Exact on-lattice stochastic reaction-diffusion simulations using partial-propensity methods.
Stochastic reaction-diffusion systems frequently exhibit behavior that is not predicted by deterministic simulation models. Stochastic simulation methods, however, are computationally expensive. We present a more efficient stochastic reaction-diffusion simulation algorithm that samples realizations from the exact solution of the reaction-diffusion master equation. The present algorithm, called partial-propensity stochastic reaction-diffusion (PSRD) method, uses an on-lattice discretization of the reaction-diffusion system and relies on partial-propensity methods for computational efficiency. We describe the algorithm in detail, provide a theoretical analysis of its computational cost, and demonstrate its computational performance in benchmarks. We then illustrate the application of PSRD to two- and three-dimensional pattern-forming Gray-Scott systems, highlighting the role of intrinsic noise in these systems.
MOSAIC Group, Institute of Theoretical Computer Science and Swiss Institute of Bioinformatics, ETH Zurich, Zürich, Switzerland. rajeshr@ethz.ch
Rajesh
R
Ramaswamy
Ivo F
IF
Sbalzarini
D000465:Algorithms; D003198:Computer Simulation; D004058:Diffusion; D008956:Models, Chemical; D013269:Stochastic Processes