# 13C Metabolic Flux

Estimates metabolic fluxes from carbon-13 isotope tracing data with fitting and diagnostics.

- Repository: https://github.com/K-Dense-AI/scientific-agent-skills (55651 stars)
- Category: Data and ML
- License: MIT
- Last updated: 2026-10-26
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add K-Dense-AI/scientific-agent-skills --skill 13c-metabolic-flux -g -a claude-code -y`
Codex: `npx skills add K-Dense-AI/scientific-agent-skills --skill 13c-metabolic-flux -g -a codex -y`
Cursor: `npx skills add K-Dense-AI/scientific-agent-skills --skill 13c-metabolic-flux -g -a cursor -y`
Gemini CLI: `npx skills add K-Dense-AI/scientific-agent-skills --skill 13c-metabolic-flux -g -a gemini-cli -y`

## Description

Estimates intracellular metabolic fluxes from steady-state carbon-13 isotope-tracing measurements using validated atom maps, mfapy isotope simulation, constrained multistart fitting, and flux-profile diagnostics. Use for 13C-MFA, carbon tracing, mass isotopomer distributions (MDVs/MIDs), positional isotopomers, parallel tracer experiments, and determining whether labeling data constrain a pathway flux. Distinguishes measured-label inference from COBRA flux balance analysis and flags experiments requiring nonstationary MFA.

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

# Carbon-13 metabolic flux inference

Turn reviewed carbon maps, explicit tracer mixtures, and corrected labeling measurements
into feasible flux estimates and evidence about which fluxes the experiment constrains.
Use the bundled solver rather than reconstructing isotope balances or fitting each
reaction independently. It runs mfapy's EMU forward simulator and fits fluxes in the
mass-balanced feasible space with SciPy. It does not use an FBA objective.

## Scope and required evidence

This implementation supports **metabolic and isotopic steady state**, a single shared
flux state across one or more tracer experiments, nonnegative one-way reaction fluxes,
and carbon-subset mass distributions. Reversible reactions are two separately mapped
directions. Measurement error is Gaussian with a supplied covariance or a disclosed
diagonal approximation.

Before fitting, obtain:

- The carbon network and the source of each atom assignment. Stoichiometry alone does
  not specify where labeled atoms go. Record compartments as separate metabolite IDs.
- Evidence for both steady-state assumptions. Stable metabolite abundance does not
  establish isotopic steady state. Time-course labeling requires INST-MFA with pool
  sizes and initial labeling; do not average it into this solver.
- Every carbon input's positional isotopomer distribution, including unlabeled
  supplements, bicarbonate/CO2 when assimilated, and tracer impurity.
- Fragment carbon assignments, natural-abundance correction history, and uncertainty
  of the **reported mean**. Raw peak intensities, derivatized spectra, and MS/MS
  transitions require validated preprocessing before these inputs can be constructed.
- Flux units, extracellular rate measurements or a stated relative-flux reference,
  and biologically justified bounds. Label fractions alone cannot set an absolute rate.

The rest: https://github.com/K-Dense-AI/scientific-agent-skills/blob/HEAD/skills/13c-metabolic-flux/SKILL.md