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Evolutionary rescue over a phenotype fitness landscape: across the mutation rate spectrum
Post-edited
Authors : Martin, Guillaume (Author of the conference)
CIRM (Publisher )
37N25 - Dynamical systems in biology
60G99 - None of the above but in this section
35K58 - Semilinear parabolic equations
35Q92 - PDEs in connection with biology and other natural sciences
CIRM (Publisher )
Abstract :
Evolutionary rescue (ER) is the process by which a population, initially destined to extinction due to environmental stress, avoids extinction via adaptive evolution. One of the widely observed pattern of ER (especially in the study of antibiotic resistance) is that it is more likely to occur in mild than in strong stress. This may be due either to purely demographic effects (extinction is faster in strong stress) or to evolutionary effects (adaptation is harder in strong stress). Disentangling the two and predicting the likelihood of ER has important medical or agronomic implications, but also has a strong potential for empirical testing of eco-evolutionary theory, as ER experiments are widespread (at least in microbial systems) and fairly rapid to perform.
Here, I will present results from three recent articles [1-3] where we considered the probability of ER, and the distribution of extinction times, in a classic phenotype-fitness landscape: Fisher's geometric model (FGM). In our (classic) version of the FGM, fitness is a quadratic function of traits, with an optimum that depends on the environment. This model has received some empirical support with respects to its ability to reproduce or even predict patterns of context dependence in mutation effects on fitness (be it environmental or genetic context).
In our FGM-ER scenario, a population is initially adapted to the current optimum (either a clone or at mutation selection balance). The environment shifts abruptly and the optimum position, plus possibly peak height and width are modified. We follow the evolutionary and demographic response to this change, assuming a density-independent demography (which we approximate by continuous branching process CB process or Feller process).
In spite of its simplicity, the FGM displays fairly distinct behaviors depending on the relative strength of selection and mutation: this yields different approaches to deal with the FGM-ER scenario. I will thus present the different approaches we have used so far: from the strong selection, weak mutation regime to the weak mutation strong selection regime, and discuss possible extensions at the transition between these regimes.
Keywords : Evolutionary theory; applied mathematics; stochastic processes
MSC Codes : Here, I will present results from three recent articles [1-3] where we considered the probability of ER, and the distribution of extinction times, in a classic phenotype-fitness landscape: Fisher's geometric model (FGM). In our (classic) version of the FGM, fitness is a quadratic function of traits, with an optimum that depends on the environment. This model has received some empirical support with respects to its ability to reproduce or even predict patterns of context dependence in mutation effects on fitness (be it environmental or genetic context).
In our FGM-ER scenario, a population is initially adapted to the current optimum (either a clone or at mutation selection balance). The environment shifts abruptly and the optimum position, plus possibly peak height and width are modified. We follow the evolutionary and demographic response to this change, assuming a density-independent demography (which we approximate by continuous branching process CB process or Feller process).
In spite of its simplicity, the FGM displays fairly distinct behaviors depending on the relative strength of selection and mutation: this yields different approaches to deal with the FGM-ER scenario. I will thus present the different approaches we have used so far: from the strong selection, weak mutation regime to the weak mutation strong selection regime, and discuss possible extensions at the transition between these regimes.
Keywords : Evolutionary theory; applied mathematics; stochastic processes
37N25 - Dynamical systems in biology
60G99 - None of the above but in this section
35K58 - Semilinear parabolic equations
35Q92 - PDEs in connection with biology and other natural sciences
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Information on the Video
Film maker : Hennenfent, GuillaumeLanguage : English Available date : 02/03/2020 Conference Date : 10/02/2020 Subseries : Research talks arXiv category : Analysis of PDEs ; Dynamical Systems Mathematical Area(s) : Dynamical Systems & ODE ; PDE Format : MP4 (.mp4) - HD Video Time : 00:38:20 Targeted Audience : Researchers Download : https://videos.cirm-math.fr/2020-02-10_Martin.mp4 
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Information on the Event
Event Title : Thematic Month Week 2: Mathematical Models in Evolutionary Biology / Mois thématique Semaine 2 : Modèles mathématiques en biologie de l'évolutionEvent Organizers : Champagnat, Nicolas ; Coville, Jérôme ; Gomulkiewicz, Richard ; Hamel, Francois ; Roques, Lionel Dates : 10/02/2020 - 14/02/2020 Event Year : 2020 Event URL : https://conferences.cirm-math.fr/2302.html
Citation Data
DOI : 10.24350/CIRM.V.19605203Cite this video as: Martin, Guillaume (2020). Evolutionary rescue over a phenotype fitness landscape: across the mutation rate spectrum. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19605203 URI : http://dx.doi.org/10.24350/CIRM.V.19605203 |
See Also
- [Multi angle] Coevolution of habitat use in stochastic environments / Author of the conference Schreiber, Sebastian J..
- [Multi angle] The infinitesimal model of phenotypic evolution - a microscopic approach / Author of the conference Véber, Amandine.
- [Multi angle] Applying the infinitesimal model / Author of the conference Etheridge, Alison ; Author of the conference Barton, Nicholas H..
Bibliography
- OSMOND, Matthew M., OTTO, Sarah P., et MARTIN, Guillaume. Genetic paths to evolutionary rescue and the distribution of fitness effects along them. Genetics, 2020, vol. 214, no 2, p. 493-510. - http://dx.doi.org/10.1534/genetics.119.302890
- ANCIAUX, Yoann, LAMBERT, Amaury, RONCE, Ophélie, et al. Population persistence under high mutation rate: from evolutionary rescue to lethal mutagenesis. Evolution, 2019, vol. 73, no 8, p. 1517-1532. - https://doi.org/10.1111/evo.13771
- ANCIAUX, Yoann, CHEVIN, Luis-Miguel, RONCE, Ophélie, et al. Evolutionary rescue over a fitness landscape. Genetics, 2018, vol. 209, no 1, p. 265-279. - http://dx.doi.org/10.1534/genetics.118.300908
