Affiliations: Brandenburg University of Technology Cottbus-Senfienberg, Chair of Data Structures and Software Dependability, Postbox 1013 44, D-03013 Cottbus, Germany. Christian.rohr@ b-tu.de
Correspondence:
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Address for correspondence: Brandenburg University of Technology Cottbus-Senftenberg, Chair of Data Structures and Software Dependability, Postbox 10 13 44, D-03013 Cottbus, Germany
Abstract: We present an approach to improve the efficiency of stochastic simulation for large and dense biochemical reaction networks. We use stochastic Petri nets as modelling framework, but the proposed simulation approach is not limited to Petri net representations. The underlying continuous-time Markov chain (CTMC) is converted to an equivalent discrete-time Markov chain (DTMC); this itself gains no efficiency. We improve the efficiency via discrete-time leaps, even though this results in an approximate method. The discrete-time leaps are done by applying the maximum firing rule; this reduces drastically the number of steps. The presented algorithm is implemented in our modelling and simulation tool Snoopy, as well as in our advanced analysis and model checking tool MARCIE. We demonstrate the approach on models of different sizes and complexities.
Keywords: discrete-time leap method, stochastic Petri net, approximate stochastic simulation, maximum firing rule, binomial distribution, weighted random shuffle
