A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters

Gerstgrasser, Matthias; Nicholls, Sarah; Stout, Michael; Smart, Katherine; Powell, Chris; Kypraios, Theodore; Stekel, Dov J.

Abstract

Biolog phenotype microarrays enable simultaneous, high throughput analysis of cell cultures in different environments. The output is high-density time-course data showing redox curves (approximating growth) for each experimental condition. The software provided with the Omnilog incubator/reader summarizes each time-course as a single datum, so most of the information is not used. However, the time courses can be extremely varied and often contain detailed qualitative (shape of curve) and quantitative (values of parameters) information. We present a novel, Bayesian approach to estimating parameters from Phenotype Microarray data, fitting growth models using Markov Chain Monte Carlo methods to enable high throughput estimation of important information, including length of lag phase, maximal ``growth'' rate and maximum output. We find that the Baranyi model for microbial growth is useful for fitting Biolog data. Moreover, we introduce a new growth model that allows for diauxic growth with a lag phase, which is particularly useful where Phenotype Microarrays have been applied to cells grown in complex mixtures of substrates, for example in industrial or biotechnological applications, such as worts in brewing. Our approach provides more useful information from Biolog data than existing, competing methods, and allows for valuable comparisons between data series and across different models.

Citation

Gerstgrasser, M., Nicholls, S., Stout, M., Smart, K., Powell, C., Kypraios, T., & Stekel, D. J. (2016). A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. Journal of Bioinformatics and Computational Biology, 14(03), 1-23. https://doi.org/10.1142/S0219720016500074

• Journal Article Type: Article
• Acceptance Date: Oct 18, 2015
• Online Publication Date: Jan 13, 2016
• Publication Date: Jan 13, 2016
• Deposit Date: Jan 27, 2016
• Publicly Available Date: Dec 11, 2018
• Journal: Journal of Bioinformatics and Computational Biology
• Print ISSN: 0219-7200
• Electronic ISSN: 1757-6334
• Publisher: World Scientific
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 03
• Article Number: 1650007
• Pages: 1-23
• DOI: https://doi.org/10.1142/S0219720016500074
• Keywords: Biolog, Growth Model, Diauxic, Lag Phase, Bayesian Statistics, Phenotype Microarrays
• Public URL: https://nottingham-repository.worktribe.com/output/772825
• Publisher URL: http://www.worldscientific.com/doi/abs/10.1142/S0219720016500074
• Contract Date: Dec 11, 2018