Efficient stochastic simulation of biochemical reactions with noise and delays

V.H. Thanh, R. Zunino, C. Priami

Research output: Contribution to journalArticleScientificpeer-review

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. © 2017 Author(s).
Original languageEnglish
JournalJournal of Chemical Physics
Volume146
Issue number8
DOIs
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

Keywords

  • Random number generation
  • Stochastic models
  • Stochastic systems, Biochemical reaction network
  • Biochemical reactions
  • Computational costs
  • Computationally efficient
  • Efficient formulation
  • Exact trajectories
  • Stochastic simulation algorithms
  • Stochastic simulations, Computational efficiency, algorithm
  • animal
  • biochemistry
  • biological model
  • computer simulation
  • human
  • Markov chain
  • probability, Algorithms
  • Animals
  • Biochemical Phenomena
  • Computer Simulation
  • Humans
  • Models, Biological
  • Probability
  • Stochastic Processes

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