title: Automatic identification of model reductions for discrete stochastic simulation.
abstract: 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.
authors: Department of Computer Science, University of California Santa Barbara, Santa Barbara, California 93106, USA. sheng@cs.ucsb.edu
authors: Sheng
authors: S
authors: Wu
authors: Jin
authors: J
authors: Fu
authors: Hong
authors: H
authors: Li
authors: Linda
authors: L
authors: Petzold
mesh_terms: D000465:Algorithms; D000818:Animals; D001777:Blood Coagulation; D001779:Blood Coagulation Factors / Q000378:metabolism; D003198:Computer Simulation; D004926:Escherichia coli / Q000502:physiology; D018869:Heat-Shock Response; D006801:Humans; D008954:Models, Biological; D008956:Models, Chemical; D012984:Software; D013269:Stochastic Processes
chemical_list: D001779:Blood Coagulation Factors