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Epidemiological modeling in StochSS Live!

https://doi.org/10.1093/bioinformatics/btab061

Summary
We present StochSS Live!, a web-based service for modeling, simulation, and analysis of a wide range of mathematical, biological and biochemical systems. Using an epidemiological model of COVID-19, we demonstrate the power of StochSS Live! to enable researchers to quickly develop a deterministic or a discrete stochastic model, infer its parameters, and analyze the results.

Availability
StochSS Live! is freely available at https://live.stochss.org/.

Supplementary information
Available at https://github.com/StochSS/Covid19_Modeling

Announcing StochSS Live!

The Next-Generation Toolkit for Simulation-Driven Biological Discovery

We are pleased to announce the release of StochSS Live: Stochastic Simulation Service.  StochSS Live 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://live.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

The Next-Generation of StochSS will be coming soon!

We are pleased to announce that development has begun on the next generation of StochSS!

The next-generation of StochSS will have the following features:

Model Development Toolkit. We are developing tools to facilitate and accelerate the process of Model Development: the iterations of modeling, simulation, and experiment that are typically required to converge on the most plausible model that can explain the data. The Model Development Toolkit will address parameter estimation and quantification of uncertainty, generation and evaluation of the set of plausible models, and optimal design of experiments (prediction of which information would be most informative to validate or invalidate a model).

Model Exploration Toolkit. We are developing tools for Model Exploration: the process of exploring the parameter space to ensure that the model is robust to variations in uncertain and/or undetermined parameters, to find the regions of parameter space in which the model is capable of yielding a given behavior such as oscillations, and to discover all of the qualitatively distinct behaviors which the model can yield within the space of uncertain and/or undetermined parameters.

Expanded core capabilities. We will extend the core functional capabilities, including an updated cloud backend, to support scalable computing for model development and model exploration, and improved compatibility with other software via support for standard formats for model exchange.

We thank the NIBIB for supporting this project.

StochSS 1.9 available for download

We are pleased to announce the release of StochSS Version 1.9! StochSS: Stochastic Simulation Service, is an integrated development environment for modeling and simulation of discrete stochastic biochemical systems. An easy-to-use GUI enables researchers to quickly develop and simulate biological models on a desktop or laptop, which can then be expanded or combined to incorporate increasing levels of complexity. As the demand for computational power increases, StochSS is able to seamlessly scale up by deploying cloud computing resources. The software currently supports simulation of ODE and well-mixed discrete stochastic models, parameter estimation of discrete stochastic models, and simulation of spatial stochastic models.
New capabilities of Version 1.9 include:

  1. Support for simulation and parameter sweep jobs on batch cluster computing (i.e. Qsub based computing clusters)
  2. Configuration of batch computing resources
  3. Enhancement to multi-user setup, including email authentication

Click here to download StochSS 1.9 (Legacy) for your platform.