# Flux Analyzer

Analyse FBA flux distributions to extract biological insights.

- Repository: https://github.com/aiming-lab/AutoResearchClaw (14798 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-08-19
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add aiming-lab/AutoResearchClaw --skill flux-analyzer -g -a claude-code -y`
Codex: `npx skills add aiming-lab/AutoResearchClaw --skill flux-analyzer -g -a codex -y`
Cursor: `npx skills add aiming-lab/AutoResearchClaw --skill flux-analyzer -g -a cursor -y`
Gemini CLI: `npx skills add aiming-lab/AutoResearchClaw --skill flux-analyzer -g -a gemini-cli -y`

## Description

Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## 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.

---

## Workflow

### Step 1, Load Model and FBA Results

```python
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.

```python
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")

The rest: https://github.com/aiming-lab/AutoResearchClaw/blob/HEAD/external/agents/Biology-Agent/skills/flux-analyzer/SKILL.md