Efficient formulations for exact stochastic simulation of chemical systems.
One can generate trajectories to simulate a system of chemical reactions using either Gillespie's direct method or Gibson and Bruck's next reaction method. Because one usually needs many trajectories to understand the dynamics of a system, performance is important. In this paper, we present new formulations of these methods that improve the computational complexity of the algorithms. We present optimized implementations, available from http://cain.sourceforge.net/, that offer better performance than previous work. There is no single method that is best for all problems. Simple formulations often work best for systems with a small number of reactions, while some sophisticated methods offer the best performance for large problems and scale well asymptotically. We investigate the performance of each formulation on simple biological systems using a wide range of problem sizes. We also consider the numerical accuracy of the direct and the next reaction method. We have found that special precautions must be taken in order to ensure that randomness is not discarded during the course of a simulation.
Center for Advanced Computing Research, California Institute of Technology, M/C 158-79, Pasadena, CA 91125, USA. sean@caltech.edu
Sean
S
Mauch
Mark
M
Stalzer
D000465:Algorithms; D055598:Chemical Phenomena; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005796:Genes; D007700:Kinetics; D012313:RNA; D013269:Stochastic Processes
D012313:RNA