enabled sensitivity analysis for renewed model structure

src/PAModelpy/PAModel.py

@@ -1001,8 +1001,35 @@ def calculate_csc(self, obj_value, mu, mu_ub, mu_lb, mu_ec_f, mu_ec_b):
                 len(self.capacity_sensitivity_coefficients)
             ] = new_row_LB
 
+        print(self.enzymes)
         for enzyme in self.enzymes:
-            self.calculate_enzyme_csc(enzyme, mu_ec_f, mu_ec_b, obj_value)
+            for catalyzing_enzyme in self._get_catalyzing_enzymes_for_enzyme(enzyme):
+                ce = self.enzymes.get_by_id(catalyzing_enzyme)
+                self.calculate_enzyme_csc(ce, mu_ec_f, mu_ec_b, obj_value)
+
+    def _get_catalyzing_enzymes_for_enzyme(self, enzyme:Union[Enzyme, str])-> list:
+        """ Retrieves those enzymes which are associated with a constraint
+
+        Enzymes which are only present in an enzyme complex are thus ignored
+
+        Args:
+            enzyme: Enzyme object or enzyme identifier
+
+        Returns:
+
+        """
+        if isinstance(enzyme, Enzyme):
+            enzyme_id = enzyme.id
+        else:
+            enzyme_id = enzyme
+
+        #get all associated constraints
+        associated_constraints = ["_".join(cid.split("_")[:-1]) for cid in self.constraints.keys() if (enzyme_id in cid)&("_max" in cid)] # enzymes always have a min and max constraint
+        #remove the constraints associated with a reaction
+        associated_enzymes = [cid for cid in associated_constraints if not any([rxn.id in cid for rxn in self.reactions])]
+        # removing duplicates and empty strings
+        associated_enzymes = list(set([enz for enz in associated_enzymes if len(enz)>0]))
+        return associated_enzymes
 
     def calculate_csc_for_molecule(self, molecule: Union[Enzyme],
                                    mu_min:pd.DataFrame, mu_max:pd.DataFrame, obj_value:float,
@@ -1079,27 +1106,29 @@ def calculate_esc(self, obj_value, mu_ec_f, mu_ec_b):
 
         # calculate enzyme sensitivity coefficient
         for enzyme in self.enzymes:
-            # get the reactions associated with the enzyme
-            reactions = ",".join(self.get_reactions_with_enzyme_id(enzyme.id))
-
-            # get the right row from the shadow price dataframes
-            sp_ec_f = mu_ec_f[mu_ec_f["rxn_id"] == f"EC_{enzyme.id}"][
-                "shadow_prices"
-            ].iloc[0]
-            sp_ec_b = mu_ec_b[mu_ec_b["rxn_id"] == f"EC_{enzyme.id}"][
-                "shadow_prices"
-            ].iloc[0]
-            e_fwd = self.enzyme_variables.get_by_id(enzyme.id).forward_variable.primal
-            e_rev = self.enzyme_variables.get_by_id(enzyme.id).reverse_variable.primal
-
-            # EC: enzyme constraint
-            enzyme_sensitivity_coefficient = (
-                e_fwd * sp_ec_f + e_rev * sp_ec_b
-            ) / obj_value
-            # add new_row to dataframe
-            self.enzyme_sensitivity_coefficients.loc[
-                len(self.enzyme_sensitivity_coefficients)
-            ] = [reactions, enzyme.id, enzyme_sensitivity_coefficient]
+            for catalyzing_enzyme in self._get_catalyzing_enzymes_for_enzyme(enzyme):
+
+                # get the reactions associated with the enzyme
+                reactions = ",".join(self.get_reactions_with_enzyme_id(catalyzing_enzyme))
+
+                # get the right row from the shadow price dataframes
+                sp_ec_f = mu_ec_f[mu_ec_f["rxn_id"] == f"EC_{catalyzing_enzyme}"][
+                    "shadow_prices"
+                ].iloc[0]
+                sp_ec_b = mu_ec_b[mu_ec_b["rxn_id"] == f"EC_{catalyzing_enzyme}"][
+                    "shadow_prices"
+                ].iloc[0]
+                e_fwd = self.enzyme_variables.get_by_id(catalyzing_enzyme).forward_variable.primal
+                e_rev = self.enzyme_variables.get_by_id(catalyzing_enzyme).reverse_variable.primal
+
+                # EC: enzyme constraint
+                enzyme_sensitivity_coefficient = (
+                    e_fwd * sp_ec_f + e_rev * sp_ec_b
+                ) / obj_value
+                # add new_row to dataframe
+                self.enzyme_sensitivity_coefficients.loc[
+                    len(self.enzyme_sensitivity_coefficients)
+                ] = [reactions, catalyzing_enzyme, enzyme_sensitivity_coefficient]
 
     def calculate_sum_of_enzymes(self):
         """

tests/unit_tests/test_pamodel/test_pam_generation.py

@@ -101,12 +101,23 @@ def test_if_toy_pam_with_enzyme_comples_has_same_growth_rate_as_without():
 
     assert sut.objective.value == pytest.approx(toy_pam.objective.value, abs = 1e-6)
 
-# def test_set_up_ecolicore_pam_works():
-#     sut = set_up_ecolicore_pam()
-#     assert True
-# def test_set_up_ecoli_pam_works():
-#     sut = set_up_ecoli_pam()
-#     assert True
+def test_set_up_ecolicore_pam_works():
+    sut = set_up_ecolicore_pam()
+    sut.optimize()
+    assert True
+def test_if_ecolicore_pam_optimizes():
+    sut = set_up_ecolicore_pam()
+    sut.optimize()
+    assert sut.objective.value > 0
+
+def test_set_up_ecoli_pam_works():
+    sut = set_up_ecoli_pam()
+    assert True
+
+def test_if_ecoli_pam_optimizes():
+    sut = set_up_ecoli_pam()
+    sut.optimize()
+    assert sut.objective.value > 01
 
 #########################################################################################################################
 # HELPER FUNCTIONS

tests/unit_tests/test_pamodel/test_pamodel.py

@@ -2,6 +2,7 @@
 from cobra.io import load_json_model
 
 from src.PAModelpy import PAModel,Config,ActiveEnzymeSector, UnusedEnzymeSector, TransEnzymeSector
+from tests.unit_tests.test_pamodel.test_pam_generation import set_up_toy_pam_with_isozymes_and_enzymecomplex
 
 def test_if_pamodel_change_kcat_function_works():
     #arrange
@@ -155,6 +156,18 @@ def test_if_pamodel_remove_sectors_can_remove_translational_protein_sector():
     assert toy_pam.constraints[toy_pam.TOTAL_PROTEIN_CONSTRAINT_ID].ub == tpc_ub + sector.intercept
     assert all(coeff == 0 for coeff in lin_coeff.values())
 
+def test_if_pamodel_gets_catalyzing_enzymes_for_enzyme_object():
+    # Arrange
+    sut = set_up_toy_pam_with_isozymes_and_enzymecomplex(sensitivity = False)
+    enzyme_ut = 'E10'
+    associated_enzymes = ['E10', 'E3_E10_E11']
+
+    # Assert
+    catalyzing_enzymes = sut._get_catalyzing_enzymes_for_enzyme(enzyme_ut)
+
+    # Assert
+    assert all(enz in catalyzing_enzymes for enz in associated_enzymes)
+
 #######################################################################################################
 #HELPER METHODS
 #######################################################################################################