--- a/COBRAxy/ras_to_bounds.py	Fri Dec 13 11:06:12 2024 +0000
+++ b/COBRAxy/ras_to_bounds.py	Fri Dec 13 18:46:12 2024 +0000
@@ -116,30 +116,31 @@
     return dataset
 
 
-def apply_ras_bounds(model, ras_row):
+def apply_ras_bounds(bounds, ras_row):
     """
     Adjust the bounds of reactions in the model based on RAS values.
 
     Args:
-        model (cobra.Model): The metabolic model to be modified.
+        bounds (pd.DataFrame): Model bounds.
         ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
     Returns:
-        None
+        new_bounds (pd.DataFrame): integrated bounds.
     """
+    new_bounds = bounds.copy()
     for reaction in ras_row.index:
         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
+        lower_bound=bounds.loc[reaction, "lower_bound"]
+        upper_bound=bounds.loc[reaction, "upper_bound"]
         valMax=float((upper_bound)*scaling_factor)
         valMin=float((lower_bound)*scaling_factor)
         if upper_bound!=0 and lower_bound==0:
-            model.reactions.get_by_id(reaction).upper_bound=valMax
+            new_bounds.loc[reaction, "upper_bound"] = valMax
         if upper_bound==0 and lower_bound!=0:
-            model.reactions.get_by_id(reaction).lower_bound=valMin
+            new_bounds.loc[reaction, "lower_bound"] = valMin
         if upper_bound!=0 and lower_bound!=0:
-            model.reactions.get_by_id(reaction).lower_bound=valMin
-            model.reactions.get_by_id(reaction).upper_bound=valMax
-    pass
+            new_bounds.loc[reaction, "lower_bound"] = valMin
+            new_bounds.loc[reaction, "upper_bound"] = valMax
+    return new_bounds
 
 def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
     """
@@ -155,10 +156,9 @@
     Returns:
         None
     """
-    model_new = model.copy()
-    apply_ras_bounds(model_new, ras_row)
-    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)
+    bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
+    new_bounds = apply_ras_bounds(bounds, ras_row)
+    new_bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
     pass
 
 def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame:
@@ -197,10 +197,9 @@
     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())
     else:
-        model_new = model.copy()
-        apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_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)
+        bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
+        newBounds = apply_ras_bounds(bounds, pd.Series([1]*len(rxns_ids), index=rxns_ids))
+        newBounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
     pass