Linking molecular mechanisms to their evolutionary consequences: a primer

被引:1
作者
Grah, Rok [1 ]
Guet, Calin C. [1 ]
Tkacik, Gasper [1 ]
Lagator, Mato [2 ]
机构
[1] IST Austria, Am Campus 1, AT-3400 Klosterneuburg, Austria
[2] Univ Manchester, Fac Biol Med & Hlth, Sch Biol Sci, Div Evolut Infect & Genom Sci, Michael Smith Bldg, Manchester M13 9PL, England
基金
英国惠康基金;
关键词
evolution; biological complexity; mechanistic model; gene expression; TRANSCRIPTIONAL REGULATION; GENE-REGULATION; POPULATION-GENETICS; QUANTITATIVE MODEL; ESCHERICHIA-COLI; LAMBDA-REPRESSOR; FITNESS; MUTATIONS; EPISTASIS; SEQUENCES;
D O I
10.1093/genetics/iyae191
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
A major obstacle to predictive understanding of evolution stems from the complexity of biological systems, which prevents detailed characterization of key evolutionary properties. Here, we highlight some of the major sources of complexity that arise when relating molecular mechanisms to their evolutionary consequences and ask whether accounting for every mechanistic detail is important to accurately predict evolutionary outcomes. To do this, we developed a mechanistic model of a bacterial promoter regulated by 2 proteins, allowing us to connect any promoter genotype to 6 phenotypes that capture the dynamics of gene expression following an environmental switch. Accounting for the mechanisms that govern how this system works enabled us to provide an in-depth picture of how regulated bacterial promoters might evolve. More importantly, we used the model to explore which factors that contribute to the complexity of this system are essential for understanding its evolution, and which can be simplified without information loss. We found that several key evolutionary properties-the distribution of phenotypic and fitness effects of mutations, the evolutionary trajectories during selection for regulation-can be accurately captured without accounting for all, or even most, parameters of the system. Our findings point to the need for a mechanistic approach to studying evolution, as it enables tackling biological complexity and in doing so improves the ability to predict evolutionary outcomes.
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页数:21
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