Experimental evolution of protein-protein interaction networks

被引:15
|
作者
Kacar, Betuel [1 ,2 ,3 ]
Gaucher, Eric A. [2 ,4 ]
机构
[1] NASA Astrobiol Inst, Mountain View, CA 94035 USA
[2] Georgia Inst Technol, Sch Biol, Atlanta, GA 30322 USA
[3] Blue Marble Space Inst Sci, Seattle, WA 98145 USA
[4] Georgia Inst Technol, Sch Chem & Biochem, Atlanta, GA 30322 USA
关键词
ancestral sequence reconstruction; evolutionary biochemistry of protein network; experimental evolution; network evolution; protein interaction network; HIGHLY EXPRESSED PROTEINS; ESCHERICHIA-COLI; EF-TU; ADENYLATE KINASE; GENES; YEAST; TOPOLOGY; REVEALS; BIOLOGY; SYSTEMS;
D O I
10.1042/BJ20130205
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks.
引用
收藏
页码:311 / 319
页数:9
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