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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, afield 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 an optimistic, 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. First results of combining NSM and the grid-inspired simulation system AURORA are shown.