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从谱系学的角度求解迁移-重组方程。

Solving the migration-recombination equation from a genealogical point of view.

机构信息

Faculty of Mathematics, Bielefeld University, Postbox 100131, 33501, Bielefeld, Germany.

Statistics Department, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK.

出版信息

J Math Biol. 2021 Mar 27;82(5):41. doi: 10.1007/s00285-021-01584-4.

Abstract

We consider the discrete-time migration-recombination equation, a deterministic, nonlinear dynamical system that describes the evolution of the genetic type distribution of a population evolving under migration and recombination in a law of large numbers setting. We relate this dynamics (forward in time) to a Markov chain, namely a labelled partitioning process, backward in time. This way, we obtain a stochastic representation of the solution of the migration-recombination equation. As a consequence, one obtains an explicit solution of the nonlinear dynamics, simply in terms of powers of the transition matrix of the Markov chain. The limiting and quasi-limiting behaviour of the Markov chain are investigated, which gives immediate access to the asymptotic behaviour of the dynamical system. We finally sketch the analogous situation in continuous time.

摘要

我们考虑离散时间迁移-重组方程,这是一个确定性的非线性动力系统,用于描述在大规模环境下迁移和重组作用下的群体遗传类型分布的演化。我们将这种动力学(正向时间)与一个马尔可夫链(即向后时间的标记划分过程)联系起来。通过这种方式,我们得到了迁移-重组方程解的随机表示。因此,可以直接根据马尔可夫链的转移矩阵的幂次得到非线性动力学的显式解。研究了马尔可夫链的极限和准极限行为,这使得对动力系统的渐近行为可以直接进行分析。最后,我们简要概述了连续时间的类似情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22c4/8004498/fc37e587b381/285_2021_1584_Fig1_HTML.jpg

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