Models and simulation-based parameter inference for microbial evolution


This project aims at developing quantitative methods for the study of biological systems, with questions centred around the generation and maintenance of genetic diversity in bacterial populations. It will heavily rely on mathematical and computational tools and concepts coming from the artificial intelligence field.

We aim at tackling two main biological questions :

(1) Which ecological factors promote or hinder the generation of genetic diversity in bacterial populations ? In particular, what is the effect of ecological interactions

between individuals ?

(2) Which selection pressures result from biochemistral constraints related to metabolism (robustness and stability of expression despite stochastic fluctuations), what are their effect on the organization and diversification of genomes, and what are their effect on the evolution of interactions between individuals ?

We will combine three types of approaches :

  • stochastic simulations for the emergence of mutations within quasi-clonal bacterial populations (inspired by the fluctuation assay from S. Luria and M. Delbrück) with approximate bayesian computing methods for parameters estimation
  • artificial-life type individual-based simulations of genome evolution, with a between-individuals metabolic interaction layer and within-individual biochemistral constraints
  • analysis of bacterial genomes and metagenomes, with (a) flux balance analysis to understand metabolic constraints in full genomes (or reconstructed genomes from shotgun sequencing) and (b) analysis of co-occurence patterns in microbiomes, heavily relying on machine learning methods for dimensionality reduction


A PhD student is currently beeing hired thanks to this chair. Beyond funding, we hope that the MIAI will provide a fruitful ground for interdisciplinary discussions around the applications of artificial intelligence to the study of biological systems.

Published on  January 23, 2024
Updated on January 23, 2024