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适应度景观的几何形状:峰值、形状与普遍正上位性

Geometry of fitness landscapes: peaks, shapes and universal positive epistasis.

作者信息

Crona Kristina, Krug Joachim, Srivastava Malvika

机构信息

Department of Mathematics and Statistics, American University, Washington, DC, USA.

Institute for Biological Physics, University of Cologne, Cologne, Germany.

出版信息

J Math Biol. 2023 Mar 28;86(4):62. doi: 10.1007/s00285-023-01889-6.

Abstract

Darwinian evolution is driven by random mutations, genetic recombination (gene shuffling) and selection that favors genotypes with high fitness. For systems where each genotype can be represented as a bitstring of length L, an overview of possible evolutionary trajectories is provided by the L-cube graph with nodes labeled by genotypes and edges directed toward the genotype with higher fitness. Peaks (sinks in the graphs) are important since a population can get stranded at a suboptimal peak. The fitness landscape is defined by the fitness values of all genotypes in the system. Some notion of curvature is necessary for a more complete analysis of the landscapes, including the effect of recombination. The shape approach uses triangulations (shapes) induced by fitness landscapes. The main topic for this work is the interplay between peak patterns and shapes. Because of constraints on the shapes for [Formula: see text] imposed by peaks, there are in total 25 possible combinations of peak patterns and shapes. Similar constraints exist for higher L. Specifically, we show that the constraints induced by the staircase triangulation can be formulated as a condition of universal positive epistasis, an order relation on the fitness effects of arbitrary sets of mutations that respects the inclusion relation between the corresponding genetic backgrounds. We apply the concept to a large protein fitness landscape for an immunoglobulin-binding protein expressed in Streptococcal bacteria.

摘要

达尔文进化由随机突变、基因重组(基因重排)以及对具有高适应性的基因型的选择所驱动。对于每个基因型都可表示为长度为L的位串的系统,L - 立方体图提供了可能的进化轨迹概述,其节点由基因型标记,边指向具有更高适应性的基因型。峰值(图中的汇点)很重要,因为种群可能会被困在次优峰值处。适应度景观由系统中所有基因型的适应度值定义。为了更全面地分析景观,包括重组的影响,需要某种曲率概念。形状方法使用由适应度景观诱导的三角剖分(形状)。这项工作的主要主题是峰值模式与形状之间的相互作用。由于峰值对[公式:见正文]形状的限制,峰值模式和形状总共有25种可能的组合。对于更高的L也存在类似的限制。具体而言,我们表明由阶梯三角剖分诱导的限制可以表述为普遍正上位性的条件,这是一种关于任意突变集的适应度效应的序关系,它尊重相应遗传背景之间的包含关系。我们将该概念应用于在链球菌中表达的免疫球蛋白结合蛋白的大型蛋白质适应度景观。

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