Accelerating rejection-based simulation of biochemical reactions with bounded acceptance probability.
Stochastic simulation of large biochemical reaction networks is often computationally expensive due to the disparate reaction rates and high variability of population of chemical species. An approach to accelerate the simulation is to allow multiple reaction firings before performing update by assuming that reaction propensities are changing of a negligible amount during a time interval. Species with small population in the firings of fast reactions significantly affect both performance and accuracy of this simulation approach. It is even worse when these small population species are involved in a large number of reactions. We present in this paper a new approximate algorithm to cope with this problem. It is based on bounding the acceptance probability of a reaction selected by the exact rejection-based simulation algorithm, which employs propensity bounds of reactions and the rejection-based mechanism to select next reaction firings. The reaction is ensured to be selected to fire with an acceptance rate greater than a predefined probability in which the selection becomes exact if the probability is set to one. Our new algorithm improves the computational cost for selecting the next reaction firing and reduces the updating the propensities of reactions.
The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy.
Vo Hong
VH
Thanh
The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy.
Corrado
C
Priami
Department of Mathematics, University of Trento, Trento, Italy.
Roberto
R
Zunino
D000465:Algorithms; D003198:Computer Simulation; D020928:Mitogen-Activated Protein Kinases / Q000737:chemistry; D008954:Models, Biological; D011336:Probability; D017455:Receptors, IgE / Q000737:chemistry; D015398:Signal Transduction; D013269:Stochastic Processes
D017455:Receptors, IgE; D020928:Mitogen-Activated Protein Kinases