This article addresses reaction networks in which spatial and stochastic effects are of crucial
importance. For such systems, particle-based models allow us to describe all microscopic
details with high accuracy. However, they suffer from computational inefficiency if particle
numbers and density get too large. Alternative coarse-grained-resolution models reduce
computational effort tremendously, eg, by replacing the particle distribution by a continuous
concentration field governed by reaction–diffusion PDEs. We demonstrate how models on …