Reaction factoring and bipartite update graphs accelerate the Gillespie Algorithm for large-scale biochemical systems.

ABSTRACT: ODE simulations of chemical systems perform poorly when some of the species have extremely low concentrations. Stochastic simulation methods, which can handle this case, have been impractical for large systems due to computational complexity. We observe, however, that when modeling complex biological systems: (1) a small number of reactions tend to occur a disproportionately large percentage of the time, and (2) a small number of species tend to participate in a disproportionately large percentage of reactions. We exploit these properties in LOLCAT Method, a new implementation of the Gillespie Algorithm. First, factoring reaction propensities allows many propensities dependent on a single species to be updated in a single operation. Second, representing dependencies between reactions with

SUBMITTER: Indurkhya S 

PROVIDER: S-EPMC2798956 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature