J. Warringer

Quantifying massively parallel microbial growth with spatially mediated interactions

The paper quantifies microbial growth variability across spatial locations using machine learning and develops a spatial consumer-resource model to explain growth patterns and infer intrinsic growth parameters.

Adaptation of the yeast gene knockout collection is near-perfectly predicted by fitness and diminishing return epistasis

This study shows that fitness predicts adaptation dynamics in yeast gene knockouts under arsenic exposure, with diminishing return epistasis explaining the predictability.

Machine-learning prediction of resistance to sub-inhibitory antimicrobial concentrations from Escherichia coli genomes

This paper uses machine learning to predict E. coli growth under sub-inhibitory antimicrobial concentrations from genomic data, finding moderate to strong correlations but highlighting remaining missing heritability.

Genomic epidemiology and evolution of Escherichia coli in wild animals

This study analyzes whole genome sequencing data of commensal E. coli from wild animals to explore population structure, host associations, and the presence of virulence and antibiotic resistance genes.

Genetically controlled mtDNA editing prevents ROS damage by arresting oxidative phosphorylation

The paper shows yeast cells use genetically controlled mtDNA editing to prevent ROS damage by shutting down oxidative phosphorylation.

Domestication reprogrammed the budding yeast life cycle

This study shows that domestication of budding yeast reprogrammed its life cycle, favoring survival as quiescent cells over sexual reproduction and enhancing fermentative traits.