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

Delighted to say that our first paper of 2016 is published. Matthias’s Biolog paper is now online-ready with the Journal of Bioinformatics and Computational Biology. This is also the first output from our Biolog grant – with a second paper detailing our newer software and analysis being planned.

Gerstgrasser M, Nicholls S, Stout M, Smart K, Powell C, Kypraios T and Stekel DJ. 2016. A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters. J Bioinform Comput Biol. DOI: 10.1142/S0219720016500074


Biolog phenotype microarrays (PMs) 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 (MCMC) 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.


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