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Predicting microbial growth in a mixed culture from growth curve data
Contributed by Marcus W. Feldman, May 3, 2019 (sent for review February 6, 2019; reviewed by Benjamin Kerr, Paul Rainey, and Michael Travisano)
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We present a model-based approach for prediction of microbial growth in a mixed culture and relative fitness using data solely from growth curve experiments, which are easier to perform than competition experiments. Our approach combines growth and competition models and utilizes the total densities of mixed cultures. We implemented our approach in an open-source software package, validated it using experiments with bacteria, and demonstrated its application for estimation of relative fitness. Our approach establishes that growth in a mixed culture can be predicted using growth and competition models. It provides a way to infer relative strain or species frequencies even when competition experiments are not feasible, and to determine how differences in growth affect differences in fitness.
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.
Footnotes
- ↵1To whom correspondence may be addressed. Email: yoav{at}yoavram.com or mfeldman{at}stanford.edu.
Author contributions: Y.R., E.D.-G., M.B., K.K., U.O., M.W.F., T.F.C., J.B., and L.H. designed research; Y.R., E.D.-G., M.B., and K.K. performed research; Y.R., E.D.-G., M.B., K.K., U.O., M.W.F., T.F.C., J.B., and L.H. analyzed data; and Y.R., E.D.-G., M.B., K.K., U.O., M.W.F., T.F.C., J.B., and L.H. wrote the paper.
Reviewers: B.K., University of Washington; P.R., Max Planck Institute for Evolutionary Bio; and M.T., University of Minnesota.
The authors declare no conflict of interest.
Data deposition: The data reported in this paper have been deposited on Figshare (DOI: 10.6084/m9.figshare.3485984.v1). Source code is available at https://github.com/yoavram/curveball; an installation guide, tutorial, and documentation are available at http://curveball.yoavram.com.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1902217116/-/DCSupplemental.
- Copyright © 2019 the Author(s). Published by PNAS.
This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
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