, please ensure that the `sensitivity` argument -is set to `True`. If you are not interested in the sensitivity analysis, you can set `sensitivity` to `False`, which -will speed up the computation time. - -```python -#load the genome-scale information -model = read_sbml_model(os.path.join('Models', 'iML1515.xml')) - -#load the PAM with the genome-scale information and the information about the enzyme sectors -pamodel = PAModel(id_or_model=model, - p_tot=0.258, - active_sector=active_enzyme_sector, - translational_sector=translation_enzyme_sector, - unused_sector=unused_protein_sector, - sensitivity =True - ) -``` - -### Step 3: run and validate the model results -To see if the PAM is working we can run some dummy simulations. Also, the PAMValidator module has functions which -allow for easy visualization of the model predictions vs measured experimental data. - -```python -pamodel.test() -``` -This is a simulation with a glucose uptake rate set to 10 mmol/gcdw/h. -We can easily change to a different substrate uptake rate, e.g. 5 mmol/gcdw/h by putting that in as an function argument - -```python -pamodel.test(5) -``` -In the PAMValidator object, you can find functions to run simulations over a range of substrate uptake rates. -To initiate the PAMValidator, you need to provide the model and a path to an excel file with experimental data. -In this example,we'll use the experimental data which can be found in -`Data/Ecoli_phenotypes/Ecoli_phenotypes_py_rev.xls`. - -```python -from PAModelpy.PAMValidator import PAMValidator - -validator = PAMValidator(pamodel,'Data/Ecoli_phenotypes/Ecoli_phenotypes_py_rev.xls') -#model flux rates of biomass formation, acetate, CO2 and O2 vs glucose uptake rate for a range of growth rates -validator.validate_range() -``` - -Alternatively, you can run simulations in a good old-fashioned for-loop. For examples on how to do that look at the -jupyter notebooks in the `Figures` directory. - -### Step 4: interpreting the results -What does the result tell you? What is the predicted metabolic phenotype and how does this relate to the experimental -results. Did the model capture overflow metabolism? - -### Outlook -After this tutorial, you know how to apply PAModelpy to this very well-studied *E.coli* example. But how to address you're -specific issue with your specific microbe? In the next example we'll show you how to use the Config() object to give the -PAModel the right naming conventions for your specific microbe. Are you more interested in performing modifications to -the model, such as deleting or adding enzymes, changing kcats, changing enzymes upper- and lowerbounds? Then have a look -at the following jupyter notebook: `Examples/PAModel_example_script.ipynb`. Have fun! - -## Example 2: working with the *Escherichia coli* Protein Allocation model (PAM) -In Example 1, a detailed step-by-step guide for building a PAM is given. In this example we use methods available in PAModelpy to build a PAM -and give you an overview of things you can do with the PAM once it is build. - -### 1. Building the PAM using PAModelpy.utils -In the example above, you've learned that there are quite some steps required to build a PAM. However, in PAModelpy, there -are methods available which help you do this. For these methods, you need a specific way of structuring the information about -the enzymes sectors. This makes it easier to automate generation of PAMs. The details are discussed in the -[PAM_setup_guide](PAM_setup_guide.md). For this example, we use the methods describes in the [PAM_setup_guide](PAM_setup_guide.md) -to build a pam. - -```python -from PAModelpy.utils import set_up_pam - -# Define input paths -model_path = "Models/iML1515.xml" -param_file = "Data/proteinAllocationModel_iML1515_EnzymaticData_new.xlsx" - -# Build the PAM -pam = set_up_pam(pam_info_file=param_file, - model=model_path, - total_protein=0.258, # Optional: Total protein concentration (g_prot/g_cdw) - active_enzymes=True, # Do you want to include an active enzyme sector? - translational_enzymes=True, # Do you want to include a translational protein sector? - unused_enzymes=True, # Do you want to include an unused enzyme sector? - sensitivity=False, # Do you want to perform a sensitivity analysis? - adjust_reaction_ids=False) # Does your model ignore suffixes like 'pp' and 'ex'? (ofter the case in core models) -``` -### 2. Modifying the pam -Here are some example modifications to the PAM - -#### Changing kcat values -For changing a kcat value, you need to provide the enzyme-reaction-direction mapping. For example, you want to change -the kcat relating the forward reaction of `13PPDH2` with the enzyme abundance of `Q46856`: - -```python -rxn2kcat = {'13PPDH2':{'f':10}} - -pam.change_kcat_values(enzyme_id = 'Q46856', - kcats = rxn2kcat) -``` - -NB: ensure your kcat values are in the right units (1/s). The change_kcat_value function will convert the kcat values to -the model units for you. - -#### Changing enzyme concentrations -```python -enzyme = pam.enzymes.get_by_id('Q46856') -enzyme.concentration = 0.1 #mmol/gCDW -``` - -#### Changing the enzyme sector linear equations -If you want to do modifications to the linear relations of a sector, you can use the buiild-in function to do this. -This is for example usefull when you are changing carbon source and want to modify the translational sector. The -units of the intercept should be in g_protein/g_CDW. The units of the slope*lin_rxn should yield g_protein/g_CDW. -```python -translation_sector = pam.sectors.get_by_id('TranslationalProteinSector') - -pam. change_sector_parameters(sector = translation_sector, - slope = 0.1, #in this case: g_p*h/(g_cdw*mmol_glc) - intercept=0.1, # g_p/g_cdw - lin_rxn_id='EX_glc__D_e', #optional: change the reaction to which the sector is related - print_change = True #do you want to see the change? False by default - ) -``` -The output will look like: -`Changing the slope and intercept of the TranslationalProteinSector` -`Changing slope from to 10 mg/gcdw/h"` -`Changing intercept from to 10 mg/gcdw` - -#### Changing the total protein content -You can change the total protein content available for the enzyme sectors (in grams_protein/g_CDW) -```python -pam.change_total_protein_constraint(p_tot=0.3) -``` -#### Changing reaction bounds -In order to calculate sensitivities, the mathematical structure with which the reaction bounds are defines are altered. -It is therefore recommendable to use the build-in functions to change reaction bounds. Besides being more robust, -this is also more straightforward. The units are similar to the model units (mmol/gCDW/h). - -```python -pam.change_reaction_bounds(rxn_id='13PPDH2', - lower_bound= 0.1, - upper_bound=0.11) -``` - -#### Toggling sensitivity -For some applications you need a sensitivity analysis, for some you don't. As the sensitivity analysis can slow the computation -down, it is possible to toggle the sensitivity on and off between simulations: - -```python -pam.sensitivity = True #sensitivity is on -``` - -### 3. Perfoming simulations with the PAM -If you are happy with the PAM, you off course want to perform simulations! With the `change_reaction_bounds` function -you can change the bounds of for example the biomass production reaction or the substrate uptake rate. Changing -the objective can be done in the same way as you are used to with a [cobrapy model](https://cobrapy.readthedocs.io/en/latest/building_model.html#Objective). - -The only thing you have to do is optimize! - -```python -solution = pam.optimize() -``` - -### 4. Getting the results of the simulations -The solution which is returned by the `optimize()` function is the same solution object as returned by a [cobrapy model](https://cobrapy.readthedocs.io/en/latest/simulating.html). -This solution object only includes the metabolic reaction fluxes and the objective value, and NOT the enzyme concentration -and sensitivities. To access the enzyme concentrations you can use the attribute `enzyme.concentration`. - -If you enabled the sensitivity analysis, you can access the capacity sensitivity coefficients and enzyme sensitivity coefficients -after solving the model. - -```python -csc = pam.capacity_sensitivity_coefficients #pd.DataFrame with columns: ["rxn_id", "enzyme_id", "constraint", "coefficient"] -esc = pam.enzyme_sensitivity_coefficients #pd.DataFrame with columns: ["rxn_id", "enzyme_id", "coefficient"] -``` - -## Example 3: Determining the most sensitive enzymes in a toy model - -When looking at the flux distribution resulting from our simulations, we do not get any information about which enzymes -played an important role in prediciting the specific metabolic phenotype. However, with the right model configurations, -we get the sensitivity of the objective function to slight changes in the enzyme availability (enzyme sensitivity -coefficients, ESC) as a result from the model simulations. In this example we'll use a toy model to illustrate how these -sensitivities can help us explain concepts of protein allocation. - - -![toy_model_image](assets/toy-model.png) - -
**Figure 1. Toy model network and parameters** -*This toy model represents a schematic overview of a microbial metabolism, -with an energy efficient (R1-R2-R4+R5-R6-R7) and an enzyme efficient (R1-R2-R3+R5-R6-R7) pathway. Besides the enzymes -catalyzing the reactions (denoted with an 'E') and corresponding catalytic efficiency (kcat), also the relation -with the reactions and the enzyme sectors are given. UES: Unused Enzyme Sector, TES: Translational Enzyme Sector, AES: -Active Enzyme Sector.*
-
- - -First, all import statements you'll need in this example: - -```python -import numpy as np -from cobra.io import load_json_model -import plotly.express - -from PAModelpy.EnzymeSectors import ActiveEnzymeSector, TransEnzymeSector, UnusedEnzymeSector -from PAModelpy.PAModel import PAModel -from PAModelpy.PAMValidator import PAMValidator -from PAModelpy.configuration import Config -``` - -### Step 1: Build the toy model -Obviously, we first have to build the toy model. To make it easy, we have provided the toy model structure -in a .json file in the `Models` directory. As the PAModelpy package makes working with real-life data easy, -it performs units conversions to some inputs. For example, the kcat value is normally published in per sec, while we need -per hour in our calculations. Furthermore, some inputs are scaled in order to decrease the order of magnitude difference -between the variables. When we want to use 'dummy' data in a toy model, we need to take this into account. - -But before we start building the model, we need to be aware of one thing: the PAModel object assumes you want to -analyse the *E.coli* iML1515 model by default. How can we make the model aware that we are using another model, and that -we thus need other identifiers for substrate uptake rate, growth rate, etc? The Config object helps you with just that! -You can use this object to configure all the identifiers you need. Don't forget to pass this object to all the PAModel -objects you'll initialize, so all the information is passed on! - -```python -config = Config() -config.BIOMASS_REACTION = 'R7' -config.GLUCOSE_EXCHANGE_RXNID = 'R1' -config.CO2_EXHANGE_RXNID = 'R8' -config.ACETATE_EXCRETION_RXNID = 'R9' -``` - -With these defaults defined, we can start building our model. - -```python -nmbr_reactions = 9 - -# Building Active Enzyme Sector -kcat_fwd = [1, 0.5, 1, 1, 0.5 ,0.45, 1.5] -kcat_rev = [kcat for kcat in kcat_fwd] -rxn2kcat = {} -for i in range(nmbr_reactions-3): # all reactions have an enzyme, except excretion reactions - rxn_id = f'R{i+1}' - # 1e-6 to correct for the unit transformation in the model (meant to make the calculations preciser for different unit dimensions) - #dummy molmass - rxn2kcat = {**rxn2kcat, **{rxn_id: {f'E{i+1}':{'f': kcat_fwd[i]/(3600*1e-6), 'b': kcat_rev[i]/(3600*1e-6), - 'molmass': 1e6, - 'protein_reaction_relation': [[f'E{i+1}']]} - }}} -active_enzyme = ActiveEnzymeSector(rxn2protein = rxn2kcat, configuration=config) - -# Building Tranlational Protein Sector -translation_enzyme = TransEnzymeSector(id_list = ['R7'], tps_mu=[0.01*1e-3], tps_0=[0.01*1e-3], mol_mass= [1], configuration=config) - -# Building Unused Enzyme Sector -unused_enzyme = UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=config) - -# Building the toy_pam -model = load_json_model('Models/toy_model.json') -toy_pam = PAModel(model, name='toy model MCA with enzyme constraints', - active_sector=active_enzyme, - translational_sector = translation_enzyme, - unused_sector = unused_enzyme, - p_tot=0.6*1e-3, configuration=config) -``` - -### Step 2: Perform the model simulations -With the model in place, we can start our analysis. Since we are interested in which enzymes are important in different -metabolic phenotypes, we want to run simulations over a range of growth rates. After each simulation we need to retrieve -and store the enzyme sensitivity coefficients, so we can study them. We also will save the capacity sensitivity coefficients, -which will give us information about which factor is limiting metabolism (substrate or enzyme availability). We directly -save all the information we need later for plotting. - -```python -substrate_axis = list() -Ccsc = list() -Cesc = list() -x_axis_csc = list() -mu_list = list() - -for substrate in list(np.arange(1e-3, 1e-1, 1e-2)): - toy_pam.change_reaction_bounds(rxn_id='R1', - lower_bound=0, upper_bound=substrate) - toy_pam.optimize() - if toy_pam.solver.status == 'optimal' and toy_pam.objective.value>0: - print('Running simulations with ', substrate, 'mmol/g_cdw/h of substrate going into the system') - substrate_axis += [substrate] - mu_list += [toy_pam.objective.value] - - Ccsc_new = list() - for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - Ccsc_new += toy_pam.capacity_sensitivity_coefficients[toy_pam.capacity_sensitivity_coefficients['constraint'] == csc].coefficient.to_list() - Ccsc += [Ccsc_new] - - Cesc += [toy_pam.enzyme_sensitivity_coefficients.coefficient.to_list()] - - print('Sum of capacity sensitivity coefficients: \t \t \t \t \t \t \t ', round(sum(Ccsc_new),6)) - print('Sum of variable sensitivity coefficients: \t \t \t \t \t \t \t ', round(sum(Cesc[-1]), 6), '\n') - -for csc in ['flux_ub', 'flux_lb', 'enzyme_max', 'enzyme_min', 'proteome', 'sector']: - if csc == 'flux_ub' or csc == 'flux_lb': - x_axis_csc += [rid +'_' + csc for rid in toy_pam.capacity_sensitivity_coefficients[toy_pam.capacity_sensitivity_coefficients['constraint'] == csc].rxn_id.to_list()] - else: - x_axis_csc += [rid +'_' + csc for rid in toy_pam.capacity_sensitivity_coefficients[toy_pam.capacity_sensitivity_coefficients['constraint'] == csc].enzyme_id.to_list()] - -x_axis_esc = toy_pam.enzyme_sensitivity_coefficients.enzyme_id.to_list() -``` - -### Step 3: Plot the enzyme and capacity sensitivty coefficients heatmaps -By plotting our results, we learn which individual reactions and enzymes contribute the most to which -metabolic phenotype. - -```python -def print_heatmap(xaxis, matrix, yaxis = None): - - if yaxis is None: - yaxis = list() - for i in range(1, n + 1): - yaxis += [f'R{i}'] - fig = plotly.express.imshow(matrix, aspect="auto", - x = xaxis, y = yaxis, - labels = dict(x = 'sensitivity coefficients', y='substrate uptake')) - fig.show() - -print_heatmap(x_axis_csc, Ccsc, yaxis=substrate_axis) -print_heatmap(x_axis_esc, Cesc, yaxis=substrate_axis) -``` - -### Step 4: Interpret the results -Compare the toy model network structure :ref:`toy_model_image` with the results from the heatmap. Did you expect these results? Do they make -sense? Which mechanisms to explain these observations. If the observations are not inline with you're expectations, -you can use the enzyme sensitivities to point to the enzymatic parameters which might need to be adjusted (in this dummy -example this makes no sense off course, but in reality this is a very plausible outcome). - -### Outlook -This tastes like more? In our publication we use the sensitivity analysis to explain metabolic phenotypes and to pinpoint -genetic engineering examples. In the `Figures` folder you can find the code we used to generate these results. diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 808cb63..0000000 --- a/docs/index.rst +++ /dev/null @@ -1,25 +0,0 @@ -Welcome to PAModelpy -==================== - -The PAModelpy package is designed to integrate protein constraints and protein sectors into protein allocation models (PAMs). -In this documentation, you can find all the information you need to build and analyze PAMs. - -.. toctree:: - :maxdepth: 2 - :caption: Table of Contents - - introduction.md - PAM_setup_guide.md - example.md - -.. toctree:: - :maxdepth: 1 - :caption: API reference - - api_reference/CatalyticEvent.md - api_reference/Constraints.md - api_reference/Enzyme.md - api_reference/EnzymeSectors.md - api_reference/PAMValidator.md - api_reference/PAModel.md - api_reference/configuration.md diff --git a/docs/introduction.md b/docs/introduction.md deleted file mode 100644 index ebdcc44..0000000 --- a/docs/introduction.md +++ /dev/null @@ -1,85 +0,0 @@ -# PAModelpy - Protein Allocation Model reconstruction in Python - -## What is PAModelpy? -Models of metabolism are powerful tools to explore the metabolic potential of microorganism. -Powerful tools have been created to support Python-based analysis of genome-scale models. -These models, however, cannot capture all metabolic phenotypes and the simulation results have high flux variability. -Adding protein to each reaction increases the simulation fidelity. -The PAModelpy package is designed to integrate protein constraints and protein sectors as described by [Alter et al. (2021)](https://journals.asm.org/doi/10.1128/mSystems.00625-20) to metabolic models. -It is the Python implementation of the [PAM MATLAB framework](https://github.com/Spherotob/PAM_public) to create GECKO like protein-constrained models. - -The PAModelpy package builds upon the community-wide used [COBRApy](https://github.com/opencobra/cobrapy/tree/devel). -We have extended this package with the following features: -- protein-reaction associations -- infrastructure to include isozymes and promiscuous enzymes -- protein sectors -- specialized objects to build protein allocation models -- the possibility to perform a computational efficient sensitivity analysis - -## Installation -[PAModelpy is a PiPy package](https://pypi.org/project/PAModelpy/) which allows for easy installation with pip: - -`pip install PAModelpy` - -Note that the package has been tested with the [Gurobi](https://www.mathworks.com/products/connections/product_detail/gurobi-optimizer.html) solver. - -## What can you find where in this repository? -This repository contains not only the source code, but also examples and scripts which were used in -[van den Bogaard et al. (2024)](https://doi.org/10.1093/bioinformatics/btae691). - -- **Data** - - *eGFP_expression_Bienick2014*: measured growth rate and eGFP expression by [Bienick et al. (2014)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0109105) - - *proteinAllocationModel_iML1515_EnzymaticData_py*: information about the proteinsectors of the PAM for *Escherichia coli* (*E.coli*) - - *proteome_data_extract_schmidt2016*: quantitative proteomics data from [Schmidt et al. (2016)](https://www.nature.com/articles/nbt.3418) used to parametrize the *E.coli* core PAM - - *Ecoli_phenotypes/Ecoli_phenotypes_py_rev*: experimental physiology measurements to validate the model simulations -- **Examples**: example notebook on how to build, run and validate a PAM using the PAModelpy package -- **Figures**: scripts used to create Figure 1-3 and supplementary figures -- **MATLAB**: MATLAB code for doing simulations with the *E. coli* core PAM and the toy model (validating the sensitivity relationships) -- **Models**: models used (iML1515 and core *E. coli* model) -- **Results**: results of computational performance analysis -- **Scripts**: scripts used for gathering results - - computational performance analysis: `compare_computational_efficiency_esc.py` and `numeric_error_estimation_schemes_esc.py` - - *E.coli* core PAM creation: `analyze_proteome.ipynb` and `create_ecolicore_pam_incl_UE.ipynb` - - Sensitivity analysis: `toy_ec_pam.py` and `Ecoli_core_sensitivity_analysis.ipynb` -- **src/PAModelpy**: source code for PAModelpy package - - -## Code structure: -- **EnzymeSectors**: The objects which are used to store the data of the different enzyme sectors which are added to the genome-scale model -- **PAModel**: Proteome Allocation (PA) model class. This class builds on to the `cobra.core.Model` class from the COBRApy toolbox with functions to build enzyme sectors, to add enzyme kinetics parameters and in the future to perform a sensitivity analysis on the enzyme variables. -- **Enzyme**: Different classes which relate enzymes to the model with enzyme constraints and variables. -- **CatalyticEvent**: A class which serves as an interface between reactions and enzyme. This allows for easy lookup of Protein-Reaction assocations. -- **PAMValidator**: Functions to validate the model predictions with physiology data and giving a graphical overview. The script uses data for E.coli (found in `./Data/Ecoli_physiology`) by default. - -For the technical users, the following UML diagram gives an overview of the model structure and the software architecture. Please be aware that the Config object is not shown in this UML diagram, -as this object is merely used to transfer identifiers from one object to another in the software. - -![PAModelUML](PAModelpy_UML.svg) - -*UML diagram of the PAModelpy software. Not all attributes and functions are shown. Those who are shown are assumes to be most descriptive for the objects function.* - -## Dependencies -Dependencies for the scripts in this repository, not included in the PAModelpy package: -- `PAModelpy` -- `plotly` -- `matplotlib` -- `scipy` -- `time` -- `resource` -- `PIL` - -All dependencies can be installed in one go by downloading this repository and running: - -`python setup.py install` - -from the `src` directory - -The dependencies of the PAModelpy package can be found in `src/pyproject.toml` - -## License -Copyright institute of Applied Microbiology, RWTH Aachen University, Aachen, Germany (2024) - -PAModelpy is free of charge open source software, which can be used and modified for your particular purpose under the [MIT](https://opensource.org/license/mit/) -or [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) of the users choice. - -Please note that according to these licenses, the software is provided 'as is', WITHOUT WARRANTY OF ANY KIND, without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. \ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 5394189..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt deleted file mode 100644 index 504ef6d..0000000 --- a/docs/requirements.txt +++ /dev/null @@ -1,3 +0,0 @@ -sphinx==7.1.2 -sphinx-rtd-theme==1.3.0rc1 -myst-parser==3.0.1 \ No newline at end of file diff --git a/docs/sidebars.js b/docs/sidebars.js deleted file mode 100644 index 3327580..0000000 --- a/docs/sidebars.js +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Creating a sidebar enables you to: - - create an ordered group of docs - - render a sidebar for each doc of that group - - provide next/previous navigation - - The sidebars can be generated from the filesystem, or explicitly defined here. - - Create as many sidebars as you want. - */ - -// @ts-check - -/** @type {import('@docusaurus/plugin-content-docs').SidebarsConfig} */ -const sidebars = { - // By default, Docusaurus generates a sidebar from the docs folder structure - tutorialSidebar: [{type: 'autogenerated', dirName: '.'}], - - // But you can create a sidebar manually - /* - tutorialSidebar: [ - 'intro', - 'hello', - { - type: 'category', - label: 'Tutorial', - items: ['tutorial-basics/create-a-document'], - }, - ], - */ -}; - -export default sidebars; diff --git a/src/PAModelpy/MembraneSector.py b/src/PAModelpy/MembraneSector.py index 03fc5d0..d61487a 100644 --- a/src/PAModelpy/MembraneSector.py +++ b/src/PAModelpy/MembraneSector.py @@ -16,23 +16,22 @@ def __init__( enzyme_location: {}, cog_class: {} = None, max_area: float = 0.27, + r_alpha: float = 0.00023, # radius of one alpha helix [um] + cdw_per_cell: float = 0.28 * 1e-12, # 0.28 pg + n_a: float = 6.02214076 * 1e23, # avogadro number configuration=Config): self.id = 'MembraneSector' - self.area_avail_0 = area_avail_0 - self.area_avail_mu = area_avail_mu self.alpha_numbers_dict = alpha_numbers_dict self.cog_class = cog_class self.enzyme_location = enzyme_location - self.area_alpha = math.pi * math.pow((0.00023), 2) # area per alpha helix unit [um] - self.cdw_per_cell = 0.28 * 1e-12 #0.28 pg - self.n_a = 6.02214076 * 1e23 #avogadro number + self.area_alpha = math.pi * math.pow((r_alpha), 2) # area per alpha helix unit [um2] self.max_membrane_area = max_area #percentage of membrane area that can be covered by proteins - self.unit_factor = 1e-3 * self.cdw_per_cell * self.n_a + self.unit_factor = 1e-3 * cdw_per_cell * n_a #Defining the slope and intercept - self.intercept = self.area_avail_0 #μm2 - self.slope = self.area_avail_mu #μm2/h + self.intercept = area_avail_0 #μm2 + self.slope = area_avail_mu #μm2/h def add(self, model): @@ -50,28 +49,11 @@ def _add_membrane_constraint(self, model): } for enz_complex in model.enzyme_variables: - enzymes = enz_complex.id.split("_") - alpha_numbers_in_complex = [0] # zero if enzyme is not in membrane - - for enz in enzymes: - if enz in self.alpha_numbers_dict.keys() and self.enzyme_location[ - enz] == 'Cell membrane': - alpha_numbers_in_complex.append(self.alpha_numbers_dict[enz]) - self.membrane_proteins[enz_complex.id] = enz_complex.kcats - - alpha_numbers_for_complex = max(alpha_numbers_in_complex) + alpha_number_for_complex = self.get_alpha_number_for_enz_complex(enz_complex) + coeff = self.get_coeff_value(alpha_number_for_complex) - coefficients[enz_complex.forward_variable] = ( - 1e-6 * alpha_numbers_for_complex # correction for the solver issue - * self.area_alpha - * self.unit_factor - / self.max_membrane_area) - - coefficients[enz_complex.reverse_variable] = ( - 1e-6 * alpha_numbers_for_complex # correction for the solver issue - * self.area_alpha - * self.unit_factor - / self.max_membrane_area) + coefficients[enz_complex.forward_variable] = coeff / self.max_membrane_area + coefficients[enz_complex.reverse_variable] = coeff / self.max_membrane_area occupied_membrane = model.problem.Constraint(0, lb=0, ub=self.intercept, name='membrane') model.add_cons_vars(occupied_membrane) @@ -79,27 +61,17 @@ def _add_membrane_constraint(self, model): occupied_membrane.set_linear_coefficients(coefficients=coefficients) def calculate_occupied_membrane(self, model): - occupied_membrane = 0 + occupied_area = 0 for enz_complex in model.enzyme_variables: - enzymes = enz_complex.id.split("_") enz_complex_concentration = enz_complex.forward_variable.primal + enz_complex.reverse_variable.primal - alpha_numbers_in_complex = [0] - - for enz in enzymes: - if enz in self.alpha_numbers_dict.keys() and self.enzyme_location[ - enz] == 'Cell membrane': - alpha_numbers_in_complex.append(self.alpha_numbers_dict[enz]) - - alpha_numbers_for_complex = max(alpha_numbers_in_complex) - occupied_membrane += ( - enz_complex_concentration * 1e-6 * alpha_numbers_for_complex # correction for the solver issue - * self.area_alpha - * self.unit_factor) + alpha_number_for_complex = self.get_alpha_number_for_enz_complex(enz_complex) + coeff = self.get_coeff_value(alpha_number_for_complex) + occupied_area += coeff * enz_complex_concentration - available_membrane = self.slope * model.objective.value + self.intercept + available_area = self.slope * model.objective.value + self.intercept - return occupied_membrane, available_membrane + return occupied_area, available_area def change_available_membrane_area(self, new_max_area: float, model): self._update_membrane_constraint(new_max_area, model) @@ -113,29 +85,40 @@ def _update_membrane_constraint(self, new_max_area:float, model): } for enz_complex in model.enzyme_variables: + alpha_number_for_complex = self.get_alpha_number_for_enz_complex(enz_complex) + coeff = self.get_coeff_value(alpha_number_for_complex) + + coefficients[enz_complex.forward_variable] = coeff / new_max_area + coefficients[enz_complex.reverse_variable] = coeff / new_max_area + + model.constraints['membrane'].set_linear_coefficients(coefficients=coefficients) + model.solver.update() + + return self + + def get_alpha_number_for_enz_complex(self, enz_complex): + if isinstance(enz_complex, str): + enzymes = enz_complex.split("_") + else: enzymes = enz_complex.id.split("_") - alpha_numbers_in_complex = [0] # zero if enzyme is not in membrane + alpha_numbers_in_complex = [0] # zero if enzyme is not in membrane - for enz in enzymes: - if enz in self.alpha_numbers_dict.keys() and self.enzyme_location[enz] == 'Cell membrane': - alpha_numbers_in_complex.append(self.alpha_numbers_dict[enz]) + for enz in enzymes: + if enz in self.alpha_numbers_dict.keys() and self.enzyme_location[ + enz] == 'Cell membrane': + alpha_numbers_in_complex.append(self.alpha_numbers_dict[enz]) + if not isinstance(enz_complex, str): self.membrane_proteins[enz_complex.id] = enz_complex.kcats - alpha_numbers_for_complex = max(alpha_numbers_in_complex) + alpha_number_for_enz_complex = max(alpha_numbers_in_complex) - coefficients[enz_complex.forward_variable] = ( - 1e-6 * alpha_numbers_for_complex # correction for the solver issue - * self.area_alpha - * self.unit_factor - / new_max_area) + return alpha_number_for_enz_complex - coefficients[enz_complex.reverse_variable] = ( - 1e-6 * alpha_numbers_for_complex # correction for the solver issue - * self.area_alpha - * self.unit_factor - / new_max_area) + def get_coeff_value(self, alpha_number_for_complex:int): - model.constraints['membrane'].set_linear_coefficients(coefficients=coefficients) - model.solver.update() + coeff = (1e-6 # correction for the solver issue + * alpha_number_for_complex + * self.area_alpha + * self.unit_factor) - return self \ No newline at end of file + return coeff \ No newline at end of file diff --git a/src/PAModelpy/utils/pam_generation.py b/src/PAModelpy/utils/pam_generation.py index e187be6..7c394f6 100644 --- a/src/PAModelpy/utils/pam_generation.py +++ b/src/PAModelpy/utils/pam_generation.py @@ -489,6 +489,117 @@ def set_up_pam(pam_info_file:str = '', ) return pamodel +def set_up_core_pam(pam_info_file:str = '', + model:Union[str, cobra.Model] = 'Models/e_coli_core.json', + config:Config = None, + total_protein: Union[bool, float] = True, + active_enzymes: bool = True, + translational_enzymes: bool = True, + unused_enzymes: bool = True, + membrane_sector: bool = False, + max_membrane_area:float = 0.03, + sensitivity:bool = True, + enzyme_db:pd.DataFrame = None, + adjust_reaction_ids:bool = True) -> PAModel: + + + if config is None: + config = Config() + config.reset() + + # some other constants + BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM' + config.BIOMASS_REACTION = BIOMASS_REACTION + TOTAL_PROTEIN_CONCENTRATION = 0.16995 # [g_prot/g_cdw] + + #setup model if a path is provided + if isinstance(model, str): + model = cobra.io.load_json_model(model) + + #check if a different total protein concentration is given + if isinstance(total_protein, float): + TOTAL_PROTEIN_CONCENTRATION = total_protein + + # load example data for the E.coli iML1515 model + if active_enzymes: + # load active enzyme sector information + if enzyme_db is None: + enzyme_db = pd.read_excel(pam_info_file, sheet_name='ActiveEnzymes') + #for some models, the reaction ids should not include 'pp' or 'ex' + if adjust_reaction_ids: + enzyme_db['rxn_id'] = enzyme_db['rxn_id'].apply(_check_rxn_identifier_format) + # create enzyme objects for each gene-associated reaction + rxn2protein, protein2gene = parse_reaction2protein(enzyme_db, model) + + active_enzyme_info = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene, + configuration=config) + else: + active_enzyme_info = None + + if translational_enzymes: + # translational protein sector parameter (substrate dependent) + id_list_tps = ['EX_glc__D_e'] + tps_0 = [0.04992] # g/gDW + tps_mu = [-0.002944] # g h/gDW -> transformed to match glucose uptake variable + molmass_tps = [405903.94] # g/mol + + # translational protein sector + translation_enzyme_info = TransEnzymeSector( + id_list=id_list_tps, + tps_0=tps_0, + tps_mu=tps_mu, + mol_mass=molmass_tps, + configuration=config + ) + else: + translation_enzyme_info = None + + if unused_enzymes: + id_list_ups = [BIOMASS_REACTION] + ups_0 = [0.0407] # g/gDW + ups_mu = [-0.0214] # g h/gDW -> negative relation with growth rate + molmass_ups = [405903.94] # g/mol + + unused_enzyme_info = UnusedEnzymeSector( + id_list=id_list_ups, + ups_0=ups_0, + ups_mu=ups_mu, + mol_mass=molmass_ups, + configuration=config + ) + else: + unused_enzyme_info = None + + if membrane_sector: + membrane_info = pd.read_excel(pam_info_file, sheet_name='Membrane').set_index('Parameter') + active_membrane_info = pd.read_excel(pam_info_file, sheet_name='MembraneEnzymes').set_index('enzyme_id') + + area_avail_0 = membrane_info.at['area_avail_0','Value'] + area_avail_mu = membrane_info.at['area_avail_mu','Value'] + alpha_numbers_dict = active_membrane_info.alpha_numbers.to_dict() + enzyme_location = active_membrane_info.location.to_dict() + + membrane_sector = MembraneSector(area_avail_0=area_avail_0, + area_avail_mu=area_avail_mu, + alpha_numbers_dict=alpha_numbers_dict, + enzyme_location=enzyme_location, + max_area=max_membrane_area) + + else: + membrane_sector = None + + + if total_protein: total_protein = TOTAL_PROTEIN_CONCENTRATION + + coremodel = PAModel(id_or_model=model, p_tot=total_protein, + active_sector=active_enzyme_info, + translational_sector=translation_enzyme_info, + unused_sector=unused_enzyme_info, + membrane_sector=membrane_sector, + sensitivity=sensitivity, configuration = config + ) + return coremodel + def increase_kcats_in_parameter_file(kcat_increase_factor: int, pam_info_file_path_ori: str, pam_info_file_path_out: str = None): diff --git a/tests/acceptance_tests/test_accept_pamodel.py b/tests/acceptance_tests/test_accept_pamodel.py deleted file mode 100644 index 2194555..0000000 --- a/tests/acceptance_tests/test_accept_pamodel.py +++ /dev/null @@ -1,116 +0,0 @@ -import pytest -from cobra.io import load_json_model -import sys -import os - -sys.path.append(os.path.abspath(os.path.dirname( - os.path.dirname( #testing dir - os.path.dirname(__file__))))) #this dir - -from src.PAModelpy.PAModel import PAModel -from src.PAModelpy.configuration import Config -from src.PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector - -from Scripts.pam_generation import set_up_toy_pam, set_up_ecoli_pam - -# def test_toy_model_sensitivity_coefficients_relations_sum_is_correct(): -# #arrange -# toy_pam = set_up_toy_pam() -# #act -# toy_pam.test(-0.001) -# #assert -# assert_sensitivity_coefficients(toy_pam) -# -# def test_ecoli_model_sensitivity_coefficients_relations_sum_is_correct(): -# # arrange -# ecoli_pam = set_up_ecoli_pam() -# # act -# ecoli_pam.test() -# # assert -# assert_sensitivity_coefficients(ecoli_pam) - - -####################################################################################################### -#HELPER METHODS -####################################################################################################### - -def assert_sensitivity_coefficients(pamodel): - feasibility_deviation = pamodel.solver.problem.Params.FeasibilityTol * len(pamodel.variables) *4 - - # calculate fraction of protein space occupied - sum_enzymes = pamodel.calculate_sum_of_enzymes() - phi_e0 = pamodel.constraints[pamodel.TOTAL_PROTEIN_CONSTRAINT_ID].ub - alpha = sum_enzymes / phi_e0 - - # calculate sum of coefficients - sum_FCSC = calculate_sum_of_flux_capacity_sensitivity_coefficients(pamodel) - sum_ECSC = calculate_sum_of_enzyme_capacity_sensitivity_coefficients(pamodel) - PCSC = calculate_proteome_capacity_sensitivity_coefficients(pamodel) - sum_ESC = calculate_sum_of_enzyme_sensitivity_coefficients(pamodel) - - # validation sums - enzyme_flux_sum = sum_ESC + sum_FCSC + (1 - alpha) * PCSC - enzyme_sum = -sum_ESC + sum_ECSC + alpha * PCSC - - #assert - assert 1 == pytest.approx(sum_ECSC+sum_FCSC + PCSC, feasibility_deviation) - # assert 1 == pytest.approx(enzyme_flux_sum, feasibility_deviation) - # assert 0 == pytest.approx(enzyme_sum, feasibility_deviation) - -def calculate_sum_of_enzymes(pamodel): - sum = 0 # mg/gcdw/h - for enzyme in pamodel.enzyme_variables: - sum += enzyme.concentration - return sum - -def calculate_sum_of_flux_capacity_sensitivity_coefficients(pamodel): - return sum(pamodel.capacity_sensitivity_coefficients[ - (pamodel.capacity_sensitivity_coefficients['constraint'] == 'flux_ub') | ( - pamodel.capacity_sensitivity_coefficients['constraint'] == 'flux_lb')].coefficient) - -def calculate_sum_of_enzyme_capacity_sensitivity_coefficients(pamodel): - return sum(pamodel.capacity_sensitivity_coefficients[ - (pamodel.capacity_sensitivity_coefficients['constraint'] == 'enzyme_min') | ( - pamodel.capacity_sensitivity_coefficients[ - 'constraint'] == 'enzyme_max')].coefficient) - -def calculate_proteome_capacity_sensitivity_coefficients(pamodel): - return pamodel.capacity_sensitivity_coefficients[ - pamodel.capacity_sensitivity_coefficients['constraint'] == 'proteome'].coefficient.iloc[0] - -def calculate_sum_of_enzyme_sensitivity_coefficients(pamodel): - return sum(pamodel.enzyme_sensitivity_coefficients.coefficient) - -def build_active_enzyme_sector(Config): - n=9 - kcat_fwd = [1, 0.5, 1, 1, 0.5 ,0.45, 1.5] # High turnover for exchange reactions - kcat_rev = [kcat for kcat in kcat_fwd] - rxn2kcat = {} - for i in range(n-3): # all reactions have an enzyme, except excretion reactions - rxn_id = f'R{i+1}' - # 1e-6 to correct for the unit transformation in the model (meant to make the calculations preciser for different unit dimensions) - #dummy molmass like in MATLAB script - rxn2kcat = {**rxn2kcat, **{rxn_id: {f'E{i+1}':{'f': kcat_fwd[i]/(3600*1e-6), 'b': kcat_rev[i]/(3600*1e-6), 'molmass': 1e6}}}} - - return ActiveEnzymeSector(rxn2protein = rxn2kcat, configuration=Config) - -def build_unused_protein_sector(Config): - return UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=Config) - -def build_translational_protein_sector(Config): - return TransEnzymeSector(id_list = ['R7'], tps_mu=[0.01*1e-3], tps_0=[0.01*1e-3], mol_mass= [1], configuration=Config) - -def build_toy_pam(): - Config.BIOMASS_REACTION = 'R7' - Config.GLUCOSE_EXCHANGE_RXNID = 'R1' - Config.CO2_EXHANGE_RXNID = 'R8' - Config.ACETATE_EXCRETION_RXNID = 'R9' - - model = load_json_model('toy_model.json') - active_enzyme = build_active_enzyme_sector(Config) - unused_enzyme = build_unused_protein_sector(Config) - translation_enzyme = build_translational_protein_sector(Config) - pamodel = PAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme, - translational_sector = translation_enzyme, - unused_sector = unused_enzyme, p_tot=0.6*1e-3, configuration=Config) - return pamodel \ No newline at end of file diff --git a/tests/unit_tests/test_pamodel/test_catalytic_event.py b/tests/unit_tests/test_pamodel/test_catalytic_event.py deleted file mode 100644 index 9aa875b..0000000 --- a/tests/unit_tests/test_pamodel/test_catalytic_event.py +++ /dev/null @@ -1,25 +0,0 @@ -import pytest - -from src.PAModelpy import CatalyticEvent - -def test_if_catalytic_events_splits_rxns_from_enzymes(): - # Arrange - sut = CatalyticEvent - catalytic_reaction_ids = [ - "CYTBO3_4pp", "CE_CYTBO3_4pp_P0ABJ1_P0ABJ5_P0ABJ7_P0ABJ8", "CE_CYTBO3_P18200_P0AEZ7", - "CE_ME_P23N45", "ME", - "CE_REACTID_O123A4", "CE_REACTID_A1A2A3", - "LPLIPAL2ATE140","CE_LPLIPAL2ATE140_P23N45" - ] - rxn_ids = ["CYTBO3_4pp", "CYTBO3_4pp", "CYTBO3", - "ME", "ME", - "REACTID", "REACTID", - "LPLIPAL2ATE140", "LPLIPAL2ATE140"] - - # Apply - parsed_rxn_ids = [] - for cr_id in catalytic_reaction_ids: - parsed_rxn_ids.append(sut._extract_reaction_id_from_catalytic_reaction_id(cr_id)) - - # Assert - assert all([rxn_id_sut == rxn_id for rxn_id_sut, rxn_id in zip(parsed_rxn_ids, rxn_ids)]) \ No newline at end of file diff --git a/tests/unit_tests/test_pamodel/test_membrane_sector.py b/tests/unit_tests/test_pamodel/test_membrane_sector.py index 018e1b5..2b4e525 100644 --- a/tests/unit_tests/test_pamodel/test_membrane_sector.py +++ b/tests/unit_tests/test_pamodel/test_membrane_sector.py @@ -14,32 +14,48 @@ from src.PAModelpy.EnzymeSectors import ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector from src.PAModelpy.MembraneSector import MembraneSector +def test_if_get_alpha_number_for_enz_complex_works(): + # Arrange + membrane_sector = build_membrane_sector() + enz_complex = "E7_E8_E9_10" + expected_alpha_number = 48 + + # Act + actual_alpha_number = membrane_sector.get_alpha_number_for_enz_complex(enz_complex) + + # Assert + assert expected_alpha_number == actual_alpha_number + def test_if_add_membrane_constraint_works(): # Arrange - toy_pam = build_toy_pam(membrane_sector=build_membrane_sector()) + toy_pam = build_toy_model(membrane_sector=True) + alpha_numbers_dict = {'E1_E2_E3': 20, + 'E7_E8_E9_E10': 48, + 'E14': 12} - #calling out the variable names + ##calling out the variable names biomass_var = toy_pam.reactions.get_by_id(toy_pam.BIOMASS_REACTION).forward_variable - forward_var = toy_pam.enzyme_variables.P43660_P43661_P43662.forward_variable - reverse_var = toy_pam.enzyme_variables.P43660_P43661_P43662.reverse_variable - - expected_coefficients = { - biomass_var: (-toy_pam.membrane_sector.slope), - forward_var: (1e-6 * toy_pam.membrane_sector.alpha_numbers_dict['P43661'] - * toy_pam.membrane_sector.area_alpha - * toy_pam.membrane_sector.unit_factor), - reverse_var: (1e-6 * toy_pam.membrane_sector.alpha_numbers_dict['P43661'] - * toy_pam.membrane_sector.area_alpha - * toy_pam.membrane_sector.unit_factor) - } - # Act - toy_pam.add_membrane_constraint() + for enz_complex in toy_pam.enzyme_variables: + if enz_complex.id in alpha_numbers_dict.keys(): + expected_coefficients = { + biomass_var: (-toy_pam.membrane_sector.slope), + enz_complex.forward_variable: (1e-6 * alpha_numbers_dict[enz_complex.id] + * toy_pam.membrane_sector.area_alpha + * toy_pam.membrane_sector.unit_factor + /toy_pam.membrane_sector.max_membrane_area), + enz_complex.reverse_variable: (1e-6 * alpha_numbers_dict[enz_complex.id] + * toy_pam.membrane_sector.area_alpha + * toy_pam.membrane_sector.unit_factor + /toy_pam.membrane_sector.max_membrane_area) + } - # Retrieve the membrane constraint + # Act + ## Retrieve the membrane constraint membrane_constraint = toy_pam.constraints['membrane'] print(f"Membrane constraint: {membrane_constraint}") - retrieved_coefficients = membrane_constraint.get_linear_coefficients([biomass_var, forward_var, reverse_var]) + for enz_complex in toy_pam.enzyme_variables: + retrieved_coefficients = membrane_constraint.get_linear_coefficients([biomass_var, enz_complex.forward_variable, enz_complex.reverse_variable]) #Debug print('Expected coefficients: ', expected_coefficients) @@ -51,106 +67,96 @@ def test_if_add_membrane_constraint_works(): assert membrane_constraint.lb == 0 assert membrane_constraint.ub == toy_pam.membrane_sector.intercept -def test_if_membrane_constraints_the_same_for_sensitivity_with_and_without_toypam(): - - # building the mcpam models - membrane_sector = build_membrane_sector() - sut_toypam_true = build_toy_pam(sensitivity=True, membrane_sector=membrane_sector) - sut_toypam_false = build_toy_pam(sensitivity=False, membrane_sector=membrane_sector) - - # calling out the membrane constraints for both mcpam models - membrane_constraint_sut_toypam_true = sut_toypam_true.constraints['membrane'] - membrane_constraint_sut_toypam_false = sut_toypam_false.constraints['membrane'] - - # calling out the upper and lower bound of the constraints for both mcpam models - models = [sut_toypam_true, sut_toypam_false] - df_true = pd.DataFrame(columns=['lb', 'ub']) - df_false = pd.DataFrame(columns=['lb', 'ub']) - - for model in models: - lb = [] - ub = [] - if model.sensitivity: - for constraint in model.constraints: - lb.append(constraint.lb) - ub.append(constraint.ub) - - df_true['lb'] = lb - df_true['ub'] = ub - - else: - for constraint in model.constraints: - lb.append(constraint.lb) - ub.append(constraint.ub) - - df_false['lb'] = lb - df_false['ub'] = ub - - assert membrane_constraint_sut_toypam_true == membrane_constraint_sut_toypam_false - assert df_true['lb'] == df_false['lb'] - assert df_true['ub'] == df_false['ub'] - ##helper methods -def build_active_enzyme_sector(Config): - n=9 - kcat_fwd = [1, 0.5, 1, 1, 0.5 ,0.45, 1.5] # High turnover for exchange reactions +def build_membrane_sector(): + alpha_numbers_dict = {"E1": 20, + "E2": 20, + "E3": 20, + "E4": 8, + "E5": 1, + "E6": 1, + "E7": 48, + "E8": 48, + "E9": 48, + "E10": 48, + "E11": 12, + "E12": 12, + "E13": 12, + "E14": 12} + + enzyme_location = {"E1": "Cell membrane", + "E2": "Cell membrane", + "E3": "Cell membrane", + "E4": "Cytosol", + "E5": "Cytosol", + "E6": "Cytosol", + "E7": "Cell membrane", + "E8": "Cell membrane", + "E9": "Cell membrane", + "E10": "Cell membrane", + "E11": "Cytosol", + "E12": "Cytosol", + "E13": "Cytosol", + "E14": "Cell membrane"} + # mu = 0.1, 0=0.005 + membrane_sector = MembraneSector(area_avail_mu=-0.1042, area_avail_0=0.1479, + alpha_numbers_dict=alpha_numbers_dict, + enzyme_location=enzyme_location, max_area=1) + + return membrane_sector + +def build_toy_model(sensitivity:bool=True, membrane_sector: bool=False): + config = Config() + config.reset() + config.BIOMASS_REACTION = 'R11' + config.GLUCOSE_EXCHANGE_RXNID = 'R2' + config.CO2_EXHANGE_RXNID = 'R12' + config.ACETATE_EXCRETION_RXNID = 'R7' + + nmbr_reactions = 12 + + # Building Active Enzyme Sector + kcat_fwd = [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 1, 10e-2, 10e-2] kcat_rev = [kcat for kcat in kcat_fwd] - gpr_string = [['gene1', 'gene2', 'gene3'], ['gene2', 'gene3'], ['gene3'], ['gene4'], ['gene5'], ['gene6'], ['gene7'], ['gene8']] - pra_string = [[['P43660', 'P43661', 'P43662']], [['P43661', 'P43662']], [['P43662']], [['P43663']], [['P43664']], [['P43665']], [['P43666']]] + enz_complex_id = ['E1_E2_E3', 'E4', 'E5_E6', 'E7_E8_E9_E10', 'E11', 'E12', 'E13', 'E14'] + gpr_string = [['g1','g2','g3'], ['g4'],['g5','g6'],['g7','g8','g9','g10'],['g11','g12'],['g13'],['g14'],['g15']] + pra_string = ['g1 and g2 and g3', 'g4', 'g5 and g6', 'g7 and g8 and g9 and g10', 'g11 or g12', 'g13', 'g14', 'g15'] + rxn2protein = {} protein2gene = {} - for i in range(n-3): # all reactions have an enzyme, except excretion reactions - rxn_id = f'R{i+1}' + for i in range(nmbr_reactions-4): # all reactions have an enzyme, except excretion reactions + rxn_id = f'R{i+2}' # 1e-6 to correct for the unit transformation in the model (meant to make the calculations preciser for different unit dimensions) - #dummy molmass like in MATLAB script - rxn2protein = {**rxn2protein, - **{rxn_id: {f'P4366{i}': {'f': kcat_fwd[i] / (3600 * 1e-6), 'b': kcat_rev[i] / (3600 * 1e-6), - 'molmass': 1e6, 'genes': gpr_string[i], - 'protein_reaction_association': pra_string[i]}}}} - protein2gene = {**protein2gene, **{f'P4366{i}': gpr_string[i]}} - - return ActiveEnzymeSector(rxn2protein = rxn2protein, protein2gene=protein2gene, configuration=Config) + #dummy molmass + rxn2protein = {**rxn2protein, **{rxn_id: {f'{enz_complex_id[i]}':{'f': kcat_fwd[i]/(3600*1e-6), 'b': kcat_rev[i]/(3600*1e-6), + 'molmass': 1e6, 'genes': gpr_string[i], + 'protein_reaction_association': pra_string[i]}}}} + protein2gene = {**protein2gene, **{f'E{i+2}': gpr_string[i]}} + + active_enzyme = ActiveEnzymeSector(rxn2protein = rxn2protein, protein2gene=protein2gene, configuration=config) + + # Building Translational Protein Sector + translation_enzyme = TransEnzymeSector(id_list = ['R11'], tps_mu=[0.01*1e-3], tps_0=[0.01*1e-3], mol_mass= [1], configuration=config) + + # Building Unused Enzyme Sector + unused_enzyme = UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=config) + + # Building Membrane Sector + if membrane_sector: + membrane_sector = build_membrane_sector() + else: + membrane_sector = None + + # Building the toy_pam + model = load_json_model('Models/toy_model.json') + pamodel = PAModel(model, name='toy model MCA with enzyme constraints', + sensitivity=sensitivity, + active_sector=active_enzyme, + translational_sector=translation_enzyme, + unused_sector=unused_enzyme, + membrane_sector=membrane_sector, + p_tot=0.2, configuration=config) -def build_unused_protein_sector(Config): - return UnusedEnzymeSector(id_list = ['R1'], ups_mu=[-0.01*1e-3], ups_0=[0.1*1e-3], mol_mass= [1], configuration=Config) - -def build_translational_protein_sector(Config): - return TransEnzymeSector(id_list = ['R7'], tps_mu=[0.01*1e-3], tps_0=[0.01*1e-3], mol_mass= [1], configuration=Config) - -def build_membrane_sector(): - alpha_numbers_dict = {"P43660": 12, - "P43661": 8, - "P43662": 1, - "P43663": 12, - "P43664": 40, - "P43665": 15, - "P43666": 15} - - enzyme_location = {"P43660": "Unknown", - "P43661": "Cell membrane", - "P43662": "Cell membrane", - "P43663": "Cytoplasm", - "P43664": "Cytoplasm", - "P43665": "Cell membrane", - "P43666": "Cell membrane"} - - return MembraneSector(area_avail_mu=6.2129, area_avail_0=4.7522, - alpha_numbers_dict=alpha_numbers_dict, enzyme_location=enzyme_location) - -def build_toy_pam(sensitivity = True, membrane_sector = None): - Config.BIOMASS_REACTION = 'R7' - Config.GLUCOSE_EXCHANGE_RXNID = 'R1' - Config.CO2_EXHANGE_RXNID = 'R8' - Config.ACETATE_EXCRETION_RXNID = 'R9' - - model = load_json_model('Data/toy_model_cell_membrane_localization.json') - active_enzyme = build_active_enzyme_sector(Config) - unused_enzyme = build_unused_protein_sector(Config) - translation_enzyme = build_translational_protein_sector(Config) - pamodel = PAModel(model, name='toy model MCA with enzyme constraints', active_sector=active_enzyme, - translational_sector = translation_enzyme, sensitivity = sensitivity, - unused_sector = unused_enzyme, membrane_sector=membrane_sector, - p_tot=0.6*1e-3, configuration=Config) return pamodel def assert_bounds(model_ori, model_copy):