Sciope is a Python3 package supporting ML-assisted model exploration of stochastic biochemical reaction networks. It is a core library used in  Next-generation StochSS.


HASTE is a collection of tools enabling rapid construction of cloud-native, intelligent data pipelines following the model developed in the HASTE Project.

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StochSS: Stochastic Simulation Service

StochSS is an integrated development environment (IDE) for biochemical simulations. Users will make use of a GUI or APIs to provide the definition of their problem and the type of simulation or analysis to conduct. StochSS will transparently execute simulation workflows using a wide range of underlying computational systems – laptops, workstations as well as local clusters of workstations and public clouds. StochSS is developed in collaboration with the Petzold group, Crintz group and Lötstedt group. StochSS will use URDME and StochKit2 as computational backends.

MOLNs: Interactive Computational Experiments

MOLNs is a cloud appliance that will set up, start and manage a virtual platform for scalable, distributed computational experiments using (spatial) stochastic simulation software such as PyURDME ( and StochKit/Gillespy ( In addition, MOLNs by default makes FEniCS/Dolfin available as-a Service.

Since MOLNs will configure and manage a virtual IPython Cluster (with a Notebook frontend), with Numpy, SciPy and Ipython Parallel enabled, it can also be useful for general contextualization and management of dynamic, cloud-agnostic (supports EC2 and OpenStack-based clouds) virtual IPython environments, even if you are not into spatial stochastic simulations in systems biology.


PyURDME is a modeling and simulation toolkit for spatial stochastic simulations. It makes use of a modified version of the core solver of URDME ( for mesocopic simulations via the Reaction-Diffusion Master Equation (RDME), and builds on Dolfin/FeniCS ( for geometric modeling, meshing and Finite Element Assembly. pyURDME only rely on open-source dependencies, and offer an object-oriented pythonic API to construct and simulate models.


The Stochastic Simulation Algorithm (SSA) due to Gillespie is widely used to simulate biochemical reaction networks modeled as a continuous-time discrete-space Markov process. CellMC is an XSLT-based, automated SBML (Systems Biology Markup Language) model compiler capable of producing very efficient SSA executables for the Cell/BE or multicore x86 PCs. CellMC was developed by Emmet Caulfield as a part of his master’s thesis: “CellMC: An XSLT-based SBML model compiler for Cell/BE and IA32′. CellMC is no longer maintained but can still be obtained from .