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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.


Matthias Gerstgrasser

Sarah Nicholls

Michael Stout

Katherine Smart

Dov J. Stekel


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.


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.

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
Keywords Biolog, Growth Model, Diauxic, Lag Phase, Bayesian Statistics, Phenotype Microarrays
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