notebooks/Calculate_Popen_SynapticCurrent_alpha1GlyR_Flip.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate series of Popen curves for $\\alpha_1$ GlyR: Flip mechanism (WT from Burzomato 2004)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import numpy as np\n",
    "import yaml\n",
    "from scalcs import popen\n",
    "from scalcs import scplotlib as scpl\n",
    "from scalcs import cjumps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ratio_maxPopen(mec1, mec2, tres):\n",
    "    emaxPopen1 = popen.maxPopen(mec1, tres)\n",
    "    emaxPopen2 = popen.maxPopen(mec2, tres)\n",
    "    return emaxPopen1 / emaxPopen2 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Original rate constants for rat WT  $\\alpha_1$ GlyR taken from Burzomato 2004"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "class dcpyps.Mechanism\n",
      "Values of unit rates [1/sec]:\n",
      "0\tFrom AF*  \tto AF    \talpha1       \t4100.0\n",
      "1\tFrom AF  \tto AF*    \tbeta1        \t1500.0\n",
      "2\tFrom A2F*  \tto A2F    \talpha2       \t1400.0\n",
      "3\tFrom A2F  \tto A2F*    \tbeta2        \t18000.0\n",
      "4\tFrom A3F*  \tto A3F    \talpha3       \t700.0\n",
      "5\tFrom A3F  \tto A3F*    \tbeta3        \t28000.0\n",
      "6\tFrom A3F  \tto A3R    \tgama3        \t1600.0\n",
      "7\tFrom A3R  \tto A3F    \tdelta3       \t13000.0\n",
      "8\tFrom A3F  \tto A2F    \t3kf(-3)      \t4500.0\n",
      "9\tFrom A2F  \tto A3F    \tkf(+3)       \t11000000.0\n",
      "10\tFrom A2F  \tto A2R    \tgama2        \t11500.0\n",
      "11\tFrom A2R  \tto A2F    \tdelta2       \t15500.0\n",
      "12\tFrom A2F  \tto AF    \t2kf(-2)      \t3000.0\n",
      "13\tFrom AF  \tto A2F    \t2kf(+2)      \t22000000.0\n",
      "14\tFrom AF  \tto AR    \tgama1        \t500.0\n",
      "15\tFrom AR  \tto AF    \tdelta1       \t100.0\n",
      "16\tFrom A3R  \tto A2R    \t3k(-3)       \t600.0\n",
      "17\tFrom A2R  \tto A3R    \tk(+3)        \t230000.0\n",
      "18\tFrom A2R  \tto AR    \t2k(-2)       \t400.0\n",
      "19\tFrom AR  \tto A2R    \t2k(+2)       \t460000.0\n",
      "20\tFrom AR  \tto R    \tk(-1)        \t600.0\n",
      "21\tFrom R  \tto AR    \t3k(+1)       \t690000.0\n",
      "\n",
      "Conductance of state AF* (pS)  =      40\n",
      "\n",
      "Conductance of state A2F* (pS)  =      40\n",
      "\n",
      "Conductance of state A3F* (pS)  =      40\n",
      "\n",
      "Number of open states = 3\n",
      "Number of short-lived shut states (within burst) = 6\n",
      "Number of long-lived shut states (between bursts) = 1\n",
      "Number of desensitised states = 0\n",
      "\n",
      "Number of cycles = 2\n",
      "Cycle 0 is formed of states: A3R  A3F  A2F  A2R  \n",
      "\tforward product = 1.547325000e+17\n",
      "\tbackward product = 1.636800000e+17\n",
      "Cycle 1 is formed of states: AF  A2F  A2R  AR  \n",
      "\tforward product = 1.012000000e+16\n",
      "\tbackward product = 1.069500000e+16"
     ]
    }
   ],
   "source": [
    "tres = 30e-6\n",
    "filename = \"Flip_a1GlyRWT_Burz2004.yaml\"\n",
    "stream = open(filename, 'r')\n",
    "mec0 = yaml.load(stream)\n",
    "stream.close()\n",
    "mec0.printout()\n",
    "stream = open(filename, 'r')\n",
    "mec = yaml.load(stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "c0, pe0, pi0 = scpl.Popen(mec0, tres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Effect of changing $\\delta_3$ only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original mec:  maxPopen = 0.96095;  EC50 = 155.97 microM;  nH = 2.2392\n",
      "Modified mec:  maxPopen = 0.94043;  EC50 = 157.66 microM;  nH = 2.2295\n",
      "EC50 ratio:  1.0108173034289274\n",
      "Modified mec:  maxPopen = 0.90126;  EC50 = 158.78 microM;  nH = 2.2264\n",
      "EC50 ratio:  1.0179888462245026\n",
      "Modified mec:  maxPopen = 0.89607;  EC50 = 159.87 microM;  nH = 2.2113\n",
      "EC50 ratio:  1.0249816223304788\n"
     ]
    },
    {
     "data": {
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/IfX6oUruIkehSkbdmFl/M1thZl+Y2fASznnUzFaZ2Wdmdlp5A5LYZWVlBfr7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\nU6IXEUlySvQiIklOiV5EJMn9PznIUYNyotcGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d55160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(c0, pe0)\n",
    "print('Original mec: ', popen.print_pars(mec0, tres))\n",
    "for newrate in [1300, 130, 13]:\n",
    "    mec.Rates[7].rateconstants = newrate\n",
    "    c, pe, pi = scpl.Popen(mec, tres)\n",
    "    ax.semilogx(c, pe, 'r-')\n",
    "    print('Modified mec: ', popen.print_pars(mec, tres))\n",
    "    print('EC50 ratio: ', popen.EC50(mec, tres) / popen.EC50(mec0, tres))\n",
    "    #print('maxPopen ratio: ', ratio_maxPopen(mec, mec0, tres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Effect of changing $\\delta_2$ only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original mec:  maxPopen = 0.96095;  EC50 = 155.97 microM;  nH = 2.2392\n",
      "Modified mec:  maxPopen = 0.80781;  EC50 = 340.63 microM;  nH = 1.8899\n",
      "EC50 ratio:  2.183880441548357\n",
      "Modified mec:  maxPopen = 0.462;  EC50 = 616.98 microM;  nH = 1.8232\n",
      "EC50 ratio:  3.955707969597024\n",
      "Modified mec:  maxPopen = 0.24724;  EC50 = 599.03 microM;  nH = 1.6123\n",
      "EC50 ratio:  3.840590995128968\n"
     ]
    },
    {
     "data": {
      "image/png": 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K5PVZulQyYy5bJsdly8JVV0mrVk0SC110EVSuLMUwypQJ29WukjuBKPrKwM4M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\nXlEUJcJRRa8oihLh/D+vDN6yOQ4sZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7d55d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(c0, pe0)\n",
    "print('Original mec: ', popen.print_pars(mec0, tres))\n",
    "for newrate in [1550, 155, 15.5]:\n",
    "    mec.Rates[11].rateconstants = newrate\n",
    "    c, pe, pi = scpl.Popen(mec, tres)\n",
    "    ax.semilogx(c, pe, 'r-')\n",
    "    print('Modified mec: ', popen.print_pars(mec, tres))\n",
    "    print('EC50 ratio: ', popen.EC50(mec, tres) / popen.EC50(mec0, tres))\n",
    "    #print('maxPopen ratio: ', ratio_maxPopen(mec, mec0, tres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Effect of changing $\\delta_1$, $\\delta_2$ and $\\delta_3$ by same factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original mec:  maxPopen = 0.96095;  EC50 = 155.97 microM;  nH = 2.2392\n",
      "Modified mec:  maxPopen = 0.93652;  EC50 = 408.29 microM;  nH = 2.1867\n",
      "EC50 ratio:  2.6177162504112865\n",
      "Modified mec:  maxPopen = 0.74715;  EC50 = 1150.4 microM;  nH = 1.724\n",
      "EC50 ratio:  7.375463304078516\n",
      "Modified mec:  maxPopen = 0.24724;  EC50 = 2491.5 microM;  nH = 1.373\n",
      "EC50 ratio:  15.973801352874712\n"
     ]
    },
    {
     "data": {
      "image/png": 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S+OfM5MmyImvcOIeFIl02d9wh0zlr13ZbdrQEg5KE7e23ZfFSly7www9w6qle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\nJAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7dc62e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(c0, pe0)\n",
    "print('Original mec: ', popen.print_pars(mec0, tres))\n",
    "for nr1, nr2, nr3 in zip([1300, 130, 13], [1550, 155, 15.5], [10, 1, 0.1]):\n",
    "    mec.Rates[7].rateconstants = nr1 # delta3\n",
    "    mec.Rates[11].rateconstants = nr2 # delta2\n",
    "    mec.Rates[15].rateconstants = nr3 # delta1\n",
    "    c, pe, pi = scpl.Popen(mec, tres)\n",
    "    ax.semilogx(c, pe, 'r-')\n",
    "    print('Modified mec: ', popen.print_pars(mec, tres))\n",
    "    print('EC50 ratio: ', popen.EC50(mec, tres) / popen.EC50(mec0, tres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot macroscopic response to an exponentially decaying concentration jump "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reduce all, delta3, delta2 and delta1, 100 times\n",
    "mec.Rates[7].rateconstants = 133 # delta3\n",
    "mec.Rates[11].rateconstants = 155 # delta2\n",
    "mec.Rates[15].rateconstants = 1 # delta1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmax = 3e-3 # max concentrations\n",
    "cb = 0.0 # basal concentrations\n",
    "prepulse = 5e-3 # time before pulse starts\n",
    "tdec = 2.5e-3 # decay time constant\n",
    "cjargs = (cmax, cb, prepulse, tdec)\n",
    "\n",
    "cjlen = 0.05 # Record length\n",
    "cjstep = 5e-6 # Sampling step\n",
    "cjfunc = cjumps.pulse_instexp\n",
    "\n",
    "t, c, Popen, P  = cjumps.solve_jump(mec, cjlen, cjstep, cjfunc, cjargs)\n",
    "maxP, maxC = max(Popen), max(c)\n",
    "c1 = (c / maxC) * 0.2 * maxP + 1.02 * maxP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peak Popen =  0.128636737731\n"
     ]
    },
    {
     "data": {
      "image/png": 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DQwOr3r6qdmPHcgF6AW+HvB8L3O1lTDFq1ynAypD364D8wOsmwDqvY4xhW/8J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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7dee588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax  = plt.subplots(1,1, figsize=(5,3))\n",
    "ax.plot(t * 1000, Popen,'b-', t * 1000, c1, 'g-')\n",
    "print(\"Peak Popen = \", max(Popen))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#fout = open('trace.txt','w')\n",
    "#for i in range(t.shape[0]):\n",
    "#    fout.write('{0:.6e}\\t{1:.6e}\\n'.format(t[i], Popen[i]))\n",
    "#fout.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Effect of changing $\\gamma_3$ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original mec:  maxPopen = 0.96095;  EC50 = 155.97 microM;  nH = 2.2392\n",
      "Modified mec:  maxPopen = 0.93758;  EC50 = 408.29 microM;  nH = 2.1865\n",
      "EC50 ratio:  2.6177162504112865\n",
      "Modified mec:  maxPopen = 0.96148;  EC50 = 154.09 microM;  nH = 2.2307\n",
      "EC50 ratio:  0.9879154078549848\n",
      "Modified mec:  maxPopen = 0.96394;  EC50 = 58.413 microM;  nH = 1.9668\n",
      "EC50 ratio:  0.3745053905697899\n"
     ]
    },
    {
     "data": {
      "image/png": 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0f/q/UNZe0EGD4M03sbfQvRD+xaOTiZVS9wG9gXs8+bnCg7SG11+H//s/uxuL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\nKOIk0QshRBEniV4IIYq4/wdvRsHTEF541wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9a65940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(c0, pe0)\n",
    "print('Original mec: ', popen.print_pars(mec0, tres))\n",
    "for nr1, nr2, nr3 in zip([160, 16, 1.6], [1150, 115, 11.5], [50, 5, 0.5]):\n",
    "    mec.Rates[6].rateconstants = nr1 # gamma3\n",
    "    mec.Rates[10].rateconstants = nr2 # gamma2\n",
    "    mec.Rates[14].rateconstants = nr3 # gamma1\n",
    "    c, pe, pi = scpl.Popen(mec, tres)\n",
    "    ax.semilogx(c, pe, 'r-')\n",
    "    print('Modified mec: ', popen.print_pars(mec, tres))\n",
    "    print('EC50 ratio: ', popen.EC50(mec, tres) / popen.EC50(mec0, tres))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Changing $\\beta$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original mec:  maxPopen = 0.96095;  EC50 = 155.97 microM;  nH = 2.2392\n",
      "Modified mec:  maxPopen = 0.78155;  EC50 = 167.64 microM;  nH = 1.8393\n",
      "EC50 ratio:  1.0747922560995244\n",
      "Modified mec:  maxPopen = 0.28814;  EC50 = 356.14 microM;  nH = 1.5457\n",
      "EC50 ratio:  2.283336437402545\n",
      "Modified mec:  maxPopen = 0.039453;  EC50 = 438.09 microM;  nH = 1.4588\n",
      "EC50 ratio:  2.808790429273424\n"
     ]
    },
    {
     "data": {
      "image/png": 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DGbNnQ8eOsY+jury3BFyStKvabtoEOTn2F1GbNtC6te3vtRc0b25vsera1bYl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\nkpsVcQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7df3320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,3))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogx(c0, pe0)\n",
    "print('Original mec: ', popen.print_pars(mec0, tres))\n",
    "for nr1, nr2, nr3 in zip([2800, 280, 28], [1800, 180, 18], [150, 15, 1.5]):\n",
    "    mec.Rates[5].rateconstants = nr1 # beta3\n",
    "    mec.Rates[3].rateconstants = nr2 # beta2\n",
    "    mec.Rates[1].rateconstants = nr3 # beta1\n",
    "    c, pe, pi = scpl.Popen(mec, tres)\n",
    "    ax.semilogx(c, pe, 'r-')\n",
    "    print('Modified mec: ', popen.print_pars(mec, tres))\n",
    "    print('EC50 ratio: ', popen.EC50(mec, tres) / popen.EC50(mec0, tres))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}