Evolutionary accessibility of random and structured fitness landscapes

IF 2.2 3区 物理与天体物理 Q2 MECHANICS Journal of Statistical Mechanics: Theory and Experiment Pub Date : 2024-04-03 DOI:10.1088/1742-5468/ad3197
Joachim Krug, Daniel Oros
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Abstract

Biological evolution can be conceptualized as a search process in the space of gene sequences guided by the fitness landscape, a mapping that assigns a measure of reproductive value to each genotype. Here, we discuss probabilistic models of fitness landscapes with a focus on their evolutionary accessibility, where a path in a fitness landscape is said to be accessible if the fitness values encountered along the path increase monotonically. For uncorrelated (random) landscapes with independent and identically distributed fitness values, the probability of existence of accessible paths between genotypes at a distance linear in the sequence length L becomes nonzero at a nontrivial threshold value of the fitness difference between the initial and final genotypes, which can be explicitly computed for large classes of genotype graphs. The behaviour of uncorrelated random landscapes is contrasted with landscape models that display additional, biologically motivated structural features. In particular, landscapes defined by a tradeoff between adaptation to environmental extremes have been found to display a combinatorially large number of accessible paths to all local fitness maxima. We show that this property is characteristic of a broad class of models that satisfy a certain global constraint, and provide further examples from this class.
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随机和结构适应性景观的进化可达性
生物进化可以被概念化为在基因序列空间中的搜索过程,而搜索过程是由适应度景观(Fitness landscape)引导的,这种映射为每种基因型赋予了一定的繁殖价值。在这里,我们讨论了适应度景观的概率模型,重点是其进化的可及性,如果在适应度景观中遇到的适应度值沿路径单调增加,则称该路径为可及性路径。对于具有独立且同分布适配值的非相关(随机)景观,在初始基因型和最终基因型之间的适配值差异达到一个非微不足道的临界值时,在距离与序列长度 L 成线性关系的基因型之间存在可访问路径的概率将变为非零,而这个临界值可以明确地计算出大类基因型图谱。无相关随机景观的行为与景观模型形成了鲜明对比,后者显示出更多生物结构特征。特别是,通过对环境极端适应性的权衡而定义的景观被发现显示了大量通向所有局部适应性最大值的组合路径。我们证明了这一特性是满足特定全局约束条件的一大类模型的特征,并提供了该类模型的更多实例。
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来源期刊
CiteScore
4.50
自引率
12.50%
发文量
210
审稿时长
1.0 months
期刊介绍: JSTAT is targeted to a broad community interested in different aspects of statistical physics, which are roughly defined by the fields represented in the conferences called ''Statistical Physics''. Submissions from experimentalists working on all the topics which have some ''connection to statistical physics are also strongly encouraged. The journal covers different topics which correspond to the following keyword sections. 1. Quantum statistical physics, condensed matter, integrable systems Scientific Directors: Eduardo Fradkin and Giuseppe Mussardo 2. Classical statistical mechanics, equilibrium and non-equilibrium Scientific Directors: David Mukamel, Matteo Marsili and Giuseppe Mussardo 3. Disordered systems, classical and quantum Scientific Directors: Eduardo Fradkin and Riccardo Zecchina 4. Interdisciplinary statistical mechanics Scientific Directors: Matteo Marsili and Riccardo Zecchina 5. Biological modelling and information Scientific Directors: Matteo Marsili, William Bialek and Riccardo Zecchina
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