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A Bayesian non-parametric mixed-effects model of microbial growth curves

Publication ,  Journal Article
Tonner, PD; Darnell, CL; Bushell, FML; Lund, PA; Schmid, AK; Schmidler, SC
Published in: PLOS Computational Biology
October 26, 2020

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

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Published In

PLOS Computational Biology

DOI

EISSN

1553-7358

Publication Date

October 26, 2020

Volume

16

Issue

10

Start / End Page

e1008366 / e1008366

Publisher

Public Library of Science (PLoS)

Related Subject Headings

  • Systems Biology
  • Statistics, Nonparametric
  • Models, Biological
  • Bioinformatics
  • Bayes Theorem
  • Bacteria
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences
 

Citation

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Tonner, P. D., Darnell, C. L., Bushell, F. M. L., Lund, P. A., Schmid, A. K., & Schmidler, S. C. (2020). A Bayesian non-parametric mixed-effects model of microbial growth curves. PLOS Computational Biology, 16(10), e1008366–e1008366. https://doi.org/10.1371/journal.pcbi.1008366
Tonner, Peter D., Cynthia L. Darnell, Francesca M. L. Bushell, Peter A. Lund, Amy K. Schmid, and Scott C. Schmidler. “A Bayesian non-parametric mixed-effects model of microbial growth curves.” Edited by Jason A. Papin. PLOS Computational Biology 16, no. 10 (October 26, 2020): e1008366–e1008366. https://doi.org/10.1371/journal.pcbi.1008366.
Tonner PD, Darnell CL, Bushell FML, Lund PA, Schmid AK, Schmidler SC. A Bayesian non-parametric mixed-effects model of microbial growth curves. Papin JA, editor. PLOS Computational Biology. 2020 Oct 26;16(10):e1008366–e1008366.
Tonner, Peter D., et al. “A Bayesian non-parametric mixed-effects model of microbial growth curves.” PLOS Computational Biology, edited by Jason A. Papin, vol. 16, no. 10, Public Library of Science (PLoS), Oct. 2020, pp. e1008366–e1008366. Crossref, doi:10.1371/journal.pcbi.1008366.
Tonner PD, Darnell CL, Bushell FML, Lund PA, Schmid AK, Schmidler SC. A Bayesian non-parametric mixed-effects model of microbial growth curves. Papin JA, editor. PLOS Computational Biology. Public Library of Science (PLoS); 2020 Oct 26;16(10):e1008366–e1008366.

Published In

PLOS Computational Biology

DOI

EISSN

1553-7358

Publication Date

October 26, 2020

Volume

16

Issue

10

Start / End Page

e1008366 / e1008366

Publisher

Public Library of Science (PLoS)

Related Subject Headings

  • Systems Biology
  • Statistics, Nonparametric
  • Models, Biological
  • Bioinformatics
  • Bayes Theorem
  • Bacteria
  • 08 Information and Computing Sciences
  • 06 Biological Sciences
  • 01 Mathematical Sciences