Abstract
Determining the fitness of specific microbial genotypes has extensive application in microbial genetics, evolution, and biotechnology. While estimates from growth curves are simple and allow high throughput, they are inaccurate and do not account for interactions between costs and benefits accruing over different parts of a growth cycle. For this reason, pairwise competition experiments are the current “gold standard” for accurate estimation of fitness. However, competition experiments require distinct markers, making them difficult to perform between isolates derived from a common ancestor or between isolates of nonmodel organisms. In addition, competition experiments require that competing strains be grown in the same environment, so they cannot be used to infer the fitness consequence of different environmental perturbations on the same genotype. Finally, competition experiments typically consider only the end-points of a period of competition so that they do not readily provide information on the growth differences that underlie competitive ability. Here, we describe a computational approach for predicting density-dependent microbial growth in a mixed culture utilizing data from monoculture and mixed-culture growth curves. We validate this approach using 2 different experiments withEscherichia coliand demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.
Funder
Israel Science Foundation
Minerva Center for Lab Evolution
Manna Center Program for Food Safety & Security
Israeli Ministry of Science & Technology
Stanford Center for Computational, Evolutionary and Human Genomics
Tel Aviv University Global Research and Training Fellowship in Medical and Life Science
Naomi Foundation
EC | FP7 | FP7 Ideas: European Research Council
National Science Foundation
Publisher
Proceedings of the National Academy of Sciences
Cited by
100 articles.
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