Pierre Barrat-Charlaix

I am a quantitative biologist / physicist working at the Politecnico di Torino, in the Statistical Physics and Interdisciplinary Applications team. I am broadly interested in finding models that give a quantitative description and understanding of biological phenomena, usually (but not only) using tools from statistical physics.

Interests

Complex sequence evolution models

Sequence evolution models reflect our ability to quantitatively model evolution at the molecular level. They are central to the field of phylogenetics, and also have applications in directed evolution experiments.
I am interested in extending modern generative protein models to study evolutionary dynamics. In particular:

• how do traditional phylogenetic reconstruction methods fare when presented with data generated by a process that has complex interactions / epistasis?
• can we derive phylogenetic reconstruction methods (ancestral states, or trees) for use with complex models?

Evolution of influenza

• How predictable is the evolution of seasonal influenza viruses? What determines the fate of variants?
• Gene reassortment in influenza. During co-infection of a cell by two distinct viruses, an exchange of genes is possible. This means that while the evolutionary history of a single influenza gene is described by a tree, the history of a full genome has to be described by a graph (i.e. a genome can potentially have two parents), called the Ancestral Recombination Graph (ARG). Can we infer this ARG? How important is gene reassortment to the evolution of influenza?

Protein sequence models

During my PhD in the team of Martin Weigt, I worked on statistical models of homologous protein sequences(the so-called DCA method).