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裂解性病毒的固定概率:吸附-裂解模型

Fixation probability for lytic viruses: the attachment-lysis model.

作者信息

Patwa Z, Wahl L M

机构信息

Department of Applied Mathematics, University of Western Ontario, London, Ontario N6A 5B7, Canada.

出版信息

Genetics. 2008 Sep;180(1):459-70. doi: 10.1534/genetics.108.090555. Epub 2008 Aug 30.

Abstract

The fixation probability of a beneficial mutation is extremely sensitive to assumptions regarding the organism's life history. In this article we compute the fixation probability using a life-history model for lytic viruses, a key model organism in experimental studies of adaptation. The model assumes that attachment times are exponentially distributed, but that the lysis time, the time between attachment and host cell lysis, is constant. We assume that the growth of the wild-type viral population is controlled by periodic sampling (population bottlenecks) and also include the possibility that clearance may occur at a constant rate, for example, through washout in a chemostat. We then compute the fixation probability for mutations that increase the attachment rate, decrease the lysis time, increase the burst size, or reduce the probability of clearance. The fixation probability of these four types of beneficial mutations can be vastly different and depends critically on the time between population bottlenecks. We also explore mutations that affect lysis time, assuming that the burst size is constrained by the lysis time, for experimental protocols that sample either free phage or free phage and artificially lysed infected cells. In all cases we predict that the fixation probability of beneficial alleles is remarkably sensitive to the time between population bottlenecks.

摘要

有益突变的固定概率对有关生物体生活史的假设极为敏感。在本文中,我们使用一种针对裂解性病毒的生活史模型来计算固定概率,裂解性病毒是适应性实验研究中的关键模型生物。该模型假设附着时间呈指数分布,但裂解时间(即从附着到宿主细胞裂解的时间)是恒定的。我们假设野生型病毒群体的增长受定期抽样(种群瓶颈)控制,并且还考虑了例如通过恒化器中的洗出以恒定速率发生清除的可能性。然后,我们计算增加附着率、减少裂解时间、增加爆发大小或降低清除概率的突变的固定概率。这四种有益突变的固定概率可能有很大差异,并且严重依赖于种群瓶颈之间的时间。我们还探索影响裂解时间的突变,假设爆发大小受裂解时间限制,用于对游离噬菌体或游离噬菌体和人工裂解的感染细胞进行抽样的实验方案。在所有情况下,我们预测有益等位基因的固定概率对种群瓶颈之间的时间非常敏感。

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