Fixation Probabilities When Generation Times Are Variable: The Burst–Death Model

Hubbarde, Jennifer E; Wild, Geoff; Wahl, Lindi M.

doi:10.1534/genetics.107.072009

Cited by 21 publications

(41 citation statements)

References 19 publications

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“…Two striking results have emerged from this study. First, we note that previous work has demonstrated that the fixation probability depends sensitively on this mechanism of the selective advantage (Wahl and Dehaan 2004;Lambert 2006;Hubbarde et al 2007;Parsons and Quince 2007). In particular, when generation times are fixed, mutations that increase fecundity are more likely to fix than mutations that reduce the generation time (Wahl and Dehaan 2004).…”

Section: Discussionmentioning

confidence: 81%

“…Recent work, however, has demonstrated that fixation probabilities are extremely sensitive to the underlying life-history model (e.g., Wahl and Dehaan 2004;Lambert 2006;Parsons and Quince 2007;Hubbarde et al 2007). Thus it is critical, for any quantitative estimates of the fixation probability, to find a model that is appropriate for the population and organism under study.…”

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confidence: 99%

“…When the attachment time is very long compared to the lysis time, the ''burstdeath'' life-history model, in which generation times are exponentially distributed, is appropriate (Hubbarde et al 2007;Alexander and Wahl 2008). In this article, we include both a variable attachment time and a fixed lysis time.…”

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confidence: 99%

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Fixation Probability for Lytic Viruses: The Attachment-Lysis Model

Patwa

Wahl

2008

Genetics

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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.

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Section: Discussionmentioning

confidence: 81%

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confidence: 99%

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Fixation Probability for Lytic Viruses: The Attachment-Lysis Model

Patwa

Wahl

2008

Genetics

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“…As derived previously (Hubbarde et al 2007), for a mutation that first appears at time t 0 during the growth phase, the extinction probabilities are given by Xðt 0 Þ ¼ Gðt 2 l 2 t 0 ; b½Xð0ÞÞ:…”

Section: Discussionmentioning

confidence: 96%

Survival Probability of Beneficial Mutations in Bacterial Batch Culture

Wahl

Zhu²

2015

Genetics

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The survival of rare beneficial mutations can be extremely sensitive to the organism's life history and the trait affected by the mutation. Given the tremendous impact of bacteria in batch culture as a model system for the study of adaptation, it is important to understand the survival probability of beneficial mutations in these populations. Here we develop a life-history model for bacterial populations in batch culture and predict the survival of mutations that increase fitness through their effects on specific traits: lag time, fission time, viability, and the timing of stationary phase. We find that if beneficial mutations are present in the founding population at the beginning of culture growth, mutations that reduce the mortality of daughter cells are the most likely to survive drift. In contrast, of mutations that occur de novo during growth, those that delay the onset of stationary phase are the most likely to survive. Our model predicts that approximately fivefold population growth between bottlenecks will optimize the occurrence and survival of beneficial mutations of all four types. This prediction is relatively insensitive to other model parameters, such as the lag time, fission time, or mortality rate of the population. We further estimate that bottlenecks that are more severe than this optimal prediction substantially reduce the occurrence and survival of adaptive mutations.KEYWORDS fixation probability; serial passaging; adaptation; experimental evolution; life history I T is well understood that most de novo mutations, even if they confer a substantial fitness advantage to the organism, do not survive the vicissitudes of genetic drift when initially rare (Fisher 1922;Haldane 1927;Wright 1929;Kimura 1964). The influences of population size, population structure, and environmental fluctuations on the fate of beneficial mutations have all been well studied, including cyclic (Otto and Whitlock 1997;Pollak 2000) or dynamically changing population sizes (Lambert 2006; Parsons and Quince 2007a,b), population subdivision (Barton 1993;Cherry 2003;Whitlock 2003) and migration (Lundy and Possingham 1998;Shpak and Proulx 2007), fluctuating selection (Haccou and Iwasa 1996;Lande 2007), or several of these factors in combination (Uecker and Hermisson 2011;Waxman 2011).A phenomenon that is perhaps less well appreciated is the sensitivity of a beneficial mutation's fate-survival or extinction-to the details of the organism's life history and the trait affected by the mutation. Studies of the extinction process in a population of changing size have demonstrated that extinction probabilities sensitively depend on whether mutations increase birth rates or reduce death rates (Parsons and Quince 2007a,b) or if mutations affect other life-history traits (Lambert 2006). Previous work has also compared the fate of mutations that reduce generation times with those that increase offspring survival (Wahl and Dehaan 2004). From these studies a clear picture is emerging: beneficial mutations that confer the same ...

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“…The life history of lytic viruses, however, consists of an attachment time, during which the viral particle ''searches'' for a cell to infect, and a lysis time, the time between attachment and host-cell lysis, during which virions accumulate within the cell. Our recent work has developed a life-history model appropriate for lytic viruses, and predicted the fixation probability in the presence or absence of population bottlenecks (Alexander and Wahl, 2008;Hubbarde et al, 2007;Patwa and Wahl, 2008a), neglecting any underlying host-cell dynamics.…”

Section: Introductionmentioning

confidence: 99%