--- a/COBRAxy/ras_to_bounds.py	Sun Oct 13 15:48:44 2024 +0000
+++ b/COBRAxy/ras_to_bounds.py	Sun Oct 13 15:55:16 2024 +0000
@@ -111,7 +111,7 @@
     return dataset
 
 
-def apply_ras_bounds(model, ras_row, rxns_ids):
+def apply_ras_bounds(model, ras_row, rxns_ids, mediumRxns_ids):
     """
     Adjust the bounds of reactions in the model based on RAS values.
 
@@ -119,12 +119,12 @@
         model (cobra.Model): The metabolic model to be modified.
         ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
         rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
-    
+        mediumRxns_ids (list of str): List of reaction IDs in the medium. Their RAS is set to zero, but they are already set in the model.
     Returns:
         None
     """
     for reaction in rxns_ids:
-        if reaction in ras_row.index:
+        if reaction in ras_row.index and ras_row[reaction] not in mediumRxns_ids:
             scaling_factor = ras_row[reaction]
             lower_bound=model.reactions.get_by_id(reaction).lower_bound
             upper_bound=model.reactions.get_by_id(reaction).upper_bound
@@ -139,7 +139,7 @@
                 model.reactions.get_by_id(reaction).upper_bound=valMax
     pass
 
-def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
+def process_ras_cell(cellName, ras_row, model, rxns_ids, mediumRxns_ids, output_folder):
     """
     Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
 
@@ -148,13 +148,14 @@
         ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
         model (cobra.Model): The metabolic model to be modified.
         rxns_ids (list of str): List of reaction IDs to which the scaling factors will be applied.
+        mediumRxns_ids (list of str): List of reaction IDs in the medium. Their RAS is set to zero, but they are already set in the model.
         output_folder (str): Folder path where the output CSV file will be saved.
     
     Returns:
         None
     """
     model_new = model.copy()
-    apply_ras_bounds(model_new, ras_row, rxns_ids)
+    apply_ras_bounds(model_new, ras_row, rxns_ids, mediumRxns_ids)
     bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
     bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
     pass
@@ -181,6 +182,8 @@
                 model.reactions.get_by_id(reaction).lower_bound = -float(value)
             if(reaction == "EX_lac__L_e"):
                 model.reactions.get_by_id(reaction).lower_bound = float(0.0)
+
+    mediumRxns_ids = medium.keys()
             
     # Perform Flux Variability Analysis (FVA)
     df_FVA = cobra.flux_analysis.flux_variability_analysis(model, fraction_of_optimum=0, processes=1).round(8)
@@ -190,16 +193,16 @@
         model.reactions.get_by_id(reaction).lower_bound = float(df_FVA.loc[reaction, "minimum"])
         model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])
 
-    warning(str(model.reactions.get_by_id("EX_Lcystin_e").lower_bound))
+    #warning(str(model.reactions.get_by_id("EX_Lcystin_e").lower_bound))
 
     if ras is not None:
         #Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
         for cellName, ras_row in ras.iterrows():
-            process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder)
+            process_ras_cell(cellName, ras_row, model, rxns_ids, mediumRxns_ids, output_folder)
             break #just one cell for testing
     else:
         model_new = model.copy()
-        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids)
+        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids), rxns_ids, mediumRxns_ids)
         bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
         bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
     pass