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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 sets, growth, and status; the index is not a

pair MultiIndex. Double-deletion results also contain singleton sets (self-pairs).

Classify failed/infeasible solves separately from feasible low-growth mutants.

A knockout's growth column means the model's objective, so ensure it is biomass.

model.medium values are positive import bounds, not measured concentrations.

Exchange flux signs depend on reaction stoichiometry; the textbook's one-reactant

exchanges use negative flux for uptake. open_exchanges=False retains the allowed

nutrient universe while the optimizer selects a nutrient subset and its import

bounds; it does not fix the selected nutrients or amounts. open_exchanges=True

expands that universe and can choose unintended carbon sources. Minimal media can

be nonunique; validate the returned medium with the intended growth objective.

4. Sample the same feasible region as the FVA comparison

from cobra.sampling import OptGPSampler

with model:
    model.reactions.get_by_id(biomass_id).lower_bound = 0.9 * baseline
    # The growth floor is already installed. fraction=0 adds no stronger optimum.
    sampled_fva = flux_variability_analysis(
        model, reaction_list=["PFK"], fraction_of_optimum=0.0, processes=1,
    )
    sampler = OptGPSampler(model, processes=1, thinning=100, seed=7)
    samples = sampler.sample(200)
    codes = sampler.validate(samples)
    assert (codes == "v").all(), "Inspect bound/equality violations"
    assert (samples[biomass_id] >= 0.9 * baseline - 1e-6).all()
print(samples["PFK"].describe(), sampled_fva)

FVA does not leave its objective-fraction constraint on the model; sampling a

fresh/unconstrained model explores a different space. Feasible samples are not

a confidence interval for measured biology. Inspect independent chains,

autocorrelation and effective sample size before interpreting distributions;

200 samples are a smoke test. Sampling can include internal cycles.

5. Production envelopes and design hypotheses

from cobra.flux_analysis import production_envelope

with model:
    model.reactions.get_by_id(biomass_id).lower_bound = 0.1 * baseline
    envelope = production_envelope(
        model, reactions=["EX_glc__D_e"], objective="EX_ac_e",
        carbon_sources=["EX_glc__D_e"], points=8,
    )
print(envelope[["EX_glc__D_e", "flux_minimum", "flux_maximum"]])

These are acetate flux extrema; carbon_yield_* and mass_yield_* are

separate outputs, potentially NaN when inputs or formulas are unsuitable.

A multi-reaction grid needs a surface/heatmap, not an arbitrary connected line.

When zero flux is already allowed for the affected reactions, a knockout only

removes feasible states and cannot improve the global product maximum under

otherwise identical constraints. Check that knockout bound replacement does not

relax an original forced nonzero flux. The [design workflow](references/workflows.md)

screens the minimum product flux at a common growth requirement and also reports

maximum growth, an explicit model-based coupling hypothesis requiring validation.

6. Build and gap-fill carefully

Use Model, Reaction, Metabolite, model.add_reactions([reaction]), and

reaction.gene_reaction_rule to build the network. Set formulas and charges;

include water/protons when needed for balanced chemistry. Exchanges belong to

external metabolites, demands remove metabolites, and sinks permit both directions.

Do not add arbitrary ATP sources to make a model grow.

[The API reference](references/api_quick_reference.md) includes a balanced toy

network and a gap-fill example with a known missing reaction. gapfill returns

**a list of re

… (full content is in the download package)