To make it easy to try StochSS, our software for rapid model development and simulation of stochastic regulatory networks, we are now providing it as a service on http://try.stochss.org. To use it, simply follow the link to set up your account.
Since this only require a modern browser and a valid email address (no software installation), we hope that this service will help experienced modelers evaluate the software, and importantly, that it will reduce the barrier for new modelers to explore the possibilities of stochastic simulations in systems biology.
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
After testing StochSS, if you think it will be useful in your research, there are multiple options for you to use it 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 reserach 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/orchestration framework for large-scale computational experiments such as ensembles and parameter sweeps, backed by Jupyther and Ipython Parallel.