Underlying principles of natural selection in network evolution: systems biology approach.

IF 1.5 4区 生物学 Q4 EVOLUTIONARY BIOLOGY Evolutionary Bioinformatics Pub Date : 2007-09-26
Bor-Sen Chen, Wei-Sheng Wu
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引用次数: 0

Abstract

Systems biology is a rapidly expanding field that integrates diverse areas of science such as physics, engineering, computer science, mathematics, and biology toward the goal of elucidating the underlying principles of hierarchical metabolic and regulatory systems in the cell, and ultimately leading to predictive understanding of cellular response to perturbations. Because post-genomics research is taking place throughout the tree of life, comparative approaches offer a way for combining data from many organisms to shed light on the evolution and function of biological networks from the gene to the organismal level. Therefore, systems biology can build on decades of theoretical work in evolutionary biology, and at the same time evolutionary biology can use the systems biology approach to go in new uncharted directions. In this study, we present a review of how the post-genomics era is adopting comparative approaches and dynamic system methods to understand the underlying design principles of network evolution and to shape the nascent field of evolutionary systems biology. Finally, the application of evolutionary systems biology to robust biological network designs is also discussed from the synthetic biology perspective.

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网络进化中自然选择的基本原则:系统生物学方法。
系统生物学是一个迅速发展的领域,它整合了物理学、工程学、计算机科学、数学和生物学等不同科学领域,旨在阐明细胞中分层代谢和调控系统的基本原理,并最终预测细胞对扰动的反应。由于后基因组学研究遍及整个生命树,因此比较方法提供了一种途径,将来自许多生物体的数据结合起来,揭示从基因到生物体水平的生物网络的进化和功能。因此,系统生物学可以建立在进化生物学数十年理论研究的基础之上,同时,进化生物学也可以利用系统生物学的方法去探索新的未知方向。在本研究中,我们回顾了后基因组学时代如何采用比较方法和动态系统方法来理解网络进化的基本设计原理,以及如何塑造新生的进化系统生物学领域。最后,我们还从合成生物学的角度讨论了进化系统生物学在稳健生物网络设计中的应用。
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来源期刊
Evolutionary Bioinformatics
Evolutionary Bioinformatics 生物-进化生物学
CiteScore
4.20
自引率
0.00%
发文量
25
审稿时长
12 months
期刊介绍: Evolutionary Bioinformatics is an open access, peer reviewed international journal focusing on evolutionary bioinformatics. The journal aims to support understanding of organismal form and function through use of molecular, genetic, genomic and proteomic data by giving due consideration to its evolutionary context.
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