Literature DB >> 21476748

Michaelis-Menten speeds up tau-leaping under a wide range of conditions.

Sheng Wu1, Jin Fu, Yang Cao, Linda Petzold.   

Abstract

This paper examines the benefits of Michaelis-Menten model reduction techniques in stochastic tau-leaping simulations. Results show that although the conditions for the validity of the reductions for tau-leaping remain the same as those for the stochastic simulation algorithm (SSA), the reductions result in a substantial speed-up for tau-leaping under a different range of conditions than they do for SSA. The reason of this discrepancy is that the time steps for SSA and for tau-leaping are determined by different properties of system dynamics.

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Year:  2011        PMID: 21476748      PMCID: PMC3087420          DOI: 10.1063/1.3576123

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  8 in total

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Review 3.  Stochastic simulation of chemical kinetics.

Authors:  Daniel T Gillespie
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Authors:  Ethan A Mastny; Eric L Haseltine; James B Rawlings
Journal:  J Chem Phys       Date:  2007-09-07       Impact factor: 3.488

5.  Efficient step size selection for the tau-leaping simulation method.

Authors:  Yang Cao; Daniel T Gillespie; Linda R Petzold
Journal:  J Chem Phys       Date:  2006-01-28       Impact factor: 3.488

6.  Legitimacy of the stochastic Michaelis-Menten approximation.

Authors:  K R Sanft; D T Gillespie; L R Petzold
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Review 7.  A model for circadian oscillations in the Drosophila period protein (PER).

Authors:  A Goldbeter
Journal:  Proc Biol Sci       Date:  1995-09-22       Impact factor: 5.349

8.  The subtle business of model reduction for stochastic chemical kinetics.

Authors:  Dan T Gillespie; Yang Cao; Kevin R Sanft; Linda R Petzold
Journal:  J Chem Phys       Date:  2009-02-14       Impact factor: 3.488

  8 in total
  5 in total

1.  Adaptive deployment of model reductions for tau-leaping simulation.

Authors:  Sheng Wu; Jin Fu; Linda R Petzold
Journal:  J Chem Phys       Date:  2015-05-28       Impact factor: 3.488

2.  Accuracy of the Michaelis-Menten approximation when analysing effects of molecular noise.

Authors:  Michael J Lawson; Linda Petzold; Andreas Hellander
Journal:  J R Soc Interface       Date:  2015-05-06       Impact factor: 4.118

3.  URDME: a modular framework for stochastic simulation of reaction-transport processes in complex geometries.

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Journal:  BMC Syst Biol       Date:  2012-06-22

4.  Equilibrium distributions of simple biochemical reaction systems for time-scale separation in stochastic reaction networks.

Authors:  Bence Mélykúti; João P Hespanha; Mustafa Khammash
Journal:  J R Soc Interface       Date:  2014-08-06       Impact factor: 4.118

5.  Experimental Design for Stochastic Models of Nonlinear Signaling Pathways Using an Interval-Wise Linear Noise Approximation and State Estimation.

Authors:  Christoph Zimmer
Journal:  PLoS One       Date:  2016-09-01       Impact factor: 3.240

  5 in total

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