Predicting Evolution Using Regulatory Architecture.

IF 10.4 1区 生物学 Q1 BIOPHYSICS Annual Review of Biophysics Pub Date : 2020-05-06 Epub Date: 2020-02-04 DOI:10.1146/annurev-biophys-070317-032939
Philippe Nghe, Marjon G J de Vos, Enzo Kingma, Manjunatha Kogenaru, Frank J Poelwijk, Liedewij Laan, Sander J Tans
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引用次数: 7

Abstract

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization-in molecular recognition, within a single regulatory network, and between different networks-providing first indications of predictable features of evolutionary constraint.

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利用监管架构预测进化。
进化的极限一直让生物学家着迷。然而,由于对所研究的表型的机制理解不佳,进化约束的原因仍然难以捉摸。最近,一系列创新的方法利用了调控网络和细胞生物学的机制信息。这些方法将系统生物学模型与种群和单细胞定量以及新的遗传工具相结合,并已应用于一系列复杂的细胞功能和工程网络。在这篇文章中,我们回顾了这些进展,这些进展揭示了在不同生物组织水平上——在分子识别中,在单一调节网络中,以及在不同网络之间——上位性的机制原因,提供了进化约束的可预测特征的第一个迹象。
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来源期刊
Annual Review of Biophysics
Annual Review of Biophysics 生物-生物物理
CiteScore
21.00
自引率
0.00%
发文量
25
期刊介绍: The Annual Review of Biophysics, in publication since 1972, covers significant developments in the field of biophysics, including macromolecular structure, function and dynamics, theoretical and computational biophysics, molecular biophysics of the cell, physical systems biology, membrane biophysics, biotechnology, nanotechnology, and emerging techniques.
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