Binomial distribution based τ-leap accelerated stochastic simulation
Abstract
Recently, Gillespie introduced the τ-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson distribution, to enable simulation of long times. Here we introduce a binomial distribution τ-leap algorithm (abbreviated as BD-τ method). This method combines the bounded nature of the binomial distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original τ-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-τ method is more accurate than the original method for comparable coarse-graining in time.
- Publication:
-
Journal of Chemical Physics
- Pub Date:
- January 2005
- DOI:
- Bibcode:
- 2005JChPh.122b4112C
- Keywords:
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- 82.20.Db;
- 82.20.Wt;
- 05.40.-a;
- 02.70.Uu;
- 02.50.Ng;
- Transition state theory and statistical theories of rate constants;
- Computational modeling;
- simulation;
- Fluctuation phenomena random processes noise and Brownian motion;
- Applications of Monte Carlo methods;
- Distribution theory and Monte Carlo studies