Announcing StochSS 2.0

The Next-Generation Toolkit for Simulation-Driven Biological Discovery

We are pleased to announce the release of StochSS 2.0: Stochastic Simulation Service.  StochSS 2.0 is a complete rewrite of StochSS, our earlier Software-as-a-Service platform providing state-of-the-art tools to streamline and accelerate the development of robust quantitative models of complex biological and biochemical systems, with powerful new features including machine-learning- aided parameter inference and tools for exploring the parameter space, guided by interactive semi-supervised learning and harnessing the computational power of cloud computing. Check it out at: https://app.stochss.org/

    Features:

  • Model building user interface
    • Create your model in minutes
    • Preview simulations on the same page
  • Workflows: Simulate, analyze, and visualize your model
    • Ensemble simulations, parameter sweeps, parameter inference
    • Convert to a Jupyter Notebook for custom analysis
  • GillesPy2: Fast simulation of well-mixed systems
    • Fast stochastic (SSA) and deterministic (ODE) solvers
    • Hybrid stochastic/deterministic solver: use stochastic dynamics when populations are low, and switch automatically to deterministic simulation for greater efficiency when populations become larger
    • Event, rate, and assignment rules
  • Sciope: SCalable Inference, Optimization, and Parameter Exploration
    • Machine learning- aided parameter inference
    • Model exploration: interactive semi-supervised learning on large distributed parameter sweep
  • SpatialPy: Spatial stochastic simulation
    • Simulate 3D reaction-diffusion models using stochastic (sSSA) or deterministic (PDE) solvers
    • Analyze results using an interactive 3D visualization tool
  • SBML compliant: the standard of model interoperability

We invite you to use StochSS for your model based scientific endeavors. Go to https://stochss.org to learn more, and https://github.com/stochss/stochss to contribute.

 -Linda Petzold, Brian Drawert, and Andreas Hellander for the StochSS team