On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

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

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency. © 2015 AIP Publishing LLC.
Original languageEnglish
JournalJournal of Chemical Physics
Volume142
Issue number24
DOIs
Publication statusPublished - 2015
MoE publication typeA1 Journal article-refereed

Keywords

  • Data structures
  • Stochastic models
  • Stochastic systems
  • Trajectories, Biochemical network
  • Biochemical reaction network
  • Memory requirements
  • Simulation algorithms
  • Simulation and analysis
  • Simulation performance
  • Stochastic simulation algorithms
  • Stochastic simulations, Algorithms, algorithm
  • chemical model
  • statistics
  • time, Algorithms
  • Models, Chemical
  • Stochastic Processes
  • Time Factors

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