Competition for resources can reshape the evolutionary properties of spatial structure.

IF 3.8 2区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Anush Devadhasan, Oren Kolodny, Oana Carja
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引用次数: 0

Abstract

Many evolving ecosystems have spatial structures that can be conceptualized as networks, with nodes representing individuals or homogeneous subpopulations and links the patterns of spread between them. Prior models of evolution on networks do not take ecological niche differences and eco-evolutionary interplay into account. Here, we combine a resource competition model with evolutionary graph theory to study how heterogeneous topological structure shapes evolutionary dynamics under global frequency-dependent ecological interactions. We find that the addition of ecological competition for resources can produce a reversal of roles between amplifier and suppressor networks for deleterious mutants entering the population. We show that this effect is a nonlinear function of ecological niche overlap and discuss intuition for the observed dynamics using simulations and analytical approximations. We use these theoretical results together with spatial representations from imaging data to show that, for ductal carcinoma, where tumor growth is highly spatially constrained, with cells confined to a tree-like network of ducts, the topological structure can lead to higher rates of deleterious mutant hitchhiking with metabolic driver mutations, compared to tumors characterized by different spatial topologies.

资源竞争可以重塑空间结构的进化特性。
许多不断进化的生态系统的空间结构可以被概念化为网络,节点代表个体或同质亚群,链接代表它们之间的传播模式。先前的网络演化模型没有考虑生态位差异和生态演化的相互作用。在此,我们将资源竞争模型与进化图论相结合,研究异质拓扑结构如何塑造全球频率依赖性生态相互作用下的进化动态。我们发现,对于进入种群的有害突变体来说,增加资源的生态竞争会使放大器和抑制器网络之间的角色发生逆转。我们证明了这种效应是生态位重叠的非线性函数,并通过模拟和分析近似讨论了观察到的动态直觉。我们将这些理论结果与成像数据的空间表征结合起来,证明对于导管癌,肿瘤生长受到高度空间限制,细胞被限制在树状的导管网络中,与不同空间拓扑结构的肿瘤相比,拓扑结构会导致有害突变体搭乘代谢驱动突变的便车率更高。
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来源期刊
PLoS Computational Biology
PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY
CiteScore
7.10
自引率
4.70%
发文量
820
审稿时长
2.5 months
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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