  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.
