Flux Analyzer

Overview

The flux-analyzer skill transforms raw FBA output into actionable biological knowledge. It operates on FBA result files and the COBRApy model to produce gene essentiality maps, phenotypic phase planes (PPP), flux sampling distributions, pathway-level summaries, and product secretion profiles.

This skill is the metabolic-modelling analogue of event reconstruction and phenomenology summary stage in the ColliderAgent pipeline: it turns numbers into biology.

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Workflow

Step 1, Load Model and FBA Results

import cobra
import cobra.io
import cobra.flux_analysis
import cobra.sampling
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

model = cobra.io.load_json_model("my_model.json")
fba_fluxes = pd.read_csv("fba_fluxes.csv", index_col=0)["flux_mmol_gDW_h"]
wt_growth = model.optimize().objective_value
print(f"Wild-type growth: {wt_growth:.4f} h^-1")

Step 2, Gene Essentiality Analysis

Essential genes are those whose deletion reduces growth to below 5% of wild-type, a widely used lethality criterion.

from cobra.flux_analysis import single_gene_deletion, double_gene_deletion

# --- Single gene essentiality ---
sg_deletion = single_gene_deletion(model)
sg_deletion.columns = ["growth", "status"]
sg_deletion["is_essential"] = sg_deletion["growth"] < 0.05 * wt_growth
sg_deletion["growth_fraction"] = sg_deletion["growth"] / wt_growth

essential_genes = sg_deletion[sg_deletion["is_essential"]]
print(f"Essential genes: {len(essential_genes)} / {len(model.genes)}")
sg_deletion.to_csv("gene_essentiality.csv")