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

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.

SUBMITTER: Shubin M

PROVIDER: S-EPMC5038949 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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

Shubin Mikhail M Schaufler Katharina K Tedin Karsten K Vehkala Minna M Corander Jukka J

PloS one 20160927 9

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 ...[more]

PMID: 27676629

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ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS

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Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.

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Identifying signatures of selection in genetic time series.

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TIme-series

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