We are looking for a talented individual to join our efforts on creating smart and scalable cloud services to support simulation-driven scientific discovery via large-scale computational experiments such as parameter sweeps. This is a classic and very important problem that we will approach in new ways, leveraging recent advances in cloud computing, data-intensive computing and machine learning. See the full advertisement here (Deadline Sept. 1):
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.
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.
Last week brought some great news. I am awarded the Göran Gustafsson Prize 2016 from the Gustafsson Foundation (KTH/UU). In the proposed project titled Smart Services for Scientific Discovery we will look into new and more productive ways to combine simulation software and cloud computing infrastructure to build intelligent applications for e.g. exploring an underlying model’s essential behavior.
Hellander recently received the VR Young Researcher grant, for a project titled: From Single Cells to Cancer Tumors: Multiscale Simulation of Multicellular Systems. This means that we will be expanding the group with another PhD student in early 2016. Interested candidates are encouraged to contact me to discuss opportunities.
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
After more than a year of development, we are happy to announce the release of PyURDME 1.0.0! PyURDME is a Python module for spatial stochastic simulation model development and simulation. PyURDME is connected to URDME in that is uses a modified version of its core solver. While URDME is mainly designed as an interactive Matlab toolbox that makes use of the tight connection between Comsol Multiphysics to provide an interactive modeling environment, PyURDME is an object oriented API relying only on open source software, in particular the FeniCS/Dolfin project, providing great flexibility for modelers and developers to customize computational experiments.
PyURDME has also been designed with Cloud/Distributed computing in mind, and in particular it integrates well with the IPython tools, such as IPython Notebook. We are currently working on a platform for deploying PyURDME as a Cloud appliance, with support for interactive parallel computational experiments via IPython Parallel, so check back soon for updates on this project. We are also working on integration with StochSS, which ill provide an easy-to-use UI assisted endpoint to PyURDME.