Interdisciplinary Institute in Artificial Intelligence

DESCRIPTION

This project aims to develop quantitative methods for the study of biological systems, with a focus on the generation and maintenance of genetic diversity in bacterial populations. It will rely heavily on mathematical and computational toolsandconcepts from the field of artificial intelligence.

We aim to address 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 biochemical constraints related to metabolism (robustness and stability of expression despite stochastic fluctuations), what are their effects on the organization and diversification of genomes, and what are their effects on the evolution of interactions between individuals?
 

We will combine three types of approaches:

• Stochastic simulations of the emergence of mutations within quasi-clonal bacterial populations (inspired by the fluctuation assay developed by S. Luria and M. Delbrück) using approximate Bayesian computing methods for parameter estimation
• Artificial-life-type individual-based simulations of genome evolution, with a layer of metabolic interactions between individuals and biochemical constraints within individuals
• analysis of bacterial genomes and metagenomes, including (a) flux balance analysis to understand metabolic constraints in complete genomes (or reconstructed genomes from shotgun sequencing) and (b) analysis of co-occurrence patterns in microbiomes, relying heavily on machine learning methods for dimensionality reduction

CHAIR EVENTS

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