aiming-lab/autoresearchclaw/fba-simulator

import cobra
import cobra.io
import cobra.flux_analysis
import pandas as pd
model = cobra.io.load_json_model("my_model.json")
print(f"Model: {model.id}  Solver: {model.solver}")

# Run FBA
solution = model.optimize()
print(f"Status          : {solution.status}")
print(f"Growth rate     : {solution.objective_value:.4f} h^-1")
print(f"Glucose uptake  : "
      f"{solution.fluxes['EX_glc__D_e']:.4f} mmol/gDW/h")
print(f"O2 uptake       : "
      f"{solution.fluxes.get('EX_o2_e', 0):.4f} mmol/gDW/h")
print(f"Acetate sec.    : "
      f"{solution.fluxes.get('EX_ac_e', 0):.4f} mmol/gDW/h")
# Save full flux distribution
solution.fluxes.to_csv("fba_fluxes.csv", header=["flux_mmol_gDW_h"])

pfba_solution = cobra.flux_analysis.pfba(model)
print(f"pFBA growth rate : {pfba_solution.objective_value:.4f} h^-1")
print(f"Total flux norm  : {pfba_solution.fluxes.abs().sum():.2f}")
pfba_solution.fluxes.to_csv("pfba_fluxes.csv", header=["flux_mmol_gDW_h"])

from cobra.flux_analysis import flux_variability_analysis
# FVA at 90% of maximum growth
fva_result = flux_variability_analysis(
    model,
    fraction_of_optimum=0.90,
    processes=4,          # parallel workers
)
# fva_result is a DataFrame with columns "minimum" and "maximum"
print(fva_result.head(10))
# Identify rigidly constrained reactions (min ≈ max)
TOLERANCE = 1e-6
rigid = fva_result[
    (fva_result["maximum"] - fva_result["minimum"]).abs() < TOLERANCE
]
print(f"\nRigid reactions (min=max): {len(rigid)}")
fva_result.to_csv("fva_result.csv")

loopless_sol = cobra.flux_analysis.loopless_solution(model)
print(f"Loopless growth  : {loopless_sol.objective_value:.4f} h^-1")
loopless_sol.fluxes.to_csv("loopless_fluxes.csv",
                            header=["flux_mmol_gDW_h"])

# Single gene knockout (context manager — model is restored after)
gene_id = "b0720"  # pgi in E. coli iJO1366
with model:
    model.genes.get_by_id(gene_id).knock_out()
    ko_solution = model.optimize()
    print(f"KO {gene_id} growth: {ko_solution.objective_value:.4f} h^-1")
# Batch single gene deletions
from cobra.flux_analysis import single_gene_deletion
deletion_results = single_gene_deletion(model)
# Returns DataFrame: index = frozenset({gene_id}), columns = [growth, status]
deletion_results.to_csv("gene_deletions.csv")
# Essential genes: growth < 5% of wild-type
wt_growth = model.optimize().objective_value
essential = deletion_results[
    deletion_results["growth"] < 0.05 * wt_growth
]
print(f"\nEssential genes: {len(essential)}")
print(essential.head())

from cobra.flux_analysis import single_reaction_deletion
rxn_deletion_results = single_reaction_deletion(model)
rxn_deletion_results.to_csv("reaction_deletions.csv")
essential_rxns = rxn_deletion_results[
    rxn_deletion_results["growth"] < 0.05 * wt_growth
]
print(f"Essential reactions: {len(essential_rxns)}")

CARBON_SOURCES = {
    "glucose":   ("EX_glc__D_e", -10.0),
    "fructose":  ("EX_fru_e",    -10.0),
    "acetate":   ("EX_ac_e",     -10.0),
    "glycerol":  ("EX_glyc_e",   -10.0),
    "succinate": ("EX_succ_e",   -10.0),
}
results = []
for carbon, (rxn_id, bound) in CARBON_SOURCES.items():
    with model:
        # Close all carbon exchange reactions first
        for r in model.exchanges:
            if r.lower_bound < 0 and r.id != "EX_o2_e":
                r.lower_bound = 0.0
        # Open the target carbon source
        if rxn_id in model.reactions:
            model.reactions.get_by_id(rxn_id).lower_bound = bound
            sol = model.optimize()
            results.append({
                "carbon_source": carbon,
                "growth_rate": sol.objective_value,
                "status": sol.status,
            })
        else:
            results.append({
                "carbon_source": carbon,
                "growth_rate": None,
                "status": "reaction_not_in_model",
            })
carbon_df = pd.DataFrame(results)
print(carbon_df)
carbon_df.to_csv("carbon_source_comparison.csv", index=False)

summary = {
    "model_id": model.id,
    "wt_growth_fba": wt_growth,
    "wt_growth_pfba": pfba_solution.objective_value,
    "wt_growth_loopless": loopless_sol.objective_value,
    "n_essential_genes": len(essential),
    "n_essential_reactions": len(essential_rxns),
}
import json
with open("simulation_summary.json", "w") as f:
    json.dump(summary, f, indent=2)
print("Summary written to simulation_summary.json")