Abstract
STEPS is a stochastic reaction-diffusion simulation engine that implements a spatial extension of Gillespie's Stochastic Simulation Algorithm (SSA) in complex tetrahedral geometries. An extensive Python-based interface is provided to STEPS so that it can interact with the large number of scientific packages in Python. However, a gap existed between the interfaces of these packages and the STEPS user interface, where supporting toolkits could reduce the amount of scripting required for research projects. This paper introduces two new supporting toolkits that support geometry preparation and visualization for STEPS simulations.
Highlights
Advanced research on neuronal signaling pathways frequently requires assistance from computational modeling and simulations, causing the development of several molecular reactiondiffusion simulators in recent years
A generic mesh importing mechanism is provided, together with importing functions for common mesh formats such as Abaqus, TetGen, and Gmsh. To further enhance this interaction, we developed a Python-based toolkit that integrates STEPS with CUBIT, a sophisticated surface/volume mesh generator
IP3R on the membrane between Endoplasmic Reticulum (ER) and cytosol of a spine can be opened by first binding with cytosolic IP3 and Ca2+, or can be inactivated by binding with Ca2+ directly
Summary
Advanced research on neuronal signaling pathways frequently requires assistance from computational modeling and simulations, causing the development of several molecular reactiondiffusion simulators in recent years. In this domain, the general assumption of mass action kinetics in a well-mixed volume is often invalid, whilst stochasticity and spatiality have been demonstrated to play essential roles in regulating behaviors of the system (Santamaria et al, 2006; Antunes and De Schutter, 2012; Anwar et al, 2013). Several spatial stochastic reaction-diffusion simulators have been developed, following two fundamentally different approaches; particle-based and voxel-based. A commonly used approach in stochastic voxel-based simulators is Gillespie’s Stochastic Simulation Algorithm (Gillespie, 1977), which can be extended to deal with diffusion, referred to as “spatial SSA” or “spatial Gillespie.” Simulators that fall into this category include MesoRD (Hattne et al, 2005) and NeuroRD (Kotaleski and Blackwell, 2010), which implement variations of SSA in cubic meshes
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