Given at the 8th ILANIT/FISEB conference, Feb 22, 2017.
Fitness is not well estimated from growth curves of individual microbial isolates in monoculture. Rather, competition experiments must be performed to better infer relative fitness. However, competition experiments require unique genotypic or phenotypic markers, and thus are difficult to perform with isolates derived from a common ancestor or non-model organisms. I will present Curveball, a new computational approach for predicting competition dynamics and inferring relative fitness from growth curve data. We validated this approach using growth curve and competition experiments with bacteria. By integrating several growth phases into the fitness inference, Curveball offers a holistic approach to fitness inference from growth curve data.