University of Texas at Arlington
Computational studies of biological systems have gained widespread attention as a promising alternative to regular experimentation. Within this domain, stochastic simulation algorithms are widely used for in-silico studies of biochemical reaction networks, such as gene regulatory networks. However, inherent computational complexities limit wide-spread adoption and make traditional software solutions on general-purpose computers prohibitively slow. In this paper, the authors present a specialized stochastic simulation processor that exploits fine- and coarse-grain parallelism in Gillepie's first reaction method to achieve high performance.