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Applications + Practical Conceptualization + Mathematics = fruitful InnovationModeling Biochemical Reaction Systems with Markov Chains

Applications + Practical Conceptualization + Mathematics = fruitful Innovation: Modeling... [A biochemical reaction system is a complex network of several species interacting through different reaction channels. Modern experimental techniques have conclusively testified to the stochastic nature of these systems, particularly those with small molecular counts of reactant species. The stochastic nature of such systems can be successfully modeled by an appropriate continuous time Markov chain. The aim of this note is to describe the framework of this Markov modeling approach and then focus on different simulation techniques that are particularly beneficial to the practitioners. Our discussion includes certain approximate simulation schemes, which are computationally efficient for simulating large multiscale systems compared to the statistically exact ones.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Applications + Practical Conceptualization + Mathematics = fruitful InnovationModeling Biochemical Reaction Systems with Markov Chains

Part of the Mathematics for Industry Book Series (volume 11)
Editors: Anderssen, Robert S.; Broadbridge, Philip; Fukumoto, Yasuhide; Kajiwara, Kenji; Takagi, Tsuyoshi; Verbitskiy, Evgeny; Wakayama, Masato
Ganguly, Arnab
Applications + Practical Conceptualization + Mathematics = fruitful Innovation — Sep 19, 2015
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Publisher
Springer Japan
Copyright
© Springer Japan 2016
ISBN
978-4-431-55341-0
Pages
61 –74
DOI
10.1007/978-4-431-55342-7_6
Publisher site
See Chapter on Publisher Site

Abstract

[A biochemical reaction system is a complex network of several species interacting through different reaction channels. Modern experimental techniques have conclusively testified to the stochastic nature of these systems, particularly those with small molecular counts of reactant species. The stochastic nature of such systems can be successfully modeled by an appropriate continuous time Markov chain. The aim of this note is to describe the framework of this Markov modeling approach and then focus on different simulation techniques that are particularly beneficial to the practitioners. Our discussion includes certain approximate simulation schemes, which are computationally efficient for simulating large multiscale systems compared to the statistically exact ones.]

Published: Sep 19, 2015

Keywords: Stochastic simulation; Biochemical reactions; Markov chains; Tau leaping; Diffusion approximation

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