Path ensembles and path sampling in nonequilibrium stochastic systems.
Markovian models based on the stochastic master equation are often encountered in single molecule dynamics, reaction networks, and nonequilibrium problems in chemistry, physics, and biology. An efficient and convenient method to simulate these systems is the kinetic Monte Carlo algorithm which generates continuous-time stochastic trajectories. We discuss an alternative simulation method based on sampling of stochastic paths. Utilizing known probabilities of stochastic paths, it is possible to apply Metropolis Monte Carlo in path space to generate a desired ensemble of stochastic paths. The method is a generalization of the path sampling idea to stochastic dynamics, and is especially suited for the analysis of rare paths which are not often produced in the standard kinetic Monte Carlo procedure. Two generic examples are presented to illustrate the methodology.
Harland
Ben
B
Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA.
Sun
Sean X
SX
D000199:Actins / Q000737:chemistry* / Q000378:metabolism; D000465:Algorithms; D019295:Computational Biology; D003198:Computer Simulation; D004583:Electrons; D007700:Kinetics; D008954:Models, Biological; D009010:Monte Carlo Method; D013269:Stochastic Processes
D000199:Actins