HolobiomicsLab Genome-Scale Metabolic Model Flux Sampling / Consensus (GEM -> constrained flux states -> community consensus)
Summary
A GEM plus omics data in, a constrained flux-state report out: eFlux-style constraint integration, optGpSampler flux-space sampling, flux-distribution interpretation, and (for multi-member models) COMMIT consensus community-model integration.
When to use
Use when you have a genome-scale constraint-based metabolic model (GEM, SBML/JSON) for one or more organisms and want predicted flux states grounded in your own omics data — integrate transcriptomics / metabolomics-derived constraints (eFlux-style Reaction Activity/Propensity Scores, extracellular uptake-secretion rates) into the model, sample the feasible flux space with optGpSampler, interpret and compare the resulting flux distributions across samples or conditions, and, when multiple organism or community-member models exist, gap-fill and merge them into a consensus community model (COMMIT-style) — connecting metabolomics features to predicted flux states.
When NOT to use
- The data is not LC-MS.
- You need a single atomic step, not the full pipeline (use the leaf skill directly via the router).
Stages
Stage 1 — constrain
Goal: generic GEM + omics data -> sample-constrained metabolic model
EDAM operation: operation_3660
Inputs: sbml, tsv · Outputs: sbml
Candidate leaf skills: metabolic-model-constraint-application (primary), constraint-based-flux-balance-analysis, reaction-activity-score-computation-from-gpr, gene-expression-constraint-integration, transcriptomics-reaction-activity-scoring
Tools (primary): eFlux, TRFBA, scFBA, GX-FBA, constraint-based stoichiometric metabolic models, optGpSampler, COBRApy, Flux Variability Analysis (FVA), GLPK solver, YSI bioanalyzer (YSI2950)
Other candidate tools: STAR aligner (v.2.6.1d), HTSeq (v.0.6.1), YSI2950 bioanalyzer, Agilent 1290 Infinity UHPLC system, optGpSampler algorithm, t-SNE (t-distributed Stochastic Neighbor Embedding), Agilent 1290 Infinity UHPLC + 6550 iFunnel Q-TOF MS, eFlux, TRFBA, GX-FBA, scFBA, getRASscore (INTEGRATE step 2), getNormalizedRAS (INTEGRATE step 3), rasIntegration (INTEGRATE step 4), rasTtest (INTEGRATE step 8)
Grounding: 1 KB(s); DOIs: 10.1371/journal.pcbi.1009337
Stage 2 — sample_flux
Goal: constrained model -> feasible flux distribution samples (optGpSampler)
EDAM operation: operation_3927
Inputs: sbml · Outputs: tsv
Candidate leaf skills: irreversible-model-conversion (primary), feasible-flux-distribution-sampling, dimensionality-reduction-and-clustering-evaluation
Tools (primary): eFlux, TRFBA, scFBA, GX-FBA, optGpSampler, COBRApy
Other candidate tools: STAR aligner (v.2.6.1d), HTSeq (v.0.6.1), YSI2950 bioanalyzer, Agilent 1290 Infinity UHPLC system, optGpSampler algorithm, t-SNE (t-distributed Stochastic Neighbor Embedding), Mann-Whitney U test, GLPK, MATLAB (optional), t-SNE, Flux Variability Analysis (FVA)
Grounding: 1 KB(s); DOIs: 10.1371/journal.pcbi.1009337
Stage 3 — interpret_flux
Goal: sampled flux distributions -> normalized, compared flux-distribution report
EDAM operation: operation_3436
Inputs: tsv · Outputs: tsv
Candidate leaf skills: flux-distribution-segregation-visualization (primary), flux-distribution-interpretation-across-cell-lines, flux-variability-analysis-interpretation, flux-variability-analysis-for-scaling, constraint-based-flux-sampling-and-analysis
Tools (primary): Flux Variability Analysis, optGpSampler, COBRApy, t-SNE (scikit-learn or standalone), scipy.stats.spearmanr
Other candidate tools: constraint-based stoichiometric metabolic models, Flux Variability Analysis (FVA), randomSampling (INTEGRATE pipeline Step 6), getRASscore (INTEGRATE pipeline Step 2), concordanceAnalysis (INTEGRATE pipeline Step 10), createMetabolicDataset (INTEGRATE pipeline Step 9), COBRApy (optGpSampler), randomSampling.py (INTEGRATE pipeline), mannWhitneyUTest.py (INTEGRATE pipeline), eFlux, TRFBA, scFBA, GX-FBA, GLPK (GNU Linear Programming Kit), t-SNE, YSI bioanalyzer (YSI2950)
Grounding: 1 KB(s); DOIs: 10.1371/journal.pcbi.1009337
Stage 4 — community_consensus
Goal: draft per-organism/community-member GEMs -> gap-filled consensus community model
EDAM operation: operation_3695
Inputs: sbml · Outputs: sbml
Candidate leaf skills: metabolic-model-merging-consensus-building (primary), community-metabolic-reconstruction-synthesis, metabolic-model-consensus-integration, community-metabolic-pathway-integration, systems-biology-model-standardization
Tools (primary): COMMIT
Grounding: 2 KB(s); DOIs: 10.1371/journal.pcbi.1009906, 10.5281/zenodo.363932874
Stage 5 — report
Goal: consolidate sampled/interpreted flux states + consensus community model into a flux-state report
EDAM operation: operation_3434
Inputs: tsv, sbml · Outputs: tsv
Candidate leaf skills: constraint-based-model-sampling-and-flux-prediction (primary), mass-action-law-flux-prediction, extracellular-flux-constraint-integration
Tools (primary): COBRApy (optGpSampler algorithm), Flux Variability Analysis (FVA), constraint-based stoichiometric metabolic models, YSI2950 bioanalyzer
Other candidate tools: MassHunter ProFinder, COBRApy (implied by workflow), Flux Variability Analysis, eFlux, TRFBA, scFBA, GX-FBA, optGpSampler, COBRApy, YSI bioanalyzer (YSI2950), Agilent 1290 Infinity UHPLC + 6550 iFunnel Q-TOF MS, t-SNE
Grounding: 1 KB(s); DOIs: 10.1371/journal.pcbi.1009337
Grounding
Each stage carries the kb_slugs/dois of the leaves it draws on. Ground any stage against its source paper with the collection's /ground command or bin/perspicacite_kb_bind.py (Perspicacité KB; serverless local-clone fallback).
Verification contract
workflow.yaml is gradable by asb solve-workflow (checkpoint mode). Each stage declares typed outputs; the final stage emits the master deliverable.
Provenance
Generated by compose_workflows.py (semantic binding + EDAM-aware primary selection). derived_from_workflows lists ASB per-paper workflows whose structure corroborated this pipeline — the eval-ablation set (SPEC §8). Staging only; promote via release_gate.py.