Literature DB >> 19794828

Slow Scale Tau-leaping Method.

Yang Cao1, Linda Petzold.   

Abstract

For chemical systems involving both fast and slow scales, stiffness presents challenges for efficient stochastic simulation. Two different avenues have been explored to solve this problem. One is the slow-scale stochastic simulation (ssSSA) based on the stochastic partial equilibrium assumption. The other is the tau-leaping method. In this paper we propose a new algorithm, the slow-scale tau-leaping method, which combines some of the best features of these two methods. Numerical experiments are presented which illustrate the effectiveness of this approach.

Entities:  

Year:  2008        PMID: 19794828      PMCID: PMC2753989          DOI: 10.1016/j.cma.2008.02.024

Source DB:  PubMed          Journal:  Comput Methods Appl Mech Eng        ISSN: 0045-7825            Impact factor:   6.756


  16 in total

1.  Genetic networks. Small numbers of big molecules.

Authors:  Nina Fedoroff; Walter Fontana
Journal:  Science       Date:  2002-08-16       Impact factor: 47.728

2.  Comment on "Stiffness in stochastic chemically reacting systems: the implicit tau-leaping method" [J. Chem. Phys. 119, 12784 (2003)].

Authors:  Katrien De Cock; Xueying Zhang; Mónica F Bugallo; Petar M Djurić
Journal:  J Chem Phys       Date:  2004-08-15       Impact factor: 3.488

3.  Hybrid simulation of cellular behavior.

Authors:  Thomas R Kiehl; Robert M Mattheyses; Melvin K Simmons
Journal:  Bioinformatics       Date:  2004-02-12       Impact factor: 6.937

4.  Binomial leap methods for simulating stochastic chemical kinetics.

Authors:  Tianhai Tian; Kevin Burrage
Journal:  J Chem Phys       Date:  2004-12-01       Impact factor: 3.488

5.  Binomial distribution based tau-leap accelerated stochastic simulation.

Authors:  Abhijit Chatterjee; Dionisios G Vlachos; Markos A Katsoulakis
Journal:  J Chem Phys       Date:  2005-01-08       Impact factor: 3.488

6.  Adaptive explicit-implicit tau-leaping method with automatic tau selection.

Authors:  Yang Cao; Daniel T Gillespie; Linda R Petzold
Journal:  J Chem Phys       Date:  2007-06-14       Impact factor: 3.488

7.  Algorithms and software for stochastic simulation of biochemical reacting systems.

Authors:  Hong Li; Yang Cao; Linda R Petzold; Daniel T Gillespie
Journal:  Biotechnol Prog       Date:  2007-09-26

8.  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

9.  Multinomial tau-leaping method for stochastic kinetic simulations.

Authors:  Michel F Pettigrew; Haluk Resat
Journal:  J Chem Phys       Date:  2007-02-28       Impact factor: 3.488

10.  Stochastic mechanisms in gene expression.

Authors:  H H McAdams; A Arkin
Journal:  Proc Natl Acad Sci U S A       Date:  1997-02-04       Impact factor: 11.205

View more
  7 in total

1.  BioSimulator.jl: Stochastic simulation in Julia.

Authors:  Alfonso Landeros; Timothy Stutz; Kevin L Keys; Alexander Alekseyenko; Janet S Sinsheimer; Kenneth Lange; Mary E Sehl
Journal:  Comput Methods Programs Biomed       Date:  2018-10-10       Impact factor: 5.428

2.  Stiffness detection and reduction in discrete stochastic simulation of biochemical systems.

Authors:  Yang Pu; Layne T Watson; Yang Cao
Journal:  J Chem Phys       Date:  2011-02-07       Impact factor: 3.488

3.  Stochastic Simulation of Cellular Metabolism.

Authors:  Emalie J Clement; Thomas T Schulze; Ghada A Soliman; Beata J Wysocki; Paul H Davis; Tadeusz A Wysocki
Journal:  IEEE Access       Date:  2020-04-17       Impact factor: 3.367

Review 4.  The best models of metabolism.

Authors:  Eberhard O Voit
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2017-05-19

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

Authors:  Brian Drawert; Stefan Engblom; Andreas Hellander
Journal:  BMC Syst Biol       Date:  2012-06-22

6.  Stochastic Simulation of Pattern Formation in Growing Tissue: A Multilevel Approach.

Authors:  Stefan Engblom
Journal:  Bull Math Biol       Date:  2018-06-20       Impact factor: 1.758

7.  A hybrid stochastic-deterministic approach to explore multiple infection and evolution in HIV.

Authors:  Jesse Kreger; Natalia L Komarova; Dominik Wodarz
Journal:  PLoS Comput Biol       Date:  2021-12-22       Impact factor: 4.475

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.