Phylodynamics for Evolving Viruses (PhEV)
Internship title: Phylodynamics for Evolving Viruses (PhEV)
Name: Center for Interdisciplinary Research in Biology
Affiliation: Collège de France / CNRS / INSERM
Address: 11 Place Marcelin Berthelot, Paris 75005
Name: Amaury Lambert
Phone number: 0144271391
Name: Amaury Lambert and François Blanquart
Phone number: 0144271391
Subject Keywords: evolutionary biology; mathematical models; probabilities; adaptation; genomic
Tools and methodologies: probabilities; phylogenetic; statistics; mathematical modelling
Summary of lab’s interests: The team, “Stochastic models for the inference of life evolution”, is composed of biologists and mathematicians. We develop mathematical models and analyse genomic and ecological data to understand the evolution of life, with wide applications ranging from the evolution of mammalians to the diversification of tumours and the adaptation of pathogens.
Project summary: The identification of selective mutations and associated traits in organisms such as tumour cells, lymphocytes, viral or bacterial pathogens is of crucial importance to human health. Modern sequencing technologies offer partial or complete genomes of large samples of such cells. Recent phylodynamic methods allow us to infer the evolutionary dynamics of viral populations from such sequence samples, provided that neutral variants are sufficiently abundant to reconstruct the phylogenetic tree of these sequences beforehand. The purpose of this project is to develop new methods of inference in the absence of such neutral variants.
We will use mathematical (probabilities and statistics) and computational approaches to understand and infer the evolutionary dynamics of viruses. We will assume that the viral population evolved by mutation (absence of horizontal transfers, of recombination) in response to known selection pressures (e.g. proportion of hosts with defense against infection by the virus) and that a sample of these viruses is sequenced and aligned at one or more points in time. The principle of the project is to develop new statistical methods to exploit such multiple alignments of sequences to infer the evolutionary dynamics of asexual populations, including identifying selected mutations, and link these beneficial mutations beneficial to phenotypic traits.
Interdisciplinary aspect of the project: This project is at the interface between mathematical / computer science and biology. A very strong component of the project consists in developing mathematical models and simulations. These tools will be developed and then applied to genomic data to understand the selective pressures that shape the diversification of viral pathogens.