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

Autor: Tonner PD; Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.; Biology Department, Duke University, Durham, NC, USA., Darnell CL; Biology Department, Duke University, Durham, NC, USA., Bushell FML; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, United Kingdom., Lund PA; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Birmingham, United Kingdom., Schmid AK; Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.; Biology Department, Duke University, Durham, NC, USA.; Center for Computational Biology and Bioinformatics, Duke University, Durham, NC, USA., Schmidler SC; Program in Computational Biology and Bioinformatics, Duke University, Durham, NC, USA.; Department of Statistical Science, Duke University, Durham, USA.; Department of Computer Science, Duke University, Durham, USA.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2020 Oct 26; Vol. 16 (10), pp. e1008366. Date of Electronic Publication: 2020 Oct 26 (Print Publication: 2020).
DOI: 10.1371/journal.pcbi.1008366
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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