STEPS

STochastic Engine for Pathway Simulation

STEPS is a package for exact stochastic simulation of reaction-diffusion systems in arbitrarily complex 3D geometries. Our core simulation algorithm is an implementation of Gillespie's SSA, extended to deal with diffusion of molecules over the elements of a 3D tetrahedral mesh.

While it was mainly developed for simulating detailed models of neuronal signaling pathways in dendrites and around synapses, it is a general tool and can be used for studying any biochemical pathway in which spatial gradients and morphology are thought to play a role.

Since version 2.0 STEPS also supports accurate and efficient computational of local membrane potentials on tetrahedral meshes, with the addition of voltage-gated channels and currents. Tight integration between the reaction-diffusion calculations and the tetrahedral mesh potentials allows detailed coupling between molecular activity and local electrical excitability.

We have implemented STEPS as a set of Python modules, which means STEPS users can use Python scripts to control all aspects of setting up the model, generating a mesh, controlling the simulation and generating and analyzing output. The core computational routines are still implemented as C/C++ extension modules for maximal speed of execution.

Current Version: 2.2.0

Notice: We are currently migrating the STEPS release repository to Github for better supports in the future, and will release STEPS 3.0 on Github instead of Sourceforge. This website will still be used as our project homepage for information updates. (2017.02.11)

Reference

STEPS: Efficient simulation of stochastic reaction-diffusion models in realistic morphologies.

I.Hepburn, W.Chen, S.Wils and E. De Schutter

BMC Systems Biology 2012, 6:36. doi:10.1186/1752-0509-6-36