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13C Metabolic Flux Analysis (13C-MFA)

Estimates intracellular fluxes from steady-state carbon-13 labeling data and tells you which fluxes the experiment actually constrains.

Data & AnalyticsAdvanced★ 47,481⑂ 4,292AI score 8/10Last updated: Oct 1, 2026

What it does

  • Takes reviewed atom maps, explicit tracer mixtures, and corrected mass isotopomer distributions (MDVs/MIDs) and runs mfapy's EMU forward simulator to predict labeling.
  • Fits fluxes with SciPy multistart optimization inside the mass-balanced feasible space — no FBA objective, so it is explicitly distinguished from COBRA-style flux balance analysis.
  • Ships a three-step CLI: check (input contract, carbon conservation, steady-state feasibility), simulate (exercise the forward model), fit (fit plus profile likelihood).
  • Its strongest feature is identifiability diagnostics: per-flux profile brackets, failed-start counts, local sensitivity rank, active bounds, and explicit flagging of unresolved flux combinations.
  • States its boundaries plainly: no nonstationary (INST-)MFA, no MS/MS joint distributions, no multi-element correction, no isotope effects on rates.

Who it's for

  • Metabolic engineering and systems biology researchers running 13C tracer experiments.
  • Analysts quantifying pathway splits from GC-MS/LC-MS labeling data.
  • Reviewers and reproducibility checkers asking whether labeling data truly determine a reported flux.
  • Not for you if you only want genome-scale FBA without isotope data.

Example uses

  1. Quantify a pathway split: a two-route model with an 80% carbon-1 labeled feed and a C1 fragment at M+1 = 0.56 recovers a 70/30 split, with --profile straight giving a ~67.55–72.45 95% interval.
  2. Expose an uninformative measurement: feeding only the whole-molecule distribution makes the two routes indistinguishable, returning local rank zero and unresolved_within_bounds.
  3. Reproduce a published TCA calculation: regenerate the glutamate MDV [0.3464, 0.2695, ...], fit the glutamate branch flux near 50, and report that fumarate/oxaloacetate exchange remains unresolved.

· · · Install guide · · ·

Try it now, no install

Paste this into Claude to use the skill without installing anything.

Read the instructions in this file and follow them to help me:
https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/13c-metabolic-flux/SKILL.md

What I want: (describe your task here)

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↓ If it works for you, download the ZIP below and install it. Then it runs on its own — no pasting each time.

Install in the Claude app (no terminal)
  1. Download the ZIP with the button below.
  2. In Claude, open Settings → Capabilities and turn on 'Code execution and file creation'. (one time)
  3. Go to Customize → Skills → + → 'Upload a skill' and upload the ZIP.
↓ Download ZIP
Install in Claude Code

Let Claude do it — paste this into Claude Code

Install the skill I found on Claude Skill Mart.
Copy the skills/13c-metabolic-flux folder from the GitHub repo K-Dense-AI/scientific-agent-skills into my ~/.claude/skills/13c-metabolic-flux/.
When it's done, tell me in one line what this skill can do.

Install with a command instead

git clone https://github.com/K-Dense-AI/scientific-agent-skills.git && mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/13c-metabolic-flux ~/.claude/skills/

⚠ This is a third-party skill. Check the source repository before installing.

  1. Open a terminal and clone the repo: git clone https://github.com/K-Dense-AI/scientific-agent-skills.git
  2. Copy the skill into Claude Code's skills folder: mkdir -p ~/.claude/skills && cp -r scientific-agent-skills/skills/13c-metabolic-flux ~/.claude/skills/
  3. Confirm you have Python 3.12, uv, and Git (uv --version, python3.12 --version).
  4. Move into a separate analysis directory (outside the skill folder) and create the venv: uv venv --python 3.12 .venv-mfa
  5. Install the pinned engine: uv pip install --python .venv-mfa/bin/python -r ~/.claude/skills/13c-metabolic-flux/assets/requirements.txt (mfapy installs from a fixed Git commit, so network access is required; on Windows use .venv-mfa/Scripts/python.exe).
  6. Smoke-test with a bundled example: .venv-mfa/bin/python ~/.claude/skills/13c-metabolic-flux/scripts/mfa.py fit --model ~/.claude/skills/13c-metabolic-flux/assets/branch-model.json --data ~/.claude/skills/13c-metabolic-flux/assets/branch-identifiable.json --profile straight --output branch-fit.json
  7. Restart Claude Code and ask something like "Run 13C-MFA on my labeling data and profile the branch flux."
View source on GitHub ↗License: MIT