Virgile Andreani

The paper uses Bayesian inference to predict antibiotic survival in E. coli by integrating gene expression and growth rate data from single cells.

The paper introduces a method to optimally pack DNA-protein binding sites into dense nucleotide sequences, solving an NP-hard problem using integer linear programming.

The paper develops a pooled library screening method with a Bayesian approach to improve prediction of split protein behavior using light-inducible Cre recombinase.

This study uses single-cell time-lapse microscopy to show that gene expression and growth dynamics in Escherichia coli influence survival during antibiotic stress.