Abstract
Biolog Phenotype Microarray (PM) is a technology allowing simultaneous screening of the metabolic behaviour of bacteria under a large number of different conditions. Bacteria may often undergo several cycles of metabolic activity during a Biolog experiment. We introduce a novel algorithm to identify these metabolic cycles in PM experimental data, thus increasing the potential of PM technology in microbiology. Our method is based on a statistical decomposition of the time-series measurements into a set of growth models. We show that the method is robust to measurement noise and captures accurately the biologically relevant signals from the data. Our implementation is made freely available as a part of an R package for PM data analysis and can be found at www.helsinki.fi/bsg/software/Biolog_Decomposition.
Highlights
Biolog Phenotype Microarray (PM), not to be confused with RNA expression microarrays, is a commercially available 96-well format test system capable of multiple parallel testing of the bacterial growth responses to different nutrients and/or supplements [1]
Bacterial metabolism during growth leads to the irreversible reduction of the dye in the well with production of a purple colour which can be read as the change in absorbance over time [2]
We propose an algorithm for identifying multiple potential metabolic cycles of bacteria by decomposing the PM well signal into multiple growth models
Summary
Biolog Phenotype Microarray (PM), not to be confused with RNA expression microarrays, is a commercially available 96-well format test system capable of multiple parallel testing of the bacterial growth responses to different nutrients and/or supplements [1]. We propose an algorithm for identifying multiple potential metabolic cycles of bacteria by decomposing the PM well signal into multiple growth models. We propose a method for comparing signals with each other using summary statistics gained from the growth models.
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