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.