Efficient Constant-Time Complexity Algorithm for Stochastic Simulation of Large Reaction Networks

IEEE/ACM Transactions on Computational Biology and Bioinformatics
Vo Hong ThanhCorrado Priami

Abstract

Exact stochastic simulation is an indispensable tool for a quantitative study of biochemical reaction networks. The simulation realizes the time evolution of the model by randomly choosing a reaction to fire and update the system state according to a probability that is proportional to the reaction propensity. Two computationally expensive tasks in simulating large biochemical networks are the selection of next reaction firings and the update of reaction propensities due to state changes. We present in this work a new exact algorithm to optimize both of these simulation bottlenecks. Our algorithm employs the composition-rejection on the propensity bounds of reactions to select the next reaction firing. The selection of next reaction firings is independent of the number reactions while the update of propensities is skipped and performed only when necessary. It therefore provides a favorable scaling for the computational complexity in simulating large reaction networks. We benchmark our new algorithm with the state of the art algorithms available in literature to demonstrate its applicability and efficiency.

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Jul 3, 2015·The Journal of Chemical Physics·Vo Hong ThanhCorrado Priami
Aug 10, 2015·The Journal of Chemical Physics·Vo Hong Thanh, Corrado Priami

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Citations

Jun 17, 2016·The Journal of Chemical Physics·Vo Hong ThanhRoberto Zunino
Mar 3, 2017·The Journal of Chemical Physics·Vo Hong ThanhCorrado Priami
Jul 2, 2019·Wiley Interdisciplinary Reviews. Systems Biology and Medicine·Giulia SimoniLuca Marchetti
Feb 28, 2018·Current Opinion in Biotechnology·Lukas Andreas Widmer, Jörg Stelling
Apr 28, 2021·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·Vo Hong ThanhPekka Orponen

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