Mesoscopic-microscopic spatial stochastic simulation with automatic system partitioning
This work addresses the bottleneck of manual system partitioning in multiscale stochastic simulations, making hybrid methods more practical as black-box tools for computational biology and chemistry.
The paper proposes a hybrid mesoscopic-microscopic simulation algorithm that automatically partitions a system based on error estimates, eliminating the need for manual partitioning. The method achieves accuracy comparable to full microscopic simulations while being orders of magnitude faster for diffusion-controlled networks in 3D.
The reaction-diffusion master equation (RDME) is a model that allows for efficient on-lattice simulation of spatially resolved stochastic chemical kinetics. Compared to off-lattice hard-sphere simulations with Brownian Dynamics (BD) or Green's Function Reaction Dynamics (GFRD) the RDME can be orders of magnitude faster if the lattice spacing can be chosen coarse enough. However, strongly diffusion-controlled reactions mandate a very fine mesh resolution for acceptable accuracy. It is common that reactions in the same model differ in their degree of diffusion control and therefore require different degrees of mesh resolution. This renders mesoscopic simulation inefficient for systems with multiscale properties. Mesoscopic-microscopic hybrid methods address this problem by resolving the most challenging reactions with a microscale, off-lattice simulation. However, all methods to date require manual partitioning of a system, effectively limiting their usefulness as 'black-box' simulation codes. In this paper we propose a hybrid simulation algorithm with automatic system partitioning based on indirect a priori error estimates. We demonstrate the accuracy and efficiency of the method on models of diffusion-controlled networks in 3D.