Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity

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	Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity
	Qian, Hong; Liang, Jie; Liang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA
刊名	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
	2010
卷号	25 期号:1 页码:154-168
关键词	Monte-carlo Method Escherichia-coli Folding Kinetics Reaction Systems Lattice Model Steady-state Protein Fluctuations Thermodynamics Simulation
ISSN号	1000-9000
英文摘要	Modern molecular biology has always been a great source of inspiration for computational science. Half a century ago, the challenge from understanding macromolecular dynamics has led the way for computations to be part of the tool set to study molecular biology. Twenty-five years ago, the demand from genome science has inspired an entire generation of computer scientists with an interest in discrete mathematics to join the field that is now called bioinformatics. In this paper, we shall lay out a new mathematical theory for dynamics of biochemical reaction systems in a small volume (i.e., mesoscopic) in terms of a stochastic, discrete-state continuous-time formulation, called the chemical master equation (CME). Similar to the wavefunction in quantum mechanics, the dynamically changing probability landscape associated with the state space provides a fundamental characterization of the biochemical reaction system. The stochastic trajectories of the dynamics are best known through the simulations using the Gillespie algorithm. In contrast to the Metropolis algorithm, this Monte Carlo sampling technique does not follow a process with detailed balance. We shall show several examples how CMEs are used to model cellular biochemical systems. We shall also illustrate the computational challenges involved: multiscale phenomena, the interplay between stochasticity and nonlinearity, and how macroscopic determinism arises from mesoscopic dynamics. We point out recent advances in computing solutions to the CME, including exact solution of the steady state landscape and stochastic differential equations that offer alternatives to the Gilespie algorithm. We argue that the CME is an ideal system from which one can learn to understand "complex behavior" and complexity theory, and from which important biological insight can be gained.
学科主题	Physics
URL标识	查看原文
WOS记录号	WOS:000273741700014
公开日期	2012-08-02
内容类型	期刊论文
源URL	[http://ir.itp.ac.cn/handle/311006/5192]
专题	理论物理研究所_理论物理所1978-2010年知识产出
通讯作者	Liang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA
推荐引用方式 GB/T 7714	Qian, Hong,Liang, Jie,Liang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA. Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2010,25(1):154-168.
APA	Qian, Hong,Liang, Jie,&Liang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA.(2010).Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,25(1),154-168.
MLA	Qian, Hong,et al."Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 25.1(2010):154-168.