cobrapy

Constraint-based modeling predicts cellular phenotypes by optimizing metabolic fluxes under stoichiometric constraints. You can calculate maximum growth rates by performing Flux Balance Analysis (FBA) on genome-scale metabolic models using reaction bounds and media constraints.

Yes, you can simulate gene and reaction knockouts to evaluate their effect on cellular metabolism. The analysis computes resulting optimal objective fluxes, allowing you to compare growth rates and product formation against the wild-type metabolic model.

Flux Variability Analysis (FVA) quantifies the range of possible fluxes for each reaction while maintaining a optimal objective threshold. You need it to quantify uncertainty and explore alternative feasible flux solutions within the metabolic network's feasible space.

Yes, the analysis supports model I/O across SBML, JSON, and YAML formats. This allows you to load established genome-scale metabolic network reconstructions for simulation and export the optimized results for sharing.

You map achievable trade-offs by computing production envelopes. This analysis evaluates optimal fluxes across varying secretion rates to identify feasible production boundaries within the metabolic network's stoichiometric constraints.

Flux sampling explores the feasible space of a metabolic network but relies on solver-backed feasibility checks and defined stoichiometric constraints. It requires properly bounded genome-scale models to avoid infeasible spaces and ensure biologically meaningful flux distributions.