title: Efficient stochastic simulation of biochemical reactions with noise and delays.
abstract: The stochastic simulation algorithm has been used to generate exact trajectories of biochemical reaction networks. For each simulation step, the simulation selects a reaction and its firing time according to a probability that is proportional to the reaction propensity. We investigate in this paper new efficient formulations of the stochastic simulation algorithm to improve its computational efficiency. We examine the selection of the next reaction firing and reduce its computational cost by reusing the computation in the previous step. For biochemical reactions with delays, we present a new method for computing the firing time of the next reaction. The principle for computing the firing time of our approach is based on recycling of random numbers. Our new approach for generating the firing time of the next reaction is not only computationally efficient but also easy to implement. We further analyze and reduce the number of propensity updates when a delayed reaction occurred. We demonstrate the applicability of our improvements by experimenting with concrete biological models.
authors.affiliation: The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy.
authors.forename: Vo Hong
authors.identifier: 0000000345990215
authors.initials: VH
authors.lastname: Thanh
authors.affiliation: Department of Mathematics, University of Trento, Trento, Italy.
authors.forename: Roberto
authors.initials: R
authors.lastname: Zunino
authors.affiliation: The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068, Italy and Department of Mathematics, University of Trento, Trento, Italy and Department of Computer Science, Stanford University, Stanford, California 94305, USA.
authors.forename: Corrado
authors.identifier: 0000000232616235
authors.initials: C
authors.lastname: Priami
mesh_terms: D000465:Algorithms; D000818:Animals; D001669:Biochemical Phenomena; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D011336:Probability; D013269:Stochastic Processes