Exploration of microbial ecosystems: from compositional patterns to metabolic models

Microbial communities can be characterized at the computational level through the lens of systems biology. The study of such microbial ecosystems brings additional challenges compared to individual populations, in terms of scale, data integration and interpretation. Yet, they are of outstanding interest in health, agroecology, or even food systems. I will present several questions we aimed to answer with methodological development ranging from statistical learning to knowledge representation and reasoning or numerical models. Starting from a large dataset depicting the composition of human gut microbiomes at all ages, we identified generic signatures whose assembly accurately depicts the dynamic equilibrium of the ecosystem. Going further than the description of a community’s members, we also build mechanistic models in order to decipher their metabolism. For small consortia, we predict the dynamics of the system using constraint-based models and try to improve scalability with statistical learning. For larger communities, we propose a Boolean approximation as a basis for modelling metabolic activity, complementarity and interactions among populations. Overall, our methods try to address several use-cases in the computational study of microbial ecosystems in order to connect the (meta)genome and other data to predictive models.

PhD in computer science obtained in 2018.

Postdoc in the Quadram Institute in Norwich UK

Recruited as a junior researcher at Inria Bordeaux in 2020.

More info: https://cfrioux.github.io/