Studying the evolution of bacterial micro-organisms by modeling and numerical simulation approaches

Charles Rocabert 1, 2
2 BEAGLE - Artificial Evolution and Computational Biology
LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558, Inria Grenoble - Rhône-Alpes, LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Variation and selection are the two core processes of Darwinian Evolution. Yet, both are directly regulated by many processes that are themselves products of evolution. Microorganisms efficiently exploit this ability to dynamically adapt to new conditions. Thus, evolution seems to have optimized its own ability to evolve, as a primary means to react to environmental changes. We call this process evolution of evolution (EvoEvo). EvoEvo covers several aspects of evolution, encompassing major concepts such variability, evolvability, robustness, and open-endedness. Those phenomena are known to affect all levels of organization in bacterial populations. Indeed, understanding EvoEvo requires to study organisms experiencing evolution, and to decipher the evolutive interactions between all the components of the biological system of interest (genomes, biochemical networks, populations, ...). The objective of this thesis was to develop and exploit mathematical and numerical models to tackle different aspects of EvoEvo, in order to produce new knowledge on this topic, in collaboration with partners from diverse fields, including experimental biology, bioinformatics, mathematics and also theoretical and applied informatics. To this aim, we followed two complementary approaches: (i) a population genetics approach to study the evolution of phenotypic noise in directional selection, by extending Fisher's geometric model of adaptation, and (ii) a digital genetics approach to study multi-level evolution. This work was funded by the EvoEvo project, under the European Commission (FP7-ICT-610427).
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Charles Rocabert. Studying the evolution of bacterial micro-organisms by modeling and numerical simulation approaches. Populations and Evolution [q-bio.PE]. Université de Lyon, 2017. English. ⟨NNT : 2017LYSEI106⟩. ⟨tel-01973793⟩

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