adjusted setup method+docs to include membrane sector

docs/PAM_setup_guide.md

@@ -68,14 +68,40 @@ pip install cobra PAModelpy
 | `ups_mu`           | Change in unused enzyme fraction per unit change of the associated reaction.    |
 | `mol_mass`         | Molar mass of unused enzymes.  [kDa]                                            |
 
+#### Membrane Sheet
+*Only required if you are building a membrane-constrained PAM (mcPAM)*
+
+| **Parameter**      | **Description**                                                                                  |
+|---------------------|--------------------------------------------------------------------------------------------------|
+| `id_list`          | Identifier related to protein fraction associated with the available membrane area.              |
+| `area_avail_0`     | Available membrane area at zero growth [μm^{2}].                                             |
+| `ups_mu`           | Increase in available membrane surface area per unit increase of the growth rate [μm^{2}/h]. |
+
+
+#### MembraneEnzyme Sheet
+*Only required if you are building a membrane-constrained PAM (mcPAM)*
+
+| **Column**     | **Description**                                  |
+|----------------|--------------------------------------------------|
+| `rxn_id`       | Reaction ID from the genome-scale model.         |
+| `enzyme_id`    | Unique enzyme identifier*.                       |
+| `alpha_number` | Number of alpha helices embedded in the membrane. |
+| `location`     | Cellular location of the enzyme**.               |
+
+
+* A unique enzyme identifier is defined as a single identifier per catalytically active unit. This means that an enzyme complex has a single identifier.
+* Enzyme-complex identifiers can be parsed from peptide identifiers and gene-to-protein mapping using the `merge_enzyme_complexes` function.
+** Either 'Cell membrane', 'Cytoplasm', or 'Unknown'
+
 ---
 
 ## Building a PAM
 
 ### Steps to Create a PAM
 
 1. Prepare the genome-scale model and parameter file.
-2. Use the `set_up_pam` function to initialize the model.
+2. Optionally: configure the Config object to match the identifiers of your model (see example 2)
+3. Use the `set_up_pam` function to initialize the model.
 
 #### Example Usage
 ```python
@@ -96,11 +122,15 @@ pam = set_up_pam(pam_info_file=param_file,
                  adjust_reaction_ids=False)
 ```
 
-3. Optimize the PAM:
+4. Optimize the PAM:
 ```python
 pam.optimize()
 print(f"Objective Value: {pam.objective.value}")
 ```
+### Creating a mcPAM
+To create a mcPAM, you can use the example as described above. The only alteration is that in the 
+`set_up_pam` function, you'll need to set the `membrane_sector` parameter to `True`. Optionally, you can 
+define the fraction of membrane area available for active enzymes using `max_membrane_area`.
 
 ---
 

src/PAModelpy/utils/pam_generation.py

@@ -349,11 +349,11 @@ 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 = None,
-               translational_enzymes: bool = None,
-               unused_enzymes: bool = None,
-               membrane_sector: bool = None,
-               max_area:float = 0.03,
+               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 = False) -> PAModel:
@@ -384,31 +384,36 @@ def set_up_pam(pam_info_file:str = '',
                 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_sector = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene,
+        active_enzyme_info = ActiveEnzymeSector(rxn2protein=rxn2protein, protein2gene=protein2gene,
                                                   configuration=config)
-
+    else:
+        active_enzyme_info = None
 
     if translational_enzymes:
         translational_info = pd.read_excel(pam_info_file, sheet_name='Translational')
-        translation_enzyme_sector = TransEnzymeSector(
+        translation_enzyme_info = 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_info = None
 
     if unused_enzymes:
         unused_protein_info = pd.read_excel(pam_info_file, sheet_name='UnusedEnzyme').set_index('Parameter')
 
         ups_0 = unused_protein_info.at['ups_0', 'Value']
         ups_mu = unused_protein_info.at['ups_mu', 'Value']
 
-        unused_protein_sector = UnusedEnzymeSector(
+        unused_enzyme_info = UnusedEnzymeSector(
             id_list=[unused_protein_info.at['id_list', 'Value']],
             ups_mu=[ups_mu],
             ups_0=[ups_0],
             mol_mass=[unused_protein_info.at['mol_mass', 'Value']],
             configuration = config)
+    else:
+        unused_enzyme_info = None
 
     if membrane_sector:
         membrane_info = pd.read_excel(pam_info_file, sheet_name='Membrane').set_index('Parameter')
@@ -417,21 +422,24 @@ def set_up_pam(pam_info_file:str = '',
         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()
+        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_area)
+                                         max_area=max_membrane_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_protein_sector,
+                       active_sector=active_enzyme_info,
+                      translational_sector=translation_enzyme_info,
+                       unused_sector=unused_enzyme_info,
                       membrane_sector=membrane_sector,
                       sensitivity=sensitivity, configuration = config
                       )