The application of parallel and distributed simulation techniques is often limited by the amount of parallelism available in the model. This holds true for large-scale cell-biological simulations, a field that has emerged as data and knowledge concerning these systems increases and biologists call for tools to guide wet-lab experimentation. A promising approach to exploit parallelism in this domain is the integration of spatial aspects, which are often crucial to a model's validity. We describe a parallel and distributed variant of the Next-Subvolume Method (NSM), a method that augments the well-known Gillespie Stochastic Simulation Algorithm (SSA) with spatial features. We discuss requirements imposed by this application on a parallel discrete event simulation engine to achieve efficient execution, and conduct a thorough performance analysis in the context of a grid-inspired simulation system called Aurora.
