Spatial Stochastic Modeling with MCell and CellBlender

TL;DR

This chapter introduces spatial stochastic simulation methods, focusing on MCell software for 3D biochemical modeling, and provides tutorials on building and analyzing complex models with spatially-resolved stochastic dynamics.

Contribution

It offers a comprehensive overview of MCell algorithms, theory, and practical tutorials for modeling spatial stochastic biochemical systems in 3D geometries.

Findings

MCell enables detailed particle-based simulations in complex geometries.

Tutorials demonstrate basic to advanced modeling features.

Examples highlight importance of spatially-resolved stochastic dynamics.

Abstract

This chapter provides a brief introduction to the theory and practice of spatial stochastic simulations. It begins with an overview of different methods available for biochemical simulations highlighting their strengths and limitations. Spatial stochastic modeling approaches are indicated when diffusion is relatively slow and spatial inhomogeneities involve relatively small numbers of particles. The popular software package MCell allows particle-based stochastic simulations of biochemical systems in complex three dimensional (3D) geometries, which are important for many cell biology applications. Here, we provide an overview of the simulation algorithms used by MCell and the underlying theory. We then give a tutorial on building and simulating MCell models using the CellBlender graphical user interface, that is built as a plug-in to Blender, a widely-used and freely available software platform for 3D modeling. The tutorial starts with simple models that demonstrate basic MCell functionality and then advances to a number of more complex examples that demonstrate a range of features and provide examples of important biophysical effects that require spatially-resolved stochastic dynamics to capture.