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

Gerstgrasser, Matthias and Nicholls, Sarah and Stout, Michael and Smart, Katherine and Powell, Chris and Kypraios, Theodore and Stekel, Dov J. (2016) A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. Journal of Bioinformatics and Computational Biology . ISSN 1757-6334

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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.

Item Type: Article
Additional Information: Electronic version of an article published as Journal of Bioinformatics and Computational Biology, 2016 doi: 10.1142/S0219720016500074 © 2016 copyright World Scientific Publishing Company http://www.worldscientific.com/worldscinet/jbcb
Keywords: Biolog, Growth Model, Diauxic, Lag Phase, Bayesian Statistics, Phenotype Microarrays
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Biosciences > Division of Agricultural and Environmental Sciences
University of Nottingham UK Campus > Faculty of Science > School of Biosciences > Division of Food Sciences
University of Nottingham UK Campus > Faculty of Science > School of Mathematical Sciences
Identification Number: https://doi.org/10.1142/S0219720016500074
Depositing User: Stekel, Dov
Date Deposited: 27 Jan 2016 13:23
Last Modified: 19 Sep 2016 23:56
URI: http://eprints.nottingham.ac.uk/id/eprint/31379

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