cobrapy

To run flux balance analysis on an SBML metabolic model, load the file and configure the solver to compute the predicted objective and key reaction fluxes. This skill handles the entire FBA workflow from file input to flux computation.

Yes, you can screen gene knockouts in a genome-scale metabolic model by running single or double gene deletion studies. The skill computes the resulting phenotypic effects and reaction fluxes to identify essential genes for metabolic engineering targets.

Flux variability analysis (FVA) determines the range of possible fluxes for each reaction while maintaining the optimal objective value. You need FVA in constraint-based modeling to evaluate metabolic flexibility and identify bottleneck reactions in genome-scale models.

This skill supports JSON and YAML model formats in addition to SBML. You can load, inspect, and save genome-scale metabolic models across these exchangeable formats for flexible constraint-based reconstruction and analysis workflows.

To fix an infeasible metabolic model with missing reactions, use gapfilling to restore feasibility. This skill performs gapfilling during constraint-based modeling to add missing reactions and resolve infeasible deletion studies or simulation errors.

The best way to explore carbon-source utilization is by generating production envelopes and running flux sampling. This skill computes these analyses on genome-scale metabolic models to map growth and production rates across different media conditions.