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题名: Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity
作者: Liang, Jie ;  Qian, Hong
刊名: JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
出版日期: 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
学科分类: Physics
通讯作者: Liang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA
部门归属: [Liang, J] Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA; [Liang, J] Shanghai Jiao Tong Univ, Shanghai Ctr Syst Biomed, Shanghai 200240, Peoples R China; [Qian, H] Univ Washington, Dept Appl Math, Seattle, WA 98195 USA; [Qian, H] Chinese Acad Sci, Kavli Inst Theoret Phys China, Beijing 100190, Peoples R China
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.
资助者: US NIH[GM079804, GM081682, GM086145, GM068610]; NSF of USA[DBI-0646035, DMS-0800257]; Shanghai Jiao Tong University[T226208001]
收录类别: SCI
原文出处: 查看原文
WOS记录号: WOS:000273741700014
Citation statistics: 
内容类型: 期刊论文
URI标识: http://ir.itp.ac.cn/handle/311006/5192
Appears in Collections:理论物理所1978-2010年知识产出_期刊论文

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Liang, Jie,Qian, Hong. 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.
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