ArticlePLoS computational biology2020
A Bayesian non-parametric mixed-effects model of microbial growth curves.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- The molecular basis of the synergistic toxicity of nickel and copper, common environmental co-contaminants.Applied and environmental microbiology · 2025Article
- Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves.Scientific reports · 2025Article
- Article
- gcplyr: an R package for microbial growth curve data analysis.BMC bioinformatics · 2024Article
- Measuring differential fitness costs and interactions between genetic cassettes using fluorescent spectrophotometry.Applied and environmental microbiology · 2024Article
- Why Do These Yeasts Smell So Good? Volatile Organic Compounds (VOCs) Produced byMolecules (Basel, Switzerland) · 2023Article
- Propionibacteria as promising tools for the production of pro-bioactive scotta: a proof-of-concept study.Frontiers in microbiology · 2023Article
- Competition-cooperation mechanism betweenFrontiers in microbiology · 2023Article
- Hierarchical Bayesian models of transcriptional and translational regulation processes with delays.Bioinformatics (Oxford, England) · 2021Article
- Article
- Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes.PLoS computational biology · 2021Article
- sparse-growth-curve: a Computational Pipeline for Parsing Cellular Growth Curves with Low Temporal Resolution.Microbiology resource announcements · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.