mcPAM ready for parametrizer

Data/enzyme_sets.json

@@ -0,0 +1,146 @@
+{
+    "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
+            }
+        }
+    }
+}

Examples/mcpam_generator.py

@@ -10,15 +10,17 @@
 
 if __name__ == "__main__":
 
-    mcpam = set_up_ecolicore_mcpam(sensitivity=False)
-    pam = set_up_ecolicore_pam(sensitivity=False)
-    pam.optimize()
-    mcpam.optimize()
-    print("PAM objective value: ", pam.objective.value)
-    print("mcPAM objective value: ", mcpam.objective.value)
+    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="core")
+    run_simulations_pam_mcpam(models, type="full scale")
+
+    for protein, kcats in mcpam.sectors.get_by_id("MembraneSector").membrane_proteins.items():
+        print(protein, kcats)
+
+
 
 
 

Scripts/mcpam_generation_uniprot_id.py

@@ -41,7 +41,7 @@ def set_up_ecolicore_pam(total_protein:bool = True,
     #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='mcPAM_data_parametrized')
+        enzyme_db = pd.read_excel(PAM_DATA_FILE_PATH, sheet_name='mcPAM_data_core')
 
         for idx in enzyme_db.rxnID:
             # transport reactions
@@ -127,7 +127,7 @@ def set_up_ecolicore_mcpam(total_protein: Union[bool, float] = True,
     # 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')
+        enzyme_db = pd.read_excel(pam_info_file, sheet_name='mcPAM_data_core')
 
         for idx in enzyme_db.rxnID:
             # transport reactions
@@ -183,7 +183,7 @@ def set_up_ecolicore_mcpam(total_protein: Union[bool, float] = True,
 
     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')
+        active_enzyme_info = pd.read_excel(pam_info_file, sheet_name='mcPAM_data_core')
 
         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']

Scripts/mcpam_simulations_analysis.py

@@ -14,7 +14,8 @@
 from src.PAModelpy.MembraneSector import MembraneSector
 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_pam, set_up_ecolicore_mcpam,
+                                                 set_up_ecoli_pam, set_up_ecoli_mcpam)
 
 def compare_mu_for_different_sensitivities_ecolicore_pam():
     rxn2protein_new = {}
@@ -154,6 +155,12 @@ def build_ecolicore_pam_and_mcpam(sensitivity:bool=True, max_area: float = 0.1):
 
     return pam_core, mcpam_core
 
+def build_ecoli_pam_and_mcpam(sensitivity:bool=True, max_area: float = 0.1):
+    pam_core = set_up_ecoli_pam(sensitivity=sensitivity)
+    mcpam_core = set_up_ecoli_mcpam(sensitivity=sensitivity)
+
+    return pam_core, mcpam_core
+
 def set_objective_pam_mcpam(models:list, objective:str):
     for model in models:
         model.objective = objective
@@ -164,30 +171,34 @@ def optimize_pam_mcpam(models: list):
 
 def run_pam_mcpam_core_with_optimized_kcats(sensitivity:bool=True,
                                             enzyme_sets_name:str='enzyme_sets.json',
-                                            print_area:bool=False):
+                                            print_area:bool=False,
+                                            type:str='full scale'):
 
     # Resetting the configurations
     config = Config()
     config.reset()
 
     # Building the models
-    pam_core, mcpam_core = build_ecolicore_pam_and_mcpam(sensitivity=sensitivity)
+    if type == "full scale":
+        pam, mcpam =build_ecoli_pam_and_mcpam(sensitivity=sensitivity)
+    else:
+        pam, mcpam = build_ecolicore_pam_and_mcpam(sensitivity=sensitivity)
 
     # List of kcat values to be changed
     enzyme_path = os.path.join('Data', enzyme_sets_name)
     with open(enzyme_path, 'r') as json_file:
         enzyme_sets = json.load(json_file)
 
-    # Changing the solver, since problem with gurobi
-    pam_core.solver = 'glpk'
-    mcpam_core.solver = 'glpk'
-
     # Changing the kcats
-    models = [pam_core, mcpam_core]
+    models = [pam, mcpam]
     change_kcats_for_multiple_enzyme_sets(models, enzyme_sets)
 
     # Setting the objectives and Optimizing the models
-    BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'
+    if type == "full scale":
+        BIOMASS_REACTION = 'BIOMASS_Ec_iML1515_core_75p37M'
+    else:
+        BIOMASS_REACTION = 'BIOMASS_Ecoli_core_w_GAM'
+
     set_objective_pam_mcpam(models, BIOMASS_REACTION)
     optimize_pam_mcpam(models)
 
@@ -197,10 +208,10 @@ def run_pam_mcpam_core_with_optimized_kcats(sensitivity:bool=True,
 
     #Printing the occupied area for mcpam core
     if print_area:
-        occupied_area, available_area = mcpam_core.calculate_occupied_membrane()
+        occupied_area, available_area = mcpam.calculate_occupied_membrane()
         print(f'occupied area {occupied_area / available_area * 100}%')
 
-    return pam_core, mcpam_core
+    return pam, mcpam
 
 def run_simulations_pam_mcpam(models, print_area:bool=False, type:str="full scale"):
     fontsize = 25