Fba Simulator

Overview

The fba-simulator skill executes constraint-based metabolic simulations on a validated COBRApy model. FBA solves a linear program to find the flux distribution that maximizes (or minimizes) the objective function subject to stoichiometric and thermodynamic constraints.

This skill sits between model construction (gsmm-builder) and biological interpretation (flux-analyzer). All simulations are non-destructive: COBRApy context managers restore model state after each perturbation.

---

Workflow

Step 1, Load Validated Model

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}")

Step 2, Standard FBA

FBA maximizes the objective (typically biomass) subject to stoichiometric steady-state constraints: S·v = 0, lb ≤ v ≤ ub.

# 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"])

Step 3, Parsimonious FBA (pFBA)

pFBA first maximizes growth, then minimizes total absolute flux, producing the most "economical" solution consistent with maximum growth. This avoids biologically unrealistic high-flux split cycles.

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"])

Step 4, Flux Variability Analysis (FVA)