Automatic identification of model reductions for discrete stochastic simulation

作者：Wu Sheng^*; Fu Jin; Li Hong; Petzold Linda

来源：Journal of Chemical Physics, 2012, 137(3): 034106.

DOI：10.1063/1.4733563

Multiple time scales in cellular chemical reaction systems present a challenge for the efficiency of stochastic simulation. Numerous model reductions have been proposed to accelerate the simulation of chemically reacting systems by exploiting time scale separation. However, these are often identified and deployed manually, requiring expert knowledge. This is time-consuming, prone to error, and opportunities for model reduction may be missed, particularly for large models. We propose an automatic model analysis algorithm using an adaptively weighted Petri net to dynamically identify opportunities for model reductions for both the stochastic simulation algorithm and tau-leaping simulation, with no requirement of expert knowledge input. Results are presented to demonstrate the utility and effectiveness of this approach. c 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4733563]

• 出版日期2012-7-21