Semantics-based parallelization for the stochastic simulation of complex cell cycle regulations

Stochastic simulation of biological systems becomes widely used, since it can intuitively account for the fluctuation of species with a few number of molecules. However, for bigger models and/or models with mixed abundance of molecules, stochastic simulation fails to produce the required results in reasonable time. Parallel simulation can offer a solution for this challenge. Nevertheless, currently available parallel software tools either provide a coarse-grained parallelization or a general-purpose fine-grained parallel simulation of the well-known stochastic simulation algorithm (SSA). The former can only take advantage of parallel processing if multiple runs have to be performed, while the latter requires extensive synchronization and communication between the different processing nodes each time a reaction is to fire. In this paper, a fine-grained parallelization approach is presented that takes advantage of the underlying model semantics to improve the simulator performance. The proposed method is applied to the yeast cell cycle regulation, which is an example of biological models that requires extensive investigation.