title: Detecting differential growth of microbial populations with Gaussian process regression.
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.
authors.affiliation: Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27708, USA.|Biology Department, Duke University, Durham, North Carolina 27708, USA.
authors.forename: Peter D
authors.initials: PD
authors.lastname: Tonner
authors.affiliation: Biology Department, Duke University, Durham, North Carolina 27708, USA.
authors.forename: Cynthia L
authors.initials: CL
authors.lastname: Darnell
authors.affiliation: Computer Science Department, Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey 08540, USA.
authors.forename: Barbara E
authors.initials: BE
authors.lastname: Engelhardt
authors.affiliation: Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27708, USA.|Biology Department, Duke University, Durham, North Carolina 27708, USA.
authors.forename: Amy K
authors.initials: AK
authors.lastname: Schmid
mesh_terms: D019617:Halobacterium salinarum / Q000235:genetics / Q000254:growth & development*; D008954:Models, Biological; D016011:Normal Distribution; D010641:Phenotype; D015003:Yeasts / Q000235:genetics / Q000254:growth & development*