01
Where it fits and where it does not
Use these four checks before committing implementation time.
- Use it when
- Reproducible exchange and execution of systems-biology models and simulation experiments across compatible tools.
- Limits
- SBML package and tool support varies, and a syntactically reproducible simulation does not prove biological validity, parameter identifiability, or agreement with experimental evidence.
- Best for
- Discovery and Computational modeling teams working across Harmonize → Exchange → Learn + reuse.
- Maturity
- EstablishedSuitable for production assessment. Pin the exact release and any implementation profile.
02
See it in the workflow
This view shows the input, the change the standard introduces, and the resulting output.
- InputWhat starts
Discovery and Computational modeling source data, metadata, and local mappings
- SBML · SED-MLWhat changes
Use SBML · SED-ML as a pinned standard across Harmonize → Exchange → Learn + reuse
- OutputWhat becomes possible
A handoff the next system or team can validate against the same release
03
A concrete example
A model release validates an SBML L3V2 model, encodes simulation tasks and outputs in SED-ML L1V5, pins algorithms and parameters, and distributes the assets as a COMBINE archive.
Why it matters: Supports simulation-derived data and mechanistic features, but model assumptions, parameter provenance, calibration data, and domain-of-validity labels remain essential.
04
What it fits with
SED-ML can execute referenced SBML models and implements MIASE concepts; COMBINE archives bundle models, simulations, data, metadata, and outputs into one exchange package.
- Data model / schemaData Package
Both support Discovery work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - StandardCWL
Both support Computational modeling work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - StandardInChI
Both support Discovery work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship - Metadata profileREMBI
Both support Discovery work and meet around Harmonize, Exchange, Learn + reuse. Compare their roles before treating them as interchangeable.
Explore relationship
05
Implementation starter
Start with one bounded handoff. Pin, test, and review it before scaling.
Define one handoff, its accountable owner, and the decision SBML · SED-ML must support.
Pin the exact version and companion artifacts: SBML L3V2 Core Release 2 · SED-ML L1V5.
Map one representative input to the required standard artifacts.
Test the result against the canonical source and record every exception.
Preserve the source data, mappings, and review evidence before scaling.
06
Test the main limitation
SBML package and tool support varies, and a syntactically reproducible simulation does not prove biological validity, parameter identifiability, or agreement with experimental evidence.
Run one representative end-to-end pilot and record exactly where SBML · SED-ML loses context, needs an extension, or depends on another standard.
Machine-readable output may still be unfit for analysis or ML.
Test the output for missing context, provenance, terminology alignment, time leakage, and the intended downstream decision. Supports simulation-derived data and mechanistic features, but model assumptions, parameter provenance, calibration data, and domain-of-validity labels remain essential.
07
Official resources
Specifications, diagrams, examples, and guides from the organizations that maintain them.
COMBINE standards registry
Official publisher or steward guidance for this standard profile.
- Publisher
- SBML and SED-ML communities · COMBINE