The huge amount of molecular data available nowadays can help addressing new and essential questions in evolution. However, reconstructing evolution requires models, algorithms, and statistical and computational methods of ever increasing complexity. Developing methods that scale with the “deluge” of data is a real challenge, in terms of algorithmics but also modeling. Out unit aims at developing new methodologies and algorithms that are able to tackle efficiently these challenges, in the field of evolution and molecular phylogeny.
Our second aim is to apply these methods and tools to pathogens, mostly viruses, and especially HIV. The goals are multiple: understand their evolution (e.g. the emergence and transmission of drug resistance mutations), decipher their genome (e.g. to confirm the existence of the 10th gene of HIV), design surveillance tools (e.g. to control outbreaks). Most of the current methods to tackle these questions are based on Bayesian approaches, which are computationally heavy and not able to process the large data sets available nowadays. To overcome these limitations we are working on new maximum-likelihood and approximate bayesian methods (ABC) applicable to very large data sets comprising several dozens of thousands of sequences. Recently we started combining advanced research in machine learning with evolutionary approaches to analyse very large pathogen sequence data sets and complex virus transmission scenarios.
Every year we co-organize the Mathematical and Computational Evolutionary Biology conference in the South of France, and teach Phylogenetics courses in Paris and the International Network of Institut Pasteur (see below). We regularly host researchers from all around the world (New Zealand, Australia, Brazil, Japan, Corea, Cambodia, Cuba, Uk and Nederlands for the past few years).
We have several postdoc positions available; if you are highly motivated and interested in our research themes, with a PhD in computer science, statistics, mathematics, evolutionary biology or molecular epidemiology, please contact (olivier dot gascuel [at] pasteur dot fr).
