期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 29, 页码 14698-14707出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1902217116
关键词
population dynamics; microbial growth; competition model; experimental evolution
资金
- Israel Science Foundation [1568/13, 340/13]
- Minerva Center for Lab Evolution
- Manna Center Program for Food Safety and Security
- Israeli Ministry of Science and Technology
- Stanford Center for Computational, Evolutionary, and Human Genomics
- Tel Aviv University Global Research and Training Fellowship in Medical and Life Science
- Naomi Foundation
- European Research Council FP7/2007-2013/ERC Grant [340087]
- National Science Foundation [DEB-1253650]
- John Templeton Foundation/St. Andrews University Grant [13337]
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 with Escherichia coli and demonstrate its application for estimating relative fitness. Our approach provides an effective way to predict growth and infer relative fitness in mixed cultures.
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