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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
The skill file stays in the publisher's repository.
Paste the copied command in a terminal. It installs this SKILL.md into that agent.
npx skills add K-Dense-AI/scientific-agent-skills --skill 13c-metabolic-flux -a cursor -y
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.
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.
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.
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:
If necessary information is missing, name it and prepare the input template; do not invent a fragment assignment, atom map, isotope correction, or measurement error. Read references/input-contract.md when preparing inputs. Read references/inference.md before interpreting an actual fit.
Run in the user's analysis directory. Set SKILL_DIR to this skill's installed directory,
using the actual resolved path. Keep environments and generated results outside the skill.
uv venv --python 3.12 .venv-mfa
uv pip install --python .venv-mfa/bin/python -r "$SKILL_DIR/assets/requirements.txt"
The following commands use .venv-mfa/bin/python; on Windows use the environment's
Scripts/python.exe. mfapy is installed from an immutable Git revision because it is
not distributed on PyPI. Installation executes dependency build code; model inputs
are data, not user-supplied Python. The adapter restricts identifiers and atom-map
syntax before they reach mfapy's internally generated numerical functions.
The pinned commit matched upstream master on 2026-09-30. Its README labels the
latest change "064", but its installed distribution still reports 0.6.3; retain
the Git commit alongside the package version in an analysis record. The refreshed
NumPy/SciPy pins require Python 3.12 or later; the commands above use the tested 3.12
environment. See the reviewed forward-model contract in
references/inference.md.
Prepare explicit inputs. Copy a relevant model asset into the analysis directory, then replace its scientific content only from reviewed evidence. The bundled models are demonstrations, not validated organism-specific reconstructions. Use a separate dataset for each biological condition; jointly fit tracer replicates only when their biological flux state is defensibly shared.
Check the contract and feasibility.
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" check \
--model model.json --data measurements.json --output input-check.json
This checks atom counts and conservation, fragments, tracer sums, uncertainty matrices, bounds, and steady-state mass-balance feasibility. It cannot verify that a chemically consistent atom map is biologically correct or that a sample reached steady state.
Exercise the forward model. Supply one mass-balanced flux vector in the declared units. Compare predicted labeling with a reference or independently derived limits.
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" simulate \
--model model.json --data measurements.json --fluxes fluxes.json \
--output simulated-mdvs.json
Fit and profile the fluxes relevant to the question.
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" fit \
--model model.json --data measurements.json --starts 12 --seed 2026 \
--profile v3 --profile v7 --profile-points 31 --profile-starts 6 \
--output fit.json
Replace v3 and v7 with actual reaction IDs. Each profile point fixes that reaction
and reoptimizes nuisance fluxes. For nonlinear networks, repeat with a different seed
and more starts before interpreting a profile. A small residual is not an
identifiability result.
Inspect the evidence. Check failed starts, residual patterns, mass balance, active bounds, local sensitivity rank, and profile status. Report threshold-crossing brackets at their actual grid resolution. Refine the grid if they are too coarse. Each requested profile gives a one-flux interval under the stated error model; multiple 95% profiles are not a simultaneous 95% region for the whole network. If a profile finds a better solution than the baseline, rerun the fit; do not publish the stale intervals. A failed profile point is unknown, not excluded by the data.
Deliver a bounded scientific result. Include model and data hashes, package versions, source/correction provenance, units and reference flux, fitted predictions, residual diagnostics, profile plots or a table, and the unresolved flux combinations. Retain the JSON artifact. Separate point estimates supported by the data from arbitrary optimizer choices along a flat direction. Suggest additional measurements only after testing that their predicted labeling changes along that direction.
These executable examples use synthetic, tracer-only data. There is no hidden natural- abundance correction, and the tracer proportions already include unlabeled material.
The analytical two-route model sends a two-carbon substrate through either a carbon-preserving or a carbon-swapping route. Uptake is fixed to 100. An 80% carbon-1 labeled feed and a carbon-1 fragment with M+1 = 0.56 determine the preserving route as 70 and the swapping route as 30.
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" fit \
--model "$SKILL_DIR/assets/branch-model.json" \
--data "$SKILL_DIR/assets/branch-identifiable.json" \
--profile straight --profile-points 41 --output branch-fit.json
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" fit \
--model "$SKILL_DIR/assets/branch-model.json" \
--data "$SKILL_DIR/assets/branch-unresolved.json" \
--profile straight --output unresolved-fit.json
The first fit recovers approximately 70/30. Under its declared Gaussian error model,
the analytical 95% interval for straight is about 67.55β72.45; the script reports
grid brackets enclosing the threshold crossings. The second fit has only the whole-
molecule distribution, which is identical for the two routes. Expect local rank zero
and unresolved_within_bounds; its returned split is an arbitrary optimum.
assets/branch-fluxes.json supplies the 70/30 forward-simulation vector.
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" simulate \
--model "$SKILL_DIR/assets/tca-model.json" \
--data "$SKILL_DIR/assets/tca-tracer.json" \
--fluxes "$SKILL_DIR/assets/tca-fluxes.json" --output tca-simulation.json
.venv-mfa/bin/python "$SKILL_DIR/scripts/mfa.py" fit \
--model "$SKILL_DIR/assets/tca-model.json" \
--data "$SKILL_DIR/assets/tca-reference-mdv.json" \
--profile v3 --profile v7 --output tca-fit.json
The first command reproduces the published rounded glutamate MDV
[0.3464, 0.2695, 0.2708, 0.0807, 0.0286, 0.0039].
The second uses synthetic reference measurements to recover the glutamate branch
flux near 50, while recognizing that this labeling does not resolve the
fumarate/oxaloacetate exchange. A constraint-induced upper edge is not evidence of
a measurement-determined exchange interval.
"100000" means carbon-1 labeled glucose. A mass distribution alone cannot specify
that positional mixture. The adapter handles mfapy's reversed integer-bit ordering.symmetric flag means equal averaging of identity and complete carbon-order
reversal, as in the bundled fumarate/succinate map. It is not arbitrary molecular
symmetry. Other permutations need an explicitly supported model representation.scripts/mfa.py is the CLI. scripts/_mfa_model.py validates inputs and adapts them to
the mfapy EMU simulator; scripts/_mfa_fit.py handles feasible flux coordinates,
multistart optimization, diagnostic rank, and profile calculations.
The engine is pinned in assets/requirements.txt.
The repository suite at tests/13c-metabolic-flux/ checks the published reference,
analytical split recovery and likelihood profiles, unresolved routes and exchange,
omitted-bin invariance with correlated errors, parallel tracers, absolute-rate
anchoring, repeated-substrate condensation, symmetry, invalid maps, and CLI behavior.
These checks establish the tested numerical behavior, not biological validation of a
user's model or a measured advantage over any particular language model.