Research interests

    With the rise of antibiotic resistance and the emergence of new forms of virulence, it has become clear that microbial evolution is at the heart of infectious diseases. The aim of our team is to study the microbial adaptation with a quantitative methodology.  For that purpose, we combine three approaches: experimental evolution, comparative genomics and population genetics.

    Experimental evolution combined with whole genome sequencing allow us to uncover the molecular bases of adaptation in test tubes as well as in mice colonization models. Comparative genomics allow us to study rates and tempo of adaptation in the wild at different time scale: species wise, for a sequence type or within a host as commensal or pathogen. Finally, integrated models of adaptation, such as Fisher Geometric Model of Adaptation, are used to capture the experimental and epidemiological observations.

    All these approaches take full benefit of the sequencing revolution, the computing progresses, as well as some high throughput phenotyping. Thanks to these technical advancements, we can increase both the level of replication and the description level of the clones and switch from a descriptive science to a quantitative one.

    One of our objectives is to define the benefits and limits of Fisher Geometric Model of Adaptation and to uncover how this model can be connected to the molecular determinants of adaptation. In Fisher’s geometric model, an organism is characterized by a set of independent phenotypic traits, each corresponding to an axis in an n-dimensional Euclidian space. The axes correspond to idealized traits; they are a combination of traits that produce the orthogonal bases of a space. A genotype is assumed to generate a single phenotype and is therefore characterized by a point in this space. The dimensionality of this space is referred to as phenotypic complexity, defined as the number of independent and evolvable traits an organism exposes to the action of natural selection in a given environment. Our goal is to test how this abstract vision of the adaptive landscape is compatible with the protein, network or genome evolution that we explore experimentally.