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

StochSS 1.8 available for download

We are pleased to announce the release of StochSS Version 1.8! 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 1.8 include:

  1. Parameter Sweep tool facilitates 1D/2D parameter sweeps for all models
  2. Integration of Jupyter Notebooks on job result pages for post-processing analysis
  3. Status page updates for filtering and automatic forwarding of running->finished jobs

Try StochSS Online

We are now providing StochSS as a service on http://try.stochss.org. This means that you can try StochSS without having to install anything locally on your computer.

Warning
Please note that this is for testing purposes only; all data may be lost if the server fails for any reason. We do not back it up regularly. However, the user does have the option to download model files and data for safe storage locally.

Screenshot showing volume rendering of a spatial stochastic simulation of a spatial negative feedback loop modeling the Hes1 regulatory network as described further in http://rsif.royalsocietypublishing.org/content/10/80/20120988
Screenshot showing volume rendering of a spatial stochastic simulation of a spatial negative feedback loop modeling the Hes1 regulatory network as described further in http://rsif.royalsocietypublishing.org/content/10/80/20120988

You have multiple options if you would like to use StochSS on your own resources. The simplest way to get started is to download the binary package (uses Docker).
Our trial server is deployed in the SNIC Science Cloud. If you would like to provide StochSS as a service for your reseach group or for a distributed collaboration, you can do this easily on your own servers, or in another cloud infrastructure provider such as Amazon EC2. MOLNs, another member of the StochSS suite of tools, can help you to configure and deploy an identical setup.
Please do not hesitate to reach out to us if you need help with this process.
Many of you also like the possibility to work with solvers in a programming environment. All of the tools that are powering StochSS are also available as stand alone libraries:

  • PyURDME (Python API for spatial stochastic modeling and simulation )
  • Gillespy (Python API for well-mixed simulations, based on StochKit2)

In addition, if you have access to cloud infrastructure, and would like to work in a pre-configured environment powered by a Jupyther Notebook frontend and interactive parallel computing, you should check out MOLNs:
MOLNs: Cloud platform framework for large-scale computational experiments such as ensembles and parameter sweeps, backed by Jupyther and Ipython Parallel.

StochSS 1.7 Available for Download!

We are pleased to announce the release of StochSS Version 1.7! 
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 1.7 include:

  •  Volume rendering visualization for spatial jobs.
  •  StochSS-launcher uses docker containers to run StochSS. Includes support for newest OSX and Windows operating systems.
  •  Added RSS feed to front page
  •  Many bug fixes and stability enhancements

For more details and instructions on how to obtain the code, visit us at www.StochSS.org.
Linda Petzold and Chandra Krintz
University of California Santa Barbara
 
Per Lötstedt and Andreas Hellander
Uppsala University

StochSS 1.6 Available for Download!

StochSS 1.6 is now officially released!
New capabilities of Version 1.6 include:

  • Spatial visualization now supports animation, wireframe rendering, and mesh slicing
  • FlexCloud: run ‘cloud’ jobs on dedicated hardware (in addition to using EC2)
  • Import SBML models
  • Many bug fixes and stability enhancements

Details and instructions on how to obtain the code can be found on the Download page
Tutorials are available on the Documentation page
Linda Petzold and Chandra Krintz
University of California Santa Barbara
Per Lotstedt and Andreas Hellander
Uppsala Universiteit