From literature to SBML decision engine
A staged pipeline for building, validating and updating mechanistic models with transparent human review.
1. Evidence extraction
Mine publications, labels, trial reports and model papers; identify mechanisms, endpoints, assumptions and quantitative anchors.
2. Model reconstruction
Rebuild the mechanistic structure as species, compartments, reactions, rules, events and observable equations.
3. SBML assembly
Encode in SBML Level 3 Version 2 where possible, with file naming, provenance and reproducible simulation assets.
4. Calibration
Fit selected parameters to curated targets and hold out data for predictive checks where feasible.
5. V&V and uncertainty
Run structural checks, numerical tests, RMSE/holdout criteria, sensitivity, identifiability and Bayesian summaries as appropriate.
6. Reports and release
Publish the model page, package artefacts, update the catalogue and define the free/subscription boundary.
Quarterly updates
Each active model can be refreshed on a quarterly cadence: new literature, new labels, new trials, revised assumptions, validation report deltas and partner-specific scenario packs.