Accelerating rejection-based simulation of biochemical reactions with bounded acceptance probability

Vo Hong Thanh, Corrado Priami, Roberto Zunino

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number224108
JournalJournal of Chemical Physics
Volume144
Issue number22
DOIs
Publication statusPublished - 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Probability
  • Stochastic models
  • Stochastic systems, Approximate algorithms
  • Biochemical reaction network
  • Biochemical reactions
  • Computational costs
  • Simulation algorithms
  • Simulation approach
  • Small population
  • Stochastic simulations, Reaction rates, immunoglobulin E receptor
  • mitogen activated protein kinase, algorithm
  • biological model
  • chemistry
  • computer simulation
  • Markov chain
  • probability
  • signal transduction, Algorithms
  • Computer Simulation
  • Mitogen-Activated Protein Kinases
  • Models, Biological
  • Receptors, IgE
  • Signal Transduction
  • Stochastic Processes

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