Evolutionary graph theory beyond single mutation dynamics: on how network structured populations cross fitness landscapes.

IF 3.3 3区 生物学
Genetics Pub Date : 2024-04-18 DOI:10.1093/genetics/iyae055
Yang Ping Kuo, Oana Carja
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引用次数: 0

Abstract

Spatially-resolved datasets are revolutionizing knowledge in molecular biology, yet are under-utilized for questions in evolutionary biology. To gain insight from these large-scale datasets of spatial organization, we need mathematical representations and modeling techniques that can both capture their complexity, but also allow for mathematical tractability. Evolutionary graph theory utilizes the mathematical representation of networks as a proxy for heterogeneous population structure and has started to reshape our understanding of how spatial structure can direct evolutionary dynamics. However, previous results are derived for the case of a single new mutation appearing in the population and the role of network structure in shaping fitness landscape crossing is still poorly understood. Here we study how network structured populations cross fitness landscapes and show that even a simple extension to a two-mutational landscape can exhibit complex evolutionary dynamics that cannot be predicted using previous single-mutation results. We show how our results can be intuitively understood through the lens of how the two main evolutionary properties of a network, the amplification and acceleration factors, change the expected fate of the intermediate mutant in the population and further discuss how to link these models to spatially-resolved datasets of cellular organization.
超越单一突变动力学的进化图论:网络结构种群如何跨越适应性景观。
空间分辨数据集正在彻底改变分子生物学知识,但在进化生物学问题上却未得到充分利用。为了从这些大规模的空间组织数据集中获得洞察力,我们需要既能捕捉其复杂性,又具有数学可操作性的数学表征和建模技术。进化图论利用网络的数学表示作为异质种群结构的代表,已经开始重塑我们对空间结构如何引导进化动态的理解。然而,以往的结果都是针对种群中出现单个新突变的情况得出的,人们对网络结构在塑造适应性景观交叉中的作用仍然知之甚少。在这里,我们研究了网络结构种群如何跨越适应性景观,并表明即使是简单扩展到双突变景观,也会表现出复杂的进化动态,而这是以前的单突变结果无法预测的。我们展示了如何通过网络的两个主要进化特性--放大因子和加速因子--如何改变种群中中间突变体的预期命运来直观地理解我们的结果,并进一步讨论了如何将这些模型与细胞组织的空间分辨数据集联系起来。
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来源期刊
Genetics
Genetics 生物-遗传学
CiteScore
6.20
自引率
6.10%
发文量
177
期刊介绍: GENETICS is published by the Genetics Society of America, a scholarly society that seeks to deepen our understanding of the living world by advancing our understanding of genetics. Since 1916, GENETICS has published high-quality, original research presenting novel findings bearing on genetics and genomics. The journal publishes empirical studies of organisms ranging from microbes to humans, as well as theoretical work. While it has an illustrious history, GENETICS has changed along with the communities it serves: it is not your mentor''s journal. The editors make decisions quickly – in around 30 days – without sacrificing the excellence and scholarship for which the journal has long been known. GENETICS is a peer reviewed, peer-edited journal, with an international reach and increasing visibility and impact. All editorial decisions are made through collaboration of at least two editors who are practicing scientists. GENETICS is constantly innovating: expanded types of content include Reviews, Commentary (current issues of interest to geneticists), Perspectives (historical), Primers (to introduce primary literature into the classroom), Toolbox Reviews, plus YeastBook, FlyBook, and WormBook (coming spring 2016). For particularly time-sensitive results, we publish Communications. As part of our mission to serve our communities, we''ve published thematic collections, including Genomic Selection, Multiparental Populations, Mouse Collaborative Cross, and the Genetics of Sex.
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