The dramatic success of infectious agents comes from their ability to adapt to both immune and pharmaceutical selective pressures. To uncover the dynamics of bacterial adaptation, experimental evolution has been widely used, focusing mostly on organismal fitness. Many of the observation derived from these experiments have been captured by Fisher’s Geometric model of Adaptation (FGMA). Despite its success, this top-down phenotypic model is relatively abstract. In fact, its most important parameter, the number of independent phenotypes an organism expose to the action of natural selection, or phenotypic complexity, remains completely disconnected from a genetic perspective. More recently, bottom-up genotype to phenotype maps from system biology have provided an alternative to unravel the constraints regulating bacterial evolution.

In the present project, we want to connect these different approaches. The interpretation of system biology models in terms of FGMA will (i) uncover the genetic determinants of phenotypic complexity, giving more genetic context to FGMA, and, (ii) transpose our understanding of evolution through FGMA to complex genotype to phenotype maps.

Four different levels of integration will be used: the gene, the metabolic network, the organism and the species. We will use:

– antibiotic resistance gene, TEM1, to connect thermodynamic models of protein evolution to FGMA, and characterize the phenotypic complexity of a single gene,

– computational models of metabolic network and experimental modification of a biochemical pathway regulation to assess the meaning of phenotypic complexity in networks,

in vitro and in vivo experimental evolution coupled with genome sequencing and mutant reconstruction to assess the molecular bases of changes in beneficial mutation rates during organismal adaptation,

– faeces of well characterised human twins to assess the factors of the human gut’s environment that shape the genetic diversity of the Escherichia coli species.