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处于突变-选择平衡的基因调控网络集合。

The ensemble of gene regulatory networks at mutation-selection balance.

机构信息

Network Science Institute, Northeastern University, Boston, MA, USA.

Institute for Experiential AI, Northeastern University, Boston, MA, USA.

出版信息

J R Soc Interface. 2023 Jan;20(198):20220075. doi: 10.1098/rsif.2022.0075. Epub 2023 Jan 4.

Abstract

The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a model of GRN evolution analogous to work from quasi-species theory, which is itself essentially the mutation-selection balance model from classical population genetics extended to multiple loci. With this GRN model, we prove that-across a broad spectrum of selection pressures-the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium is related to the topology of the genotype network, in particular, via a specific network centrality measure termed the eigenvector centrality. Finally, we determine the structural characteristics of GRNs that are favoured in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species theory-and thus to classical populations genetics-providing a mechanistic explanation for the observed distribution of GRNs evolving in response to various evolutionary forces, and shows how complex fitness landscapes can emerge from simple evolutionary rules.

摘要

不同表型的进化既涉及分子相互作用网络,也受其限制。当这些网络影响表达模式时,我们称之为基因调控网络(GRN)。在这里,我们开发了一种类似于准种理论的 GRN 进化模型,该理论本身本质上是经典群体遗传学中的突变-选择平衡模型,扩展到了多个基因座。通过这个 GRN 模型,我们证明了——在广泛的选择压力下——动力学收敛到 GRN 上的一个稳定分布。接下来,我们从第一性原理出发,展示了在平衡时 GRN 的频率如何与基因型网络的拓扑结构相关,特别是通过一种特定的网络中心度度量,称为特征向量中心度。最后,我们确定了在一系列选择环境和突变约束下,GRN 所具有的结构特征。我们的工作将 GRN 进化与准种理论联系起来——从而与经典的群体遗传学联系起来——为观察到的 GRN 分布提供了一种机制解释,这些分布是对各种进化力量的反应,并展示了如何从简单的进化规则中出现复杂的适应景观。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8e62/9810427/1b69267c7678/rsif20220075f01.jpg

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