--- a/COBRAxy/metabolicModel2Tabular.py	Wed Oct 01 11:15:00 2025 +0000
+++ b/COBRAxy/metabolicModel2Tabular.py	Wed Oct 01 13:19:03 2025 +0000
@@ -332,8 +332,7 @@
     medium = modelUtils.get_medium(model)
     objective_function = modelUtils.extract_objective_coefficients(model)
     
-    if ARGS.name == "ENGRO2":
-        compartments = modelUtils.generate_compartments(model)
+    compartments = modelUtils.generate_compartments(model)
 
     df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "GPR"])
     df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Formula"])
@@ -351,7 +350,7 @@
     merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
     merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
     merged = merged.merge(objective_function, on = "ReactionID", how = "outer")
-    if ARGS.name == "ENGRO2": 
+    if compartments is not None: 
         merged = merged.merge(compartments, on = "ReactionID", how = "outer")
     merged = merged.merge(df_medium, on = "ReactionID", how = "left")
     

--- a/COBRAxy/ras_to_bounds.py	Wed Oct 01 11:15:00 2025 +0000
+++ b/COBRAxy/ras_to_bounds.py	Wed Oct 01 13:19:03 2025 +0000
@@ -178,10 +178,7 @@
             bounds = modelUtils.generate_bounds(model)
             medium = modelUtils.get_medium(model)
             
-            try:
-                compartments = modelUtils.generate_compartments(model)
-            except:
-                compartments = None
+            compartments = modelUtils.generate_compartments(model)
 
             df_rules = pd.DataFrame(list(rules.items()), columns = ["ReactionID", "Rule"])
             df_reactions = pd.DataFrame(list(reactions.items()), columns = ["ReactionID", "Reaction"])
@@ -191,9 +188,8 @@
 
             merged = df_reactions.merge(df_rules, on = "ReactionID", how = "outer")
             merged = merged.merge(df_bounds, on = "ReactionID", how = "outer")
-            
-            # Add compartments only if they exist and model name is ENGRO2
-            if compartments is not None and hasattr(ARGS, 'name') and ARGS.name == "ENGRO2": 
+            # Add compartments only if they exist
+            if compartments is not None:
                 merged = merged.merge(compartments, on = "ReactionID", how = "outer")
             
             merged = merged.merge(df_medium, on = "ReactionID", how = "left")

--- a/COBRAxy/utils/model_utils.py	Wed Oct 01 11:15:00 2025 +0000
+++ b/COBRAxy/utils/model_utils.py	Wed Oct 01 13:19:03 2025 +0000
@@ -223,37 +223,54 @@
 
 def generate_compartments(model: cobraModel) -> pd.DataFrame:
     """
-    Generates a DataFrame containing compartment information for each reaction.
-    Creates columns for each compartment position (Compartment_1, Compartment_2, etc.)
+    Generates a DataFrame containing pathway information for each reaction.
+    Creates columns for each pathway position (Pathway_1, Pathway_2, etc.) only if pathways exist.
     
     Args:
-        model: the COBRA model to extract compartment data from.
+        model: the COBRA model to extract pathway data from.
         
     Returns:
-        pd.DataFrame: DataFrame with ReactionID and compartment columns
+        pd.DataFrame: DataFrame with ReactionID and pathway columns (if any pathways exist)
     """
     pathway_data = []
 
     # First pass: determine the maximum number of pathways any reaction has
     max_pathways = 0
     reaction_pathways = {}
+    has_any_pathways = False
 
     for reaction in model.reactions:
         # Get unique pathways from all metabolites in the reaction
-        if 'pathways' in reaction.annotation:
+        if 'pathways' in reaction.annotation and reaction.annotation['pathways']:
             if type(reaction.annotation['pathways']) == list:
-                reaction_pathways[reaction.id] = reaction.annotation['pathways']
-                max_pathways = max(max_pathways, len(reaction.annotation['pathways']))
+                # Filter out empty/None values
+                valid_pathways = [p for p in reaction.annotation['pathways'] if p]
+                if valid_pathways:
+                    reaction_pathways[reaction.id] = valid_pathways
+                    max_pathways = max(max_pathways, len(valid_pathways))
+                    has_any_pathways = True
+                else:
+                    reaction_pathways[reaction.id] = []
             else:
-                reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
+                # Single pathway value
+                if reaction.annotation['pathways']:
+                    reaction_pathways[reaction.id] = [reaction.annotation['pathways']]
+                    max_pathways = max(max_pathways, 1)
+                    has_any_pathways = True
+                else:
+                    reaction_pathways[reaction.id] = []
         else:
             # No pathway annotation - use empty list
             reaction_pathways[reaction.id] = []
 
-    # Create column names for pathways
+    # If no pathways exist in the model, return DataFrame with only ReactionID
+    if not has_any_pathways:
+        return None
+
+    # Create column names for pathways only if they exist
     pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)]
 
-    # Second pass: create the data
+    # Second pass: create the data with pathway columns
     for reaction_id, pathways in reaction_pathways.items():
         row = {"ReactionID": reaction_id}
         
@@ -263,7 +280,7 @@
             if i < len(pathways):
                 row[col_name] = pathways[i]
             else:
-                row[col_name] = None  # or "" if you prefer empty strings
+                row[col_name] = None
 
         pathway_data.append(row)
 
@@ -289,7 +306,7 @@
                 pathways.append(str(row[col]).strip())
         
         if pathways:
-            
+
             reaction = model.reactions.get_by_id(reaction_id)
             if len(pathways) == 1:
                 reaction.annotation['pathways'] = pathways[0]