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Poster communications

Testing evolution predictability using the aevol software

Guillaume Beslon 1, 2 Vincent Liard 1, 2 Santiago F Elena 3
2 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : Motivated by RNA virus’ genome biology, we used the aevol software to simulate the evolution of compacted genomes under high mutation rates. 30 independent digital wild-type (WT) genomes were generated after 200,000 generations of evolution under similar conditions. Then, each of these WTs was cloned 30 times and we let evolution to continue for 30,000 extra generations. By comparing these clones, we aimed to reveal the extent of evolutionary predictability for such compacted genomes. Results show that: (i) WTs are not equivalent in terms of evolutionary potential: some WTs are more prone than the others to increase their fitness during the last 30,000 generations. (ii) Evolution frequently occurs in bursts which implies that the probability to fix a mutation is increased after fixation of another mutation. Moreover these bursts are often initiated by chromosomal rearrangements (mainly duplications) because these rearrangements open new evolutionary pathways in the fitness landscape. Indeed, we quantified the "evolvability potential" of every clone after each mutation and found that the bursts are triggered by a strong increase of evolvability that quickly leads to point substitutions and indels fixation.
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Poster communications
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https://hal.archives-ouvertes.fr/hal-01577115
Contributor : Guillaume Beslon <>
Submitted on : Thursday, August 24, 2017 - 9:59:28 PM
Last modification on : Wednesday, July 8, 2020 - 12:43:08 PM

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Guillaume Beslon, Vincent Liard, Santiago F Elena. Testing evolution predictability using the aevol software. 16th international meeting of the European Society of Evolutionary Biology (ESEB 2017) , Aug 2017, Groningen, Netherlands. 2017. ⟨hal-01577115⟩

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