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Liang, Jie; Qian, Hong; Liang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA
Computational Cellular Dynamics Based on the Chemical Master Equation: A Challenge for Understanding Complexity
Source PublicationJOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
KeywordMonte-carlo Method Escherichia-coli Folding Kinetics Reaction Systems Lattice Model Steady-state Protein Fluctuations Thermodynamics Simulation
AbstractModern 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.
2010
ISSN1000-9000
Volume25Issue:1Pages:154-168
Subject AreaPhysics
Indexed BySCI
Funding OrganizationUS NIH[GM079804, GM081682, GM086145, GM068610]; NSF of USA[DBI-0646035, DMS-0800257]; Shanghai Jiao Tong University[T226208001] ; US NIH[GM079804, GM081682, GM086145, GM068610]; NSF of USA[DBI-0646035, DMS-0800257]; Shanghai Jiao Tong University[T226208001] ; US NIH[GM079804, GM081682, GM086145, GM068610]; NSF of USA[DBI-0646035, DMS-0800257]; Shanghai Jiao Tong University[T226208001] ; US NIH[GM079804, GM081682, GM086145, GM068610]; NSF of USA[DBI-0646035, DMS-0800257]; Shanghai Jiao Tong University[T226208001]
Citation statistics
Cited Times:27[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itp.ac.cn/handle/311006/5192
Collection理论物理所科研产出_SCI论文
Corresponding AuthorLiang, J , Univ Illinois, Dept Bioengn, Chicago, IL 60607 USA
Recommended Citation
GB/T 7714
Liang, Jie,Qian, Hong,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 Liang, Jie,Qian, Hong,&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 Liang, Jie,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.
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