Mercurial > repos > bimib > cobraxy
diff COBRAxy/utils/general_utils.py @ 408:f413b78d61bf draft
Uploaded
author | francesco_lapi |
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date | Mon, 08 Sep 2025 17:12:35 +0000 |
parents | a0b53ccc73a8 |
children | 71850bdf9e1e |
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--- a/COBRAxy/utils/general_utils.py Mon Sep 08 16:52:46 2025 +0000 +++ b/COBRAxy/utils/general_utils.py Mon Sep 08 17:12:35 2025 +0000 @@ -7,10 +7,11 @@ from enum import Enum from itertools import count -from typing import Any, Callable, Dict, Generic, List, Literal, Optional, TypeVar, Union +from typing import Any, Callable, Dict, Generic, List, Literal, Optional, TypeVar, Union, Set, Tuple import pandas as pd import cobra +from cobra import Model, Reaction, Metabolite import zipfile import gzip @@ -713,4 +714,270 @@ return -1 rename_genes(model2,dict_genes) - return model2 \ No newline at end of file + return model2 + + +def build_cobra_model_from_csv(csv_path: str, model_id: str = "ENGRO2_custom") -> cobra.Model: + """ + Costruisce un modello COBRApy a partire da un file CSV con i dati delle reazioni. + + Args: + csv_path: Path al file CSV (separato da tab) + model_id: ID del modello da creare + + Returns: + cobra.Model: Il modello COBRApy costruito + """ + + # Leggi i dati dal CSV + df = pd.read_csv(csv_path, sep='\t') + + # Crea il modello vuoto + model = Model(model_id) + + # Dict per tenere traccia di metaboliti e compartimenti + metabolites_dict = {} + compartments_dict = {} + + print(f"Costruendo modello da {len(df)} reazioni...") + + # Prima passata: estrai metaboliti e compartimenti dalle formule delle reazioni + for idx, row in df.iterrows(): + reaction_formula = str(row['Reaction']).strip() + if not reaction_formula or reaction_formula == 'nan': + continue + + # Estrai metaboliti dalla formula della reazione + metabolites = extract_metabolites_from_reaction(reaction_formula) + + for met_id in metabolites: + compartment = extract_compartment_from_metabolite(met_id) + + # Aggiungi compartimento se non esiste + if compartment not in compartments_dict: + compartments_dict[compartment] = compartment + + # Aggiungi metabolita se non esiste + if met_id not in metabolites_dict: + metabolites_dict[met_id] = Metabolite( + id=met_id, + compartment=compartment, + name=met_id.replace(f"_{compartment}", "").replace("__", "_") + ) + + # Aggiungi compartimenti al modello + model.compartments = compartments_dict + + # Aggiungi metaboliti al modello + model.add_metabolites(list(metabolites_dict.values())) + + print(f"Aggiunti {len(metabolites_dict)} metaboliti e {len(compartments_dict)} compartimenti") + + # Seconda passata: aggiungi le reazioni + reactions_added = 0 + reactions_skipped = 0 + + for idx, row in df.iterrows(): + try: + reaction_id = str(row['ReactionID']).strip() + reaction_formula = str(row['Reaction']).strip() + + # Salta reazioni senza formula + if not reaction_formula or reaction_formula == 'nan': + reactions_skipped += 1 + continue + + # Crea la reazione + reaction = Reaction(reaction_id) + reaction.name = reaction_id + + # Imposta bounds + reaction.lower_bound = float(row['lower_bound']) if pd.notna(row['lower_bound']) else -1000.0 + reaction.upper_bound = float(row['upper_bound']) if pd.notna(row['upper_bound']) else 1000.0 + + # Aggiungi gene rule se presente + if pd.notna(row['Rule']) and str(row['Rule']).strip(): + reaction.gene_reaction_rule = str(row['Rule']).strip() + + # Parse della formula della reazione + try: + parse_reaction_formula(reaction, reaction_formula, metabolites_dict) + except Exception as e: + print(f"Errore nel parsing della reazione {reaction_id}: {e}") + reactions_skipped += 1 + continue + + # Aggiungi la reazione al modello + model.add_reactions([reaction]) + reactions_added += 1 + + except Exception as e: + print(f"Errore nell'aggiungere la reazione {reaction_id}: {e}") + reactions_skipped += 1 + continue + + print(f"Aggiunte {reactions_added} reazioni, saltate {reactions_skipped} reazioni") + + # Imposta l'obiettivo di biomassa + set_biomass_objective(model) + + # Imposta il medium + set_medium_from_data(model, df) + + print(f"Modello completato: {len(model.reactions)} reazioni, {len(model.metabolites)} metaboliti") + + return model + + +# Estrae tutti gli ID metaboliti nella formula (gestisce prefissi numerici + underscore) +def extract_metabolites_from_reaction(reaction_formula: str) -> Set[str]: + """ + Estrae gli ID dei metaboliti da una formula di reazione. + Pattern robusto: cattura token che terminano con _<compartimento> (es. _c, _m, _e) + e permette che comincino con cifre o underscore. + """ + metabolites = set() + # coefficiente opzionale seguito da un token che termina con _<letters> + pattern = r'(?:\d+(?:\.\d+)?\s+)?([A-Za-z0-9_]+_[a-z]+)' + matches = re.findall(pattern, reaction_formula) + metabolites.update(matches) + return metabolites + + +def extract_compartment_from_metabolite(metabolite_id: str) -> str: + """ + Estrae il compartimento dall'ID del metabolita. + """ + # Il compartimento รจ solitamente l'ultima lettera dopo l'underscore + if '_' in metabolite_id: + return metabolite_id.split('_')[-1] + return 'c' # default cytoplasm + + +def parse_reaction_formula(reaction: Reaction, formula: str, metabolites_dict: Dict[str, Metabolite]): + """ + Parsa una formula di reazione e imposta i metaboliti con i loro coefficienti. + """ + + if reaction.id == 'EX_thbpt_e': + print(reaction.id) + print(formula) + # Dividi in parte sinistra e destra + if '<=>' in formula: + left, right = formula.split('<=>') + reversible = True + elif '<--' in formula: + left, right = formula.split('<--') + reversible = False + left, right = left, right + elif '-->' in formula: + left, right = formula.split('-->') + reversible = False + elif '<-' in formula: + left, right = formula.split('<-') + reversible = False + left, right = left, right + else: + raise ValueError(f"Formato reazione non riconosciuto: {formula}") + + # Parse dei metaboliti e coefficienti + reactants = parse_metabolites_side(left.strip()) + products = parse_metabolites_side(right.strip()) + + # Aggiungi metaboliti alla reazione + metabolites_to_add = {} + + # Reagenti (coefficienti negativi) + for met_id, coeff in reactants.items(): + if met_id in metabolites_dict: + metabolites_to_add[metabolites_dict[met_id]] = -coeff + + # Prodotti (coefficienti positivi) + for met_id, coeff in products.items(): + if met_id in metabolites_dict: + metabolites_to_add[metabolites_dict[met_id]] = coeff + + reaction.add_metabolites(metabolites_to_add) + + +def parse_metabolites_side(side_str: str) -> Dict[str, float]: + """ + Parsa un lato della reazione per estrarre metaboliti e coefficienti. + """ + metabolites = {} + if not side_str or side_str.strip() == '': + return metabolites + + terms = side_str.split('+') + for term in terms: + term = term.strip() + if not term: + continue + + # pattern allineato: coefficiente opzionale + id che termina con _<compartimento> + match = re.match(r'(?:(\d+\.?\d*)\s+)?([A-Za-z0-9_]+_[a-z]+)', term) + if match: + coeff_str, met_id = match.groups() + coeff = float(coeff_str) if coeff_str else 1.0 + metabolites[met_id] = coeff + + return metabolites + + + +def set_biomass_objective(model: Model): + """ + Imposta la reazione di biomassa come obiettivo. + """ + biomass_reactions = [r for r in model.reactions if 'biomass' in r.id.lower()] + + if biomass_reactions: + model.objective = biomass_reactions[0].id + print(f"Obiettivo impostato su: {biomass_reactions[0].id}") + else: + print("Nessuna reazione di biomassa trovata") + + +def set_medium_from_data(model: Model, df: pd.DataFrame): + """ + Imposta il medium basato sulla colonna InMedium. + """ + medium_reactions = df[df['InMedium'] == True]['ReactionID'].tolist() + + medium_dict = {} + for rxn_id in medium_reactions: + if rxn_id in [r.id for r in model.reactions]: + reaction = model.reactions.get_by_id(rxn_id) + if reaction.lower_bound < 0: # Solo reazioni di uptake + medium_dict[rxn_id] = abs(reaction.lower_bound) + + if medium_dict: + model.medium = medium_dict + print(f"Medium impostato con {len(medium_dict)} componenti") + + +def validate_model(model: Model) -> Dict[str, any]: + """ + Valida il modello e fornisce statistiche di base. + """ + validation = { + 'num_reactions': len(model.reactions), + 'num_metabolites': len(model.metabolites), + 'num_genes': len(model.genes), + 'num_compartments': len(model.compartments), + 'objective': str(model.objective), + 'medium_size': len(model.medium), + 'reversible_reactions': len([r for r in model.reactions if r.reversibility]), + 'exchange_reactions': len([r for r in model.reactions if r.id.startswith('EX_')]), + } + + try: + # Test di crescita + solution = model.optimize() + validation['growth_rate'] = solution.objective_value + validation['status'] = solution.status + except Exception as e: + validation['growth_rate'] = None + validation['status'] = f"Error: {e}" + + return validation