Bayesian modeling of microbial physiology


Microbial population growth measurements are widespread in the study of microorganisms, providing insight into areas including genetics, physiology, and engineering. The most common models of microbial population growth data are parametric, and are derived from specific assumptions about the underlying growth process. While useful in cases where these assumptions are valid, these models are inadequate in many cases typically found in microbial growth studies, including presence of significant population death and the presence of multiple growth phases (e.g. diauxie). Here, we explore the use of the Bayesian non-parametric model Gaussian processes on microbial population growth. We first develop a general hypothesis-test using Gaussian process regression and false-discovery rate corrected Bayes factor scores. We then explore a fully Bayesian model with Gaussian process priors that can capture the latent growth processes of many population measurements under a single model. Finally, we develop hierarchical Bayesian model with GP priors in order to capture random effects in microbial population growth data.





Tonner, Peter (2017). Bayesian modeling of microbial physiology. Dissertation, Duke University. Retrieved from


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