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在波动适应度景观上的克劳-木村进化模型的一个解。

A solution of the Crow-Kimura evolution model on fluctuating fitness landscape.

作者信息

Suvorov Vladimir, SaAkian David B, Lynch Michael

机构信息

Auriga Inc. - 400 TradeCenter, Ste 5900 Woburn, MA 01801, USA.

A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation - 2 Alikhanian Brothers St., Yerevan 375036, Armenia.

出版信息

Europhys Lett. 2023 Jun;142(5). doi: 10.1209/0295-5075/acd65b. Epub 2023 Jun 1.

Abstract

The article discusses the Crow-Kimura model in the context of random transitions between different fitness landscapes. The duration of epochs, during which the fitness landscape is constant over time, is modeled by an exponential distribution. To obtain an exact solution, a system of functional equations is required. However, to approximate the model, we consider the cases of slow or fast transitions and calculate the first-order corrections using either the transition rate or its inverse. Specifically, we focus on the case of slow transitions and find that the average fitness is equal to the average fitness for evolution on static fitness landscapes, but with the addition of a load term. We also investigate the model for a small number of genes and identify the exact transition points to the transient phase.

摘要

本文在不同适应度景观之间随机转变的背景下讨论了克劳-木村模型。适应度景观随时间保持恒定的时期持续时间由指数分布建模。为了获得精确解,需要一个泛函方程组。然而,为了近似该模型,我们考虑缓慢或快速转变的情况,并使用转变速率或其倒数来计算一阶修正。具体而言,我们关注缓慢转变的情况,发现平均适应度等于在静态适应度景观上进化的平均适应度,但增加了一个负荷项。我们还研究了少量基因的模型,并确定了到瞬态阶段的精确转变点。

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本文引用的文献

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Phys Rev E. 2018 Jul;98(1-1):012414. doi: 10.1103/PhysRevE.98.012414.
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