Johannes Cairns

This study uses machine learning to predict multi-strain microbial community growth from monoculture data, finding that simpler models often suffice.

This study tracks four-year microbial community evolution in two environments, showing how adaptive mutations in one species can cause abrupt community state shifts.

This study examines genome-wide evolution in bacterial prey under ciliate predation, finding that clade dynamics remain similar to those in non-predated populations despite phenotypic and mutational changes.

This study shows that total carbon availability, not resource complexity, drives tristability in a synthetic microbial community, with lag phase duration predicting competitive success.

The paper shows antibiotic history shapes microbial response through eco-evolutionary and environmental feedbacks, creating a trade-off between resistance and recovery.

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.