cobrapy

# COBRApy - Metabolic Modeling Models the "metabolism" of a cell as a linear optimization problem. Used to predict bacterial growth under different conditions or design GMO strains. ## When to Use - Predicting microbial growth rates under different nutrient conditions. - Designing metabolic engineering strategies (knockouts, additions). - Understanding metabolic flux distributions. - Comparing metabolic capabilities across organisms. - Identifying essential genes and reactions. ## Core Principles ### Flux Balance Analysis (FBA) Optimizes metabolic fluxes to maximize biomass production (or other objectives) subject to stoichiometric constraints. ### Gene-Protein-Reaction (GPR) Genes encode proteins (enzymes) that catalyze reactions. Knockouts affect reaction availability. ### Constraints Reaction bounds (lower/upper limits) represent enzyme capacity or nutrient availability. ## Quick Reference ### Standard Imports ```python import cobra from cobra.io import load_model, save_model ``` ### Basic Patterns ```python # 1. Load model (e.g., E. coli) model = cobra.io.load_model("iJO1366") # Or: model = cobra.io.read_sbml_model("model.xml") # 2. Run Flux Balance Analysis (FBA) solution = model.optimize() print(f"Growth rate: {solution.objective_value:.4f}") print(f"Status: {solution.status}") # 3. Knockout simulation (Gene essentiality) with model: model.genes.get_by_id("b0002").knock_out() print(f"Growth after knockout: {model.optimize().objective_value:.4f}") # 4. Change medium (nutrient availability) model.medium = { 'EX_glc__D_e': 10.0, # Glucose uptake 'EX_o2_e': 1000.0 # Oxygen } solution = model.optimize() ``` ## Critical Rules ### ✅ DO - **Check solution status** - Ensure status is 'optimal' before using results. - **Use context managers** - Wrap modifications in `with model:` to avoid permanent changes. - **Set appropriate bounds** - Reaction bounds should reflect biological reality. - **Validate model** - Use `model.validate()` to check for common issues. ### ❌ DON'T - **Don't ignore infeasible solutions** - If optimization fails, check constraints and bounds. - **Don't modify model in place** - Use context managers or copy the model first. - **Don't assume all reactions are active** - Many reactions have zero flux in optimal solution. ## Advanced Patterns ### Flux Variability Analysis (FVA) ```python from cobra.flux_analysis import flux_variability_analysis # Find range of possible fluxes for each reaction fva_result = flux_variability_analysis(model, model.reactions) ``` ### Gene Essentiality Analysis ```python # Test which genes are essential for growth from cobra.flux_analysis import single_gene_deletion deletion_results = single_gene_deletion(model) essential_genes = deletion_results[deletion_results['growth'] < 0.01] ``` ### Adding Custom Reactions ```python # Add a new reaction to the model new_reaction = cobra.Reaction("NEW_RXN") new_reaction.add_metabolites({ model.metabolites.get_by_id("glc__D_c"): -1, model.metabolites.get_by_id("atp_c"): -1, model.metabolites.get_by_id("adp_c"): 1, }) new_reaction.lower_bound = 0 new_reaction.upper_bound = 1000 model.add_reactions([new_reaction]) ``` COBRApy transforms metabolic networks into computable models, enabling researchers to predict and engineer cellular behavior at the systems level.