Modélisation des dynamiques adaptatives de la levure de boulanger S. cerevisae dans un environnement saisonnier

Abstract : Adaptation of species to their environment involves combinations of traits, and in particular life history traits, that influence an organism's selective value. To understand the complexity of adaptation, it is appropriate to decipher the contributions of traits to fitness in the presence of different biotic and abiotic environments. In this thesis, I have investigated fitness components when the environment is seasonal, revealing how such components drive the evolutionary dynamics of quantitative traits.My work is based on the mathematical modeling of experimental evolutions in successive batch cultures of Saccharomyces cerevisiae (baker's yeast). The life cycle of this yeast species is of the respiration-fermentation type: (i) in the presence of glucose, it grows by fermentation, transforming glucose into ethanol; (ii) once glucose has been consumed, it grows by respiration, consuming this time ethanol. This sequence corresponds to the two « seasons » in a batch culture and leads to a cycle of successive batches if cells are periodically transferred into fresh medium. By using differential equations for the time courses, my thesis work shows how growth dynamics and environmental features (abiotic or biotic) generate selection pressures on the different traits during these successive seasons, thereby determining evolutionary trajectories.To describe batch dynamics, I first developed and calibrated a set of differential equations describing the growth dynamics of a population of yeast cells throughout a batch, allowing for one or multiple strains to be present (Chapter 1). Based on this model where cells divide without changing genotype, I then showed that a strain's fitness can be understood in terms of just a few components that are easily specified mathematically. I was then able to determine which traits were under selection and how the corresponding selection pressures were affected by the abundances of each strain in the yeast population (Chapter 2). Selected traits were found to be of two types: life history traits associated with growth and mortality rates, and “transition” traits that correspond to the way a strain reacts to environmental change. I also showed that the contributions of the different fitness components are tied to both selected and non-selected traits via the lengths of seasons. Thus, during population dynamics arising across successive batches, these components change, modifying the selection pressure on each trait. One therefore has a feedback loop, revealing why fitness is frequency-dependent in this system.Next, using the fitness decomposition, I studied adaptive dynamics in successive batch cultures. In such a framework where genotypic changes were allowed, and assuming that there was a trade-off between two traits, I showed that adaptive evolutionary dynamics could lead to the emergence of new relations between selected and non-selected traits (Chapter 3).Furthermore, in order to compare my theoretical predictions to experimental results, I used mathematical and statistical models to analyze two datasets (Chapter 4). The first dataset provides trait measurements in “evolved” strains, i.e., strains obtained after evolution across successive batches, as well as of those same traits in the “ancestral” strains at the origin of the experimental evolution. Parameters inference for the different strains showed that selection had operated mainly on ethanol-related traits (production and consumption). A second dataset was obtained from batch experiments putting strains in competition with one another; the analysis showed that my theoretical modeling well predicted the roles of the different traits for determining the relative fitness of the strains.
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Submitted on : Friday, July 13, 2018 - 12:09:06 PM
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Dorian Collot. Modélisation des dynamiques adaptatives de la levure de boulanger S. cerevisae dans un environnement saisonnier. Evolution [q-bio.PE]. Université Paris-Saclay, 2018. Français. ⟨NNT : 2018SACLS179⟩. ⟨tel-01838370⟩



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