进化图论中的自循环:朋友还是敌人?

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2023-09-01 DOI:10.1371/journal.pcbi.1011387
Nikhil Sharma, Sedigheh Yagoobi, Arne Traulsen
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引用次数: 0

摘要

空间结构种群的进化动力学已经研究了很长时间。最近,重点是构建通过固定有益突变来放大选择的结构,该有益突变的概率比充分混合的群体更高,固定有害突变的概率更低。研究表明,当突变体主要出现在经常变化的节点中时,对于一个结构来说,要想充分放大选择,自环是必要的。因此,对于低突变率,自环放大器在突变选择平衡中比良好混合的群体获得更高的稳态平均适应度。但是,当突变率增加,使得固定概率不再单独描述动力学时,会发生什么?我们表明,在低突变率机制之外,自循环效应是有害的。在中等和高突变率的情况下,选择的放大器获得的稳态平均适应度低于选择的完全图和抑制器。我们还提供了突变率的估计,超过该估计,图上的突变选择动力学偏离弱突变率近似。它涉及计算相对于几个图的总体大小的平均注视时间标度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Self-loops in evolutionary graph theory: Friends or foes?

Evolutionary dynamics in spatially structured populations has been studied for a long time. More recently, the focus has been to construct structures that amplify selection by fixing beneficial mutations with higher probability than the well-mixed population and lower probability of fixation for deleterious mutations. It has been shown that for a structure to substantially amplify selection, self-loops are necessary when mutants appear predominately in nodes that change often. As a result, for low mutation rates, self-looped amplifiers attain higher steady-state average fitness in the mutation-selection balance than well-mixed populations. But what happens when the mutation rate increases such that fixation probabilities alone no longer describe the dynamics? We show that self-loops effects are detrimental outside the low mutation rate regime. In the intermediate and high mutation rate regime, amplifiers of selection attain lower steady-state average fitness than the complete graph and suppressors of selection. We also provide an estimate of the mutation rate beyond which the mutation-selection dynamics on a graph deviates from the weak mutation rate approximation. It involves computing average fixation time scaling with respect to the population sizes for several graphs.

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来源期刊
PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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