AgreenSkills session, year: 2nd session, 2014
Receiving laboratory: Department of Zoology, University of Oxford, UK
Country of origin : France
Coalescent-based Methods for Selection in Viral Phylogenies
The increasing availability of large databases of pathogen genomes requires the development of mathematical frameworks integrating different features of pathogen evolution. In the case of rapidly-evolving viruses, many assumptions concerning the diversification process are no longer valid: the heterogeneous nature of viral transmission raises the question of whether neutral models based on binary trees are adequate to represent viral evolution.
In this context, I propose to use Λ-coalescents to model phylogenies of viral populations. I will use this model to quantify viral adaptive dynamics and to estimate the bias on ancestral population inference caused by current methods not including selection.
My first contact with biology-inspired mathematics came during my master’s thesis, when I was working on the relationship of exchangeable coalescents and measure-valued processes used in population genetics, such as the Fleming-Viot process. I then wrote my PhD thesis on random trees theory, focusing especially on the scaling limits of large branching trees (so-called Lévy trees). After graduating, my interest for mathematical structures in biology led me to an 8-month postdoc in the SMILE (Stochastic Models for the Inference of Life Evolution) team at Collège de France, in Paris. There, I learned about the recent advances in using phylogenetic data to infer characteristics of species diversification.
In 2013, I joined the MaIAGE research unit (UR 1404, Mathématiques et Informatique Appliquées du Génome à l’Environnement). My research interests lie mainly in epidemiology: epidemic processes on random graphs, spatial spread and, most importantly, phylogenetic methods for the inference of epidemiological parameters (phylodynamics).
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