/cobrapy

name: cobrapy
description: Performs constraint-based metabolic modeling with COBRApy, including FBA, pFBA, FVA, gene knockouts, flux sampling, growth media, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.
license: GPL-2.0 license
allowed-tools: Read Write Edit Bash
compatibility: Requires Python 3.9+ and cobra; examples tested with Python 3.12 and cobra 0.32.1. GLPK installs via swiglpk; commercial solvers need separate installation/licensing. Remote model downloads need network access; bundled textbook examples run offline.
metadata:
  version: "1.5"
  skill-author: K-Dense Inc.
  last-reviewed: "2026-09-30"

COBRApy - Constraint-Based Reconstruction and Analysis

When to use

Use for loading/building metabolic networks, optimizing their steady-state fluxes, knockout screens, medium design, feasible-space sampling, and gap-filling hypotheses. FBA is a constraint-based prediction; it does not infer kinetic rates or establish experimental growth, thermodynamic feasibility, or flux identifiability.

Setup and reproducibility

Targets **cobra 0.32.1** (import `cobra`), checked against its released source and [current documentation](https://cobrapy.readthedocs.io/en/latest/).

uv pip install "cobra==0.32.1"
# Optional SciPy support for MATLAB I/O and array operations:
uv pip install "cobra[array]==0.32.1"

Plotting examples additionally require matplotlib, pandas, and seaborn. The `cobra[chrr]` extra supplies hopsy for the new CHRR sampler; that optional backend was documentation-reviewed, not executed in this refresh. In 0.32.1, `sample()` defaults to `method="auto"` (CHRR when hopsy is installed, otherwise OptGP). Choose the method explicitly to avoid environment-dependent changes.

Record model source/version/checksum, cobra and solver versions, objective, medium, bounds, tolerances, and any random seed with each analysis. GLPK handles the examples below; inspect `cobra.util.solver.solvers` before choosing an optional solver. Use `"hybrid"` for its HiGHS/OSQP interface when installed; the legacy `"osqp"` alias is deprecated. QP methods require a suitable backend. Start with `processes=1`; scripts using multiprocessing need a guarded entry point.

Workflow

1. Load, inspect, and validate a model

from cobra.io import load_model

# These names are bundled: textbook, iJO1366, salmonella.
model = load_model("textbook")  # model.id is e_coli_core; 95 reactions
model.solver = "glpk"
print(model.id, len(model.reactions), len(model.metabolites), len(model.genes))
print(model.reactions.get_by_id("PFK").reaction)
print(model.reactions.PFK.gene_reaction_rule)
print(model.medium)

solution = model.optimize(raise_error=True)
assert solution.status == "optimal"
print(solution.objective_value, solution.fluxes["PFK"])
# error_value=None raises on a failed solve; the default instead returns NaN.
baseline = model.slim_optimize(error_value=None)
assert baseline > 0

`e_coli_core` is a remote BiGG identifier, **not** a bundled alias for `textbook`. The released remote adapters encounter redirects on current BiGG/BioModels URLs; see [model I/O](references/api_quick_reference.md) for verified download routes and local SBML loading. Do not assume `load_model` caches: its 0.32.1 implementation accepts `cache` but does not use it. Save source files for reproducibility.

Check chemical formulas/charges and boundary annotations separately from solver feasibility. Review excluded biomass/pseudo reactions and missing chemistry; an empty imbalance dictionary alone cannot establish a chemically valid model. The [validation workflow](references/workflows.md) distinguishes those cases.

2. Compare FBA, pFBA, and FVA under stated constraints

from cobra.flux_analysis import pfba, geometric_fba, flux_variability_analysis

biomass_id = "Biomass_Ecoli_core"  # inspect IDs/objective for each new model
parsimonious = pfba(model)
print(parsimonious.fluxes[biomass_id])
# parsimonious.objective_value is the minimized total flux, not biomass growth.
centered = geometric_fba(model, processes=1)

fva = flux_variability_analysis(
    model, reaction_list=["PFK", "FBA", "PGI"],
    fraction_of_optimum=0.9, processes=1,
)
loopless_fva = flux_variability_analysis(
    model, reaction_list=["PFK", "FBA", "PGI"],
    fraction_of_optimum=0.9, loopless="fastSNP", processes=1,
)
print(fva)  # index: reaction ID; columns: minimum, maximum

FVA extrema are optimized separately and need not be jointly achievable. `loopless="fastSNP"` computes loopless bounds; `"cycleFreeFlux"` is an alternative that need not find the optimal bounds. Boolean `loopless` arguments are deprecated. Loop removal does not impose measured Gibbs energies or metabolite concentrations. Fractional objective thresholds here assume a positive biomass maximization objective; use an explicit constraint for other objective signs/directions.

3. Knockouts and media

from cobra.flux_analysis import single_gene_deletion
from cobra.medium import minimal_medium

results = single_gene_deletion(model, processes=1)
valid = results[results.status.eq("optimal") & results.growth.notna()]
low_growth = valid[valid.growth < 0.01 * baseline]  # declared 1% criterion
unresolved = results[~results.index.isin(valid.index)]
print(low_growth[["ids", "growth"]], unresolved[["ids", "status"]])

with model:
    medium = model.medium
    medium["EX_o2_e"] = 0.0
    model.medium = medium  # editing the returned dict alone does not change model
    anaerobic = model.optimize()
    print(anaerobic.status, anaerobic.objective_value)

min_medium = minimal_medium(model, 0.5 * baseline, minimize_components=True)
if min_medium is None:
    raise RuntimeError("No medium found at the requested growth target")
with model:
    model.medium = min_medium.to_dict()
    assert model.slim_optimize(error_value=None) >= 0.5 * baseline - 1e-6

Deletion results contain `ids` **se