Welcome to the Stochastic Simulation Service: the accessible platform for modeling biochemical systems. StochSS offers a simple web interface for simulating stochastic and ODE-based biochemical systems with the option to dig deeper through an integrated Jupyter notebook interface.
StochSS Live is a complete rewrite of the system. The new service unlocks the power of GillesPy2 for advanced hybrid ODE/stochastic simulations and support for importing SBML models. StochSS Live also uses SpatialPy (beta) in the notebook interface for 3D stochastic and ODE simulation. Machine learning inference capabilities and large-scale parameter sweeps are supported in the jupyter notebook interface with the sciope library. Head to the About page for more details on the features of StochSS Live.
Features
- Easy-to-use web interface for model development
- SBML import/export
- Stochastic, ODE, and hybrid simulations
- New model components: Events, Rate Rules and Assignment Rules
- 1D and 2D parameter sweeps
- Template notebooks for machine learning inference
- Integrated Jupyter notebook environment
- New Hybrid Concentration/Population algorithm for workflows
Coming soon
- Web interface for 3D modeling and simulation
- Bring your own cloud
- Public model library
- Convert between 2D and 3D models
- Web interface for parameter estimation and sensitivity
Download & Source Code
To run your own installation of StochSS, on your computer or on your own server, please see instructions on the GitHub page: https://github.com/stochss/stochss
StochSS is powered by the following libraries:
– GillesPy2: https://github.com/GillesPy2/GillesPy2/
– Sciope: https://github.com/sciope/sciope
– SpatialPy (beta): https://github.com/spatialpy/SpatialPy