赖特-费舍和艾伦-卡恩群体遗传学模型中的伯努利工厂和对偶性

IF 1.2 4区 生物学 Q4 ECOLOGY Theoretical Population Biology Pub Date : 2024-01-30 DOI:10.1016/j.tpb.2024.01.002
Jere Koskela , Krzysztof Łatuszyński , Dario Spanò
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引用次数: 0

摘要

遗传进化的数学模型通常是成对的,通过所谓的对偶关系连接起来。最具开创性的例子是赖特-费舍扩散模型和金曼凝聚模型,前者描述了一个大群体中中性等位基因频率随时间向前的随机演化,后者描述了从群体中随机取样的个体随时间向后的遗传祖先。二元性不仅提供了比任何一个孤立模型都更丰富的描述,而且经常产生满足相关量的方程。我们利用所谓的伯努利工厂--基于模拟计算的著名工具--推导出各类遗传学模型的对偶关系。作为具体例子,我们介绍了具有一般漂移函数的赖特-费舍扩散方程和具有一般非线性强迫项的艾伦-卡恩方程。漂移函数和强迫函数可以解释为频率选择的作用。据我们所知,这项研究首次将伯努利理论与种群遗传学模型中的对偶性联系起来。
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Bernoulli factories and duality in Wright–Fisher and Allen–Cahn models of population genetics

Mathematical models of genetic evolution often come in pairs, connected by a so-called duality relation. The most seminal example are the Wright–Fisher diffusion and the Kingman coalescent, where the former describes the stochastic evolution of neutral allele frequencies in a large population forwards in time, and the latter describes the genetic ancestry of randomly sampled individuals from the population backwards in time. As well as providing a richer description than either model in isolation, duality often yields equations satisfied by quantities of interest. We employ the so-called Bernoulli factory – a celebrated tool in simulation-based computing – to derive duality relations for broad classes of genetics models. As concrete examples, we present Wright–Fisher diffusions with general drift functions, and Allen–Cahn equations with general, nonlinear forcing terms. The drift and forcing functions can be interpreted as the action of frequency-dependent selection. To our knowledge, this work is the first time a connection has been drawn between Bernoulli factories and duality in models of population genetics.

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来源期刊
Theoretical Population Biology
Theoretical Population Biology 生物-进化生物学
CiteScore
2.50
自引率
14.30%
发文量
43
审稿时长
6-12 weeks
期刊介绍: An interdisciplinary journal, Theoretical Population Biology presents articles on theoretical aspects of the biology of populations, particularly in the areas of demography, ecology, epidemiology, evolution, and genetics. Emphasis is on the development of mathematical theory and models that enhance the understanding of biological phenomena. Articles highlight the motivation and significance of the work for advancing progress in biology, relying on a substantial mathematical effort to obtain biological insight. The journal also presents empirical results and computational and statistical methods directly impinging on theoretical problems in population biology.
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