ArticleGenome research2017
Detecting differential growth of microbial populations with Gaussian process regression.
Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 77 citations in OpenAlex.
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
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- Stochasticity in mammalian cell growth rates drives cell-to-cell variability independently of cell size and divisions.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Accurate monitoring of substrate-dependent growth reveals ecotypic differentiation among marine yeasts.ISME communications · 2026Article
- Silver Nanoparticles Templated by the M13 Phage Exhibit High Antibacterial Activity against Gram-Negative Pathogens and a Reduced Rate of Bacterial Resistance In Vitro.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- Fitting and diversity of lactation curves in Saanen dairy goats.Journal of animal science · 2025Article
- gcplyr: an R package for microbial growth curve data analysis.BMC bioinformatics · 2024Article
- Reverse engineering morphogenesis through Bayesian optimization of physics-based models.NPJ systems biology and applications · 2024Article
- Inferring fungal growth rates from optical density data.PLoS computational biology · 2024Article
- Heterotrophic Selenium Incorporation intoFoods (Basel, Switzerland) · 2024Article
- Application of Machine Learning Algorithms to Describe the Characteristics of Dairy Sheep Lactation Curves.Animals : an open access journal from MDPI · 2023Article
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- Systems biology approach to functionally assess theProceedings of the National Academy of Sciences of the United States of America · 2022Article
- Formulation of Chemically Defined Media and Growth Evaluation ofFrontiers in microbiology · 2022Article
- ScanGrow: Deep Learning-Based Live Tracking of Bacterial Growth in Broth.Frontiers in microbiology · 2022Article
- Article
- sparse-growth-curve: a Computational Pipeline for Parsing Cellular Growth Curves with Low Temporal Resolution.Microbiology resource announcements · 2021Article
- Quantifying relative virulence: whenThe Journal of general virology · 2021Article
- Pleiotropy and epistasis within and between signaling pathways defines the genetic architecture of fungal virulence.PLoS genetics · 2021Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Microbial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions for which growth has a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit nonstandard growth curves. Rather than assume specific functional forms for experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high-resolution time-series growth data enables accurate quantification of population growth and allows explicit control of effects from other covariates such as genetic background. This framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model and develop statistical tests to quantify the differential effects of environmental perturbations on microbial growth across a large compendium of genotypes in archaea and yeast. This method accurately identifies known transcriptional regulators and implicates novel regulators of growth under standard and stress conditions in the model archaeal organism Halobacterium salinarum For yeast, our method correctly identifies known phenotypes for a diversity of genetic backgrounds under cyclohexamide stress and also detects previously unidentified oxidative stress sensitivity across a subset of strains. Together, these results demonstrate that the GP models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial 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.