Automated Abstraction Methodology for Genetic Regulatory Networks
Automated Abstraction Methodology for Genetic Regulatory Networks
复制标题
遗传调控网络的自动抽象方法
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
A. Arkin
中科院分区：
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作者：
Hiroyuki Kuwahara;C. Myers;M. Samoilov;Nathan A. Barker;A. Arkin
为了有效地分析在生物环境中经常遇到的复杂的调节系统，抽象是必不可少的。本文提出了一种自动化的抽象方法，系统地降低了小规模的复杂性，在遗传调控网络模型，同时广泛地保留了大规模的系统行为。我们的方法首先通过使用快速平衡和准稳态近似以及一些其他化学计量简化技术来减少反应的数量，这些技术共同导致大大缩短了模拟时间。为了进一步减少分析时间，我们的方法可以表示系统的分子状态由一组缩放布尔（或n元）离散水平。这导致化学主方程由具有小得多的状态空间的马尔可夫链近似，从而提供显著的分析时间加速和可计算性增益。本文以噬菌体λ裂解/溶原性决定开关的遗传调控网络为例，说明了我们的方法的实际应用。
In order to efficiently analyze the complicated regulatory systems often encountered in biological settings, abstraction is essential. This paper presents an automated abstraction methodology that systematically reduces the small-scale complexity found in genetic regulatory network models, while broadly preserving the large-scale system behavior. Our method first reduces the number of reactions by using rapid equilibrium and quasi-steady-state approximations as well as a number of other stoichiometry-simplifying techniques, which together result in substantially shortened simulation time. To further reduce analysis time, our method can represent the molecular state of the system by a set of scaled Boolean (or n-ary) discrete levels. This results in a chemical master equation that is approximated by a Markov chain with a much smaller state space providing significant analysis time acceleration and computability gains. The genetic regulatory network for the phage λ lysis/lysogeny decision switch is used as an example throughout the paper to help illustrate the practical applications of our methodology.