Reproducibility
Published models are hard to re-run exactly as described.
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Life Sciences
Systems biology models with an engineer's toolchain
Simulate SBML Level 2 and 3 reaction networks, run parameter sweeps and Monte Carlo studies, and project models to Modelica for multi-scale analysis.
$ msx lint ./models --format json
$ msx mc Pathway pathway.mo
$ msx surrogate Pathway pathway.mo
What slows life sciences teams down
Uncertain parameters
Kinetic parameters are uncertain, so single runs aren't enough.
Siloed knowledge
Ontologies and models are rarely connected in a queryable way.
One pipeline, end to end
- 1
Import
SBML L2 / L3
- 2
Annotate
OWL2 ontologies
- 3
Simulate
ODE kinetics
- 4
Analyze
Monte Carlo & surrogates
What ModelScript does for you
Reaction kinetics
Streaming SBML parsing and ODE simulation of reaction networks.
Uncertainty
Monte Carlo and surrogate models for parameter studies.
Ontology links
Connect models to OWL2 ontologies with SPARQL queries.
Multi-scale
Project biochemical networks into Modelica.
Works with the tools you already use
Open-core toolchain that runs anywhere
Browser, CLI, VS Code, CI pipelines, or air-gapped Enterprise Appliances (AGPL-3.0 Core & Commercial Editions).