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.
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.
STEPS 3.0.0 and above provide early parallel solution for stochastic spatial reaction-diffusion and electric field simulation.
STEPS 3.6.0 and above provide a new set of APIs (API2) to speedup STEPS model development. Models developed with the old API (API1) are still supported.
STEPS 4.0.0 and above provide a new parallel solution for super large scale stochastic spatial reaction-diffusion and electric field simulation supported by distributed mesh library Omega_h.
STEPS 5.0.0 and above provide new tools for simulating vesicles and membrane rafts along with related phenomena such as endocytosis, active transport, membrane docking, fusion and release, in a parallel solution.