Production-passage-time approximation: A new approximation method to accelerate the simulation process of enzymatic reactions

Hiroyuki Kuwahara*, Chris J. Myers

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Given the substantial computational requirements of stochastic simulation, approximation is essential for efficient analysis of any realistic biochemical system. This paper introduces a new approximation method to reduce the computational cost of stochastic simulations of an enzymatic reaction scheme which in biochemical systems often includes rapidly changing fast reactions with enzyme and enzyme-substrate complex molecules present in very small counts. Our new method removes the substrate dissociation reaction by approximating the passage time of the formation of each enzyme-substrate complex molecule which is destined to a production reaction. This approach skips the firings of unimportant yet expensive reaction events, resulting in a substantial acceleration in the stochastic simulations of enzymatic reactions. Additionally, since all the parameters used in our new approach can be derived by the Michaelis-Menten parameters which can actually be measured from experimental data, applications of this approximation can be practical even without having full knowledge of the underlying enzymatic reaction. Here, we apply this new method to various enzymatic reaction systems, resulting in a speedup of orders of magnitude in temporal behavior analysis without any significant loss in accuracy. Furthermore, we show that our new method can perform better than some of the best existing approximation methods for enzymatic reactions in terms of accuracy and efficiency.

Original languageEnglish (US)
Pages (from-to)779-792
Number of pages14
JournalJournal of Computational Biology
Issue number7
StatePublished - Sep 1 2008
Externally publishedYes


  • Biochemical networks
  • Stochastic processes

ASJC Scopus subject areas

  • Computational Mathematics
  • Genetics
  • Molecular Biology
  • Computational Theory and Mathematics
  • Modeling and Simulation


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