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

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http://hdl.handle.net/10138/168053

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Shubin , M , Schaufler , K , Tedin , K , Vehkala , M & Corander , J 2016 , ' Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments ' , PLoS One , vol. 11 , no. 9 , 0162276 . https://doi.org/10.1371/journal.pone.0162276

Title: Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments
Author: Shubin, Mikhail; Schaufler, Katharina; Tedin, Karsten; Vehkala, Minna; Corander, Jukka
Contributor: University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
University of Helsinki, Department of Mathematics and Statistics
Date: 2016-09-27
Language: eng
Number of pages: 14
Belongs to series: PLoS One
ISSN: 1932-6203
URI: http://hdl.handle.net/10138/168053
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.
Subject: PHENOTYPE MICROARRAY ANALYSIS
ESCHERICHIA-COLI
112 Statistics and probability
1183 Plant biology, microbiology, virology
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