Cobrapy

Source: https://github.com/aipoch/medical-research-skills

COBRApy (COBRA: Constraint-Based Reconstruction and Analysis)

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Constraint-based reconstruction and analysis (COBRA) for metabolic models; use when you need to simulate growth/production, analyze flux ranges, or run knockout and medium studies from SBML/JSON/YAML models.
• Documentation-first workflow with no packaged script requirement.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

Skill directory: 20260316/scientific-skills/Data Analytics/cobrapy
No packaged executable script was detected.
Use the documented workflow in SKILL.md together with the references/assets in this folder.

Example run plan:

1. Read the skill instructions and collect the required inputs.
2. Follow the documented workflow exactly.
3. Use packaged references/assets from this folder when the task needs templates or rules.
4. Return a structured result tied to the requested deliverable.

Implementation Details

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: instruction-only workflow in SKILL.md.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

1. When to Use

Use this skill when you need to perform constraint-based analysis on metabolic networks, especially for:

1. Predicting growth or production under specific media and objectives using Flux Balance Analysis (FBA).
2. Quantifying flux uncertainty and alternative optima using Flux Variability Analysis (FVA) and flux sampling.
3. Identifying essential genes/reactions via single/double knockout (deletion) studies.
4. Designing or optimizing media (e.g., minimal medium) to support a target growth rate.
5. Repairing infeasible models by gapfilling against a universal reaction database/model.

2. Key Features

• Model I/O and management: load/save models in SBML (preferred), JSON, and YAML; access reactions/metabolites/genes.
• FBA variants: standard FBA, parsimonious FBA (pFBA), geometric FBA.
• FVA: compute min/max feasible fluxes; supports fraction-of-optimum and loopless FVA.
• Knockout analysis: single/double gene and reaction deletions; temporary edits via context managers.
• Medium handling: inspect and modify model.medium; compute minimal media (optionally MILP-based).
• Flux sampling: sample feasible flux space (OptGP/ACHR) and validate samples.
• Production envelopes: phenotypic phase planes / production envelopes for trade-off exploration.
• Gapfilling: propose reaction additions to restore feasibility.
• Model construction: build models from scratch (metabolites, reactions, GPR rules, boundaries, objectives).

3. Dependencies

• cobra (COBRApy) — version varies by environment (commonly >=0.20)
• A supported LP/MILP solver (one of):
  • glpk / swiglpk (often default)
  • cplex (optional)
  • gurobi (optional)
• Optional (for plotting/analysis in examples):
  • pandas
  • matplotlib

4. Example Usage

The following script is a complete, runnable example that loads a built-in model, runs FBA, performs FVA, runs a gene knockout, adjusts medium, and samples fluxes.

# cobrapy_example.py
from cobra.io import load_model
from cobra.flux_analysis import flux_variability_analysis, single_gene_deletion, pfba
from cobra.sampling import sample

def main():
    # 1) Load a model (built-in test model)
    model = load_model("textbook")  # E. coli core model

    # 2) Run standard FBA
    sol = model.optimize()
    print("=== FBA ===")
    print("Status:", sol.status)
    print("Objective (growth):", sol.objective_value)

    # 3) Run pFBA (minimize total flux at optimal growth)
    pfba_sol = pfba(model)
    print("\n=== pFBA ===")
    print("Objective (growth):", pfba_sol.objective_value)

    # 4) Flux Variability Analysis at 90% of optimum
    print("\n=== FVA (90% optimum) ===")
    fva = flux_variability_analysis(model, fraction_of_optimum=0.9)
    print(fva.head())

    # 5) Single gene deletion screen (may take time on large models)
    print("\n=== Single Gene Deletion (first 5 rows) ===")
    del_res = single_gene_deletion(model)
    print(del_res.head())

    # 6) Medium modification (must re-assign the full dict)
    print("\n=== Medium ===")
    medium = model.medium
    # Example: limit glucose uptake (exchange IDs depend on the model)
    if "EX_glc__D_e" in medium:
        medium["EX_glc__D_e"] = 5.0
        model.medium = medium
        sol2 = model.optimize()
        print("Growth after limiting glucose:", sol2.objective_value)
    else:
        print("Model has no EX_glc__D_e in medium; skipping medium edit.")

    # 7) Flux sampling (small n for quick demo)
    print("\n=== Flux Sampling ===")
    samples = sample(model, n=200, method="optgp")
    print(samples.head())

if __name__ == "__main__":
    main()

Run:

python cobrapy_example.py

5. Implementation Details

5.1 Core optimization model (FBA)

• COBRApy formulates a linear program (LP):
  • Mass balance (steady state): ( S \cdot v = 0 )
  • Bounds: ( l \le v \le u )
  • Objective: maximize (or minimize) a linear function ( c^\top v ) (e.g., biomass reaction flux)
• model.optimize() solves the LP and returns a Solution with:
  • solution.status (e.g., optimal)
  • solution.objective_value
  • solution.fluxes (pandas Series of reaction fluxes)

5.2 Reaction directionality and bounds

• Irreversible reactions typically use lower_bound = 0.
• Reversible reactions allow negative flux: lower_bound < 0.
• Use reaction.bounds = (lb, ub) to set both consistently.

5.3 Gene-Protein-Reaction (GPR) rules

• reaction.gene_reaction_rule encodes Boolean logic:
  • "gene1 and gene2" means both genes required.
  • "gene1 or gene2" means either gene sufficient.
• Knockouts propagate through GPR logic to constrain affected reactions.

5.4 FVA parameters

• flux_variability_analysis(model, fraction_of_optimum=x) constrains the objective to be at least x * optimum before computing per-reaction min/max.
• loopless=True attempts to remove thermodynamically infeasible loops (typically more expensive).

5.5 Context manager for temporary edits

• with model: creates a reversible sandbox:
  • changes to objectives, bounds, knockouts, and reaction sets revert automatically on exit.
• This prevents accidental state carryover across analyses.

5.6 Flux sampling

• Sampling explores the feasible polytope defined by constraints.
• sample(..., method="optgp") uses OptGP (often parallelizable); method="achr" uses ACHR.
• For numerical stability, validate samples when needed (e.g., via OptGPSampler.validate).

5.7 Medium handling

• model.medium is a dictionary mapping exchange reaction IDs to allowed uptake rates.
• You must re-assign the full dictionary after edits: model.medium = medium.