Predicting microbial growth in a mixed culture from growth curve data

Yoav Ram*, Eynat Dellus-Gur, Maayan Bibi, Kedar Karkare, Uri Obolski, Marcus W. Feldman, Tim F. Cooper, Judith Berman, Lilach Hadany

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 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.

Original languageEnglish
Pages (from-to)14698-14707
Number of pages10
JournalProceedings of the National Academy of Sciences of the United States of America
Volume116
Issue number29
DOIs
StatePublished - 2019

Keywords

  • Competition model
  • Experimental evolution
  • Microbial growth
  • Population dynamics

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