Skip to main content

Research Repository

Advanced Search

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.

Authors

Matthias Gerstgrasser matthias@gerstgrasser.net

Sarah Nicholls sarah.nicholls@nottingham.ac.uk

Michael Stout michael.stout@nottingham.ac.uk

Katherine Smart katherine.smart@sabmiller.com

Dov J. Stekel



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.

Journal Article Type Article
Publication Date Jan 13, 2016
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
APA6 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
DOI https://doi.org/10.1142/S0219720016500074
Keywords Biolog, Growth Model, Diauxic, Lag Phase, Bayesian Statistics, Phenotype Microarrays
Publisher URL http://www.worldscientific.com/doi/abs/10.1142/S0219720016500074
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf

Files

A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters (1.8 Mb)
PDF





You might also like



Downloadable Citations

;