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 di®erent 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, tting 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¯nd that the Baranyi model for microbial growth is useful for¯tting 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

Microbial high throughput phenomics: The potential of an irreplaceable omics

“… [56] mcmc-pma Implementation for GNU/Linux Systems Bayesian approach using adaptive Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior distributions of the parameters from fitted data using Baranyi and custom Diauxic model. No normalization [21] Biolog Decomposition R programming Novel algorithm to identify different metabolic cycles based on statistical decomposition of the time-series measurements into a set of growth models. – [46] Micro4Food PM R programming Coupling of grouping and normalization/stabilization methods proposed by Vehkala et al [56] and grofit free splines parameter estimation.…”

“…Nevertheless, this threshold is set arbitrarily and based on single summary statistics, misleading to a biased categorization. Others [21] define a profile to be metabolically active if the maximum absorbance does not fall in the 95% quantile of the same parameter for the control well. A novel procedure groups active and non-active metabolic profiles [56] .…”

Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments

“…Some methods split signals into growth or no-growth curves by using an arbitrary cut-off or comparison to a reference signal [ 5 , 6 ]. However, describing a time-series with only a single summary statistics leads to a loss of information, and may introduce bias in the results [ 7 ]. If more than two samples are provided, the differences in summary statistics can be tested, e.g.…”

“…In addition to simple summary statistics, model-based methods are widely applied to Biolog data [ 7 – 10 ]. They are able to utilize more information by fitting growth models such as logistic, Gompertz and Richard, to the metabolic profiles.…”

“…It fits Gompertz’s and Richard’s models by using the package, and enables the comparison of the curves based on the 95% confidence intervals of the model parameter estimates. The most recent software by Gerstgrasser et al [ 7 ] fits several models at once, and chooses the most suitable one utilizing Bayesian inference in parameter estimation and model selection. The signals are then compared against each other by defining maximum colour change, steepest slope and length of lag phase based on the fitted models.…”