Towards a theory of evolutionary adaptation

被引:0
|
作者
Daniel L. Hartl
Clifford H. Taubes
机构
[1] Harvard University,Department of Organismic and Evolutionary Biology
[2] Harvard University,Department of Mathematics
来源
Genetica | 1998年 / 102-103卷
关键词
adaptation; mutation-selection-drift; nearly neutral mutations;
D O I
暂无
中图分类号
学科分类号
摘要
Most theoretical models in population genetics fail to deal in a realistic manner with the process of mutation. They are consequently not informative about the central evolutionary problem of the origin, progression, and limit of adaptation. Here we present an explicit distribution of phenotypes expected in an ensemble of populations under a mutation-selection-drift model that allows mutations with a distribution of adaptive values to occur randomly in time. The model of mutation is a geometrical model in which the effect of a new mutation is determined by a random angle in n dimensional space and in which the adaptive value (fitness) of an organism decreases as the square of the deviation of its phenotype from an optimum. Each new mutation is subjected to random genetic drift and fixed or lost according to its selective value and the effective population number. Time is measured in number of fixation events, so that, at any point in time, each population is regarded as genetically homogeneous. In this mutation-selection-drift model, among an ensemble of populations, the equilibrium average phenotype coincides with the optimum because the distribution of positive and negative deviations from the optimum is symmetrical. However, at equilibrium, the mean of the absolute value of the deviation from the optimum equals √\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$\sqrt {n/8Ns} $$ \end{document}, where n is the dimensionality of the trait space, N is the effective population size, and s is the selection coefficient against a mutation whose phenotype deviates by one unit from the optimum. Furthermore, at equilibrium, the average fitness across the ensemble of populations equals 1 - (n + 1)/8N. When n is sufficiently large, there is a strong mutation pressure toward the fixation of slightly deleterious mutations. This feature relates our model to the nearly neutral theory of molecular evolution.
引用
收藏
页码:525 / 533
页数:8
相关论文
共 50 条
  • [22] EVOLUTIONARY ADAPTATION
    WIEBES, JT
    [J]. ACTA BIOTHEORETICA, 1982, 31 (04) : 239 - 243
  • [23] Want climate-change adaptation? Evolutionary theory can help
    Jones, James Holland
    Ready, Elspeth
    Pisor, Anne C.
    [J]. AMERICAN JOURNAL OF HUMAN BIOLOGY, 2021, 33 (04)
  • [24] Metaheuristic optimization with dynamic strategy adaptation: An evolutionary game theory approach
    Cuevas, Erik
    Luque, Alberto
    Aguirre, Nahum
    Navarro, Mario A.
    Rodriguez, Alma
    [J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2024, 645
  • [25] Towards a Stronger Theory for Permutation-based Evolutionary Algorithms
    Doerr, Benjamin
    Ghannane, Yassine
    Ibn Brahim, Marouane
    [J]. PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'22), 2022, : 1390 - 1398
  • [26] Towards an evolutionary theory of the origin of life based on kinetics and thermodynamics
    Pascal, Robert
    Pross, Addy
    Sutherland, John D.
    [J]. OPEN BIOLOGY, 2013, 3 (11)
  • [27] Building Digital Infrastructures: Towards an Evolutionary Theory of Contextual Triggers
    Koutsikouri, Dina
    Henfridsson, Ola
    Lindgren, Rikard
    [J]. PROCEEDINGS OF THE 50TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, 2017, : 4716 - 4725
  • [28] The micro genetic algorithm 2: Towards Online adaptation in evolutionary multiobjective optimization
    Pulido, GT
    Coello, CAC
    [J]. EVOLUTIONARY MULTI-CRITERION OPTIMIZATION, PROCEEDINGS, 2003, 2632 : 252 - 266
  • [29] Towards a unified theory of adaptation in case-based reasoning
    Fuchs, B
    Lieber, J
    Mille, A
    Napoli, A
    [J]. CASE-BASED REASONING RESEARCH AND DEVELOPMENT, 1999, 1650 : 104 - 117
  • [30] Adaptation, Plasticity, and Extinction in a Changing Environment: Towards a Predictive Theory
    Chevin, Luis-Miguel
    Lande, Russell
    Mace, Georgina M.
    [J]. PLOS BIOLOGY, 2010, 8 (04)