Accurate stochastic simulation via the step anticipation tau-leaping (SAL) algorithm.
Stochastic simulation methods are important in modeling chemical reactions, and biological and physical stochastic processes describable as continuous-time discrete-state Markov chains with a finite number of reactant species and reactions. The current algorithm of choice, tau-leaping, achieves fast and accurate stochastic simulation by taking large time steps that leap over individual reactions. During a leap interval (t, t + tau) in tau-leaping, each reaction channel operates as a Poisson process with a constant intensity. We modify tau-leaping to allow linear and quadratic changes in reaction intensities. Because our version of tau-leaping accurately anticipates how intensities change over time, we propose calling it the step anticipation tau-leaping (SAL) algorithm. We apply SAL to four examples: Kendall's process, a two-type branching process, Ehrenfest's model of diffusion, and Michaelis-Menten enzyme kinetics. In each case, SAL is more accurate than ordinary tau-leaping. The degree of improvement varies with the situation. Near stochastic equilibrium, reaction intensities are roughly constant, and SAL and ordinary tau-leaping perform about equally well.
Sehl
Mary
M
Department of Biomathematics, David Geffen School of Medicine, University of California , Los Angeles, Los Angeles, California 90095-1766, USA.
Alekseyenko
Alexander V
AV
Lange
Kenneth L
KL
D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D008956:Models, Chemical; D013269:Stochastic Processes