title: A Bayesian approach to analyzing phenotype microarray data enables estimation of microbial growth parameters.
abstract: 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.
authors.affiliation: * Department of Computer Science, University of Oxford, Parks Road, Oxford, OX1 3QD, UK.
authors.forename: Matthias
authors.initials: M
authors.lastname: Gerstgrasser
authors.affiliation: † School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
authors.forename: Sarah
authors.initials: S
authors.lastname: Nicholls
authors.affiliation: † School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Stout
authors.affiliation: ‡ SAB Miller plc, Church Street West, Woking, GU21 6HS, UK.
authors.forename: Katherine
authors.initials: K
authors.lastname: Smart
authors.affiliation: † School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
authors.forename: Chris
authors.initials: C
authors.lastname: Powell
authors.affiliation: § School of Mathematics, University of Nottingham, Nottingham, NG7 2RD, UK.
authors.forename: Theodore
authors.initials: T
authors.lastname: Kypraios
authors.affiliation: † School of Biosciences, University of Nottingham, Sutton Bonington, Leicestershire, LE12 5RD, UK.
authors.forename: Dov
authors.initials: D
authors.lastname: Stekel
mesh_terms: D019540:Area Under Curve; D001499:Bayes Theorem; D048708:Cell Growth Processes; D019295:Computational Biology / Q000379:methods*; D008390:Markov Chains; D046228:Microarray Analysis / Q000379:methods*; D009010:Monte Carlo Method; D010641:Phenotype
keywords: Bayesian statistics; Biolog; diauxic; growth model; lag phase; phenotype microarrays