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

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Tonner, PD; Darnell, CL; Bushell, FML; Lund, PA; Schmid, AK; Schmidler, SC

Published Date

  • October 26, 2020

Published In

Volume / Issue

  • 16 / 10

Start / End Page

  • e1008366 -

PubMed ID

  • 33104703

Pubmed Central ID

  • PMC7644099

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

International Standard Serial Number (ISSN)

  • 1553-734X

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1008366

Language

  • eng