Mercurial > repos > bimib > cobraxy

comparison COBRAxy/ras_to_bounds.py @ 216:b162b98f9de5 draft

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author luca_milaz
date Fri, 13 Dec 2024 18:46:12 +0000
parents 00a66b9bc29e
children 8d1988935e1f
comparison
equal deleted inserted replaced
215:5cc4a367ef70 216:b162b98f9de5
114 if len(dataset.columns) < 2: 114 if len(dataset.columns) < 2:
115 sys.exit('Execution aborted: wrong format of ' + name + '\n') 115 sys.exit('Execution aborted: wrong format of ' + name + '\n')
116 return dataset 116 return dataset
117 117
118 118
119 def apply_ras_bounds(model, ras_row): 119 def apply_ras_bounds(bounds, ras_row):
120 """ 120 """
121 Adjust the bounds of reactions in the model based on RAS values. 121 Adjust the bounds of reactions in the model based on RAS values.
122 122
123 Args: 123 Args:
124 model (cobra.Model): The metabolic model to be modified. 124 bounds (pd.DataFrame): Model bounds.
125 ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds. 125 ras_row (pd.Series): A row from a RAS DataFrame containing scaling factors for reaction bounds.
126 Returns: 126 Returns:
127 None 127 new_bounds (pd.DataFrame): integrated bounds.
128 """ 128 """
129 new_bounds = bounds.copy()
129 for reaction in ras_row.index: 130 for reaction in ras_row.index:
130 scaling_factor = ras_row[reaction] 131 scaling_factor = ras_row[reaction]
131 lower_bound=model.reactions.get_by_id(reaction).lower_bound 132 lower_bound=bounds.loc[reaction, "lower_bound"]
132 upper_bound=model.reactions.get_by_id(reaction).upper_bound 133 upper_bound=bounds.loc[reaction, "upper_bound"]
133 valMax=float((upper_bound)*scaling_factor) 134 valMax=float((upper_bound)*scaling_factor)
134 valMin=float((lower_bound)*scaling_factor) 135 valMin=float((lower_bound)*scaling_factor)
135 if upper_bound!=0 and lower_bound==0: 136 if upper_bound!=0 and lower_bound==0:
136 model.reactions.get_by_id(reaction).upper_bound=valMax 137 new_bounds.loc[reaction, "upper_bound"] = valMax
137 if upper_bound==0 and lower_bound!=0: 138 if upper_bound==0 and lower_bound!=0:
138 model.reactions.get_by_id(reaction).lower_bound=valMin 139 new_bounds.loc[reaction, "lower_bound"] = valMin
139 if upper_bound!=0 and lower_bound!=0: 140 if upper_bound!=0 and lower_bound!=0:
140 model.reactions.get_by_id(reaction).lower_bound=valMin 141 new_bounds.loc[reaction, "lower_bound"] = valMin
141 model.reactions.get_by_id(reaction).upper_bound=valMax 142 new_bounds.loc[reaction, "upper_bound"] = valMax
142 pass 143 return new_bounds
143 144
144 def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder): 145 def process_ras_cell(cellName, ras_row, model, rxns_ids, output_folder):
145 """ 146 """
146 Process a single RAS cell, apply bounds, and save the bounds to a CSV file. 147 Process a single RAS cell, apply bounds, and save the bounds to a CSV file.
147 148
153 output_folder (str): Folder path where the output CSV file will be saved. 154 output_folder (str): Folder path where the output CSV file will be saved.
154 155
155 Returns: 156 Returns:
156 None 157 None
157 """ 158 """
158 model_new = model.copy() 159 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
159 apply_ras_bounds(model_new, ras_row) 160 new_bounds = apply_ras_bounds(bounds, ras_row)
160 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"]) 161 new_bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
161 bounds.to_csv(output_folder + cellName + ".csv", sep='\t', index=True)
162 pass 162 pass
163 163
164 def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame: 164 def generate_bounds(model: cobra.Model, medium: dict, ras=None, output_folder='output/') -> pd.DataFrame:
165 """ 165 """
166 Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments. 166 Generate reaction bounds for a metabolic model based on medium conditions and optional RAS adjustments.
195 model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"]) 195 model.reactions.get_by_id(reaction).upper_bound = float(df_FVA.loc[reaction, "maximum"])
196 196
197 if ras is not None: 197 if ras is not None:
198 Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows()) 198 Parallel(n_jobs=cpu_count())(delayed(process_ras_cell)(cellName, ras_row, model, rxns_ids, output_folder) for cellName, ras_row in ras.iterrows())
199 else: 199 else:
200 model_new = model.copy() 200 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"])
201 apply_ras_bounds(model_new, pd.Series([1]*len(rxns_ids), index=rxns_ids)) 201 newBounds = apply_ras_bounds(bounds, pd.Series([1]*len(rxns_ids), index=rxns_ids))
202 bounds = pd.DataFrame([(rxn.lower_bound, rxn.upper_bound) for rxn in model_new.reactions], index=rxns_ids, columns=["lower_bound", "upper_bound"]) 202 newBounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
203 bounds.to_csv(output_folder + "bounds.csv", sep='\t', index=True)
204 pass 203 pass
205 204
206 205
207 206
208 ############################# main ########################################### 207 ############################# main ###########################################