Announcing StochSS Live!

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.


  • 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:

StochSS is powered by the following libraries:
– GillesPy2:
– Sciope:
– SpatialPy (beta):