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    Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments (2016)

    Art
    Zeitschriftenartikel / wissenschaftlicher Beitrag
    Autoren
    Shubin, Mikhail
    Schaufler, Katharina (WE 7)
    Tedin, Karsten (WE 7)
    Vehkala, Minna
    Corander, Jukka
    Quelle
    PLoS one; 11(9) — S. 1–14
    ISSN: 1932-6203
    Sprache
    Englisch
    Verweise
    DOI: 10.1371/journal.pone.0162276
    Pubmed: 27676629
    Kontakt
    Institut für Mikrobiologie und Tierseuchen

    Robert-von-Ostertag-Str. 7-13
    Gebäude 35
    14163 Berlin
    Tel.+49 30 83 8-518 40/518 43 Fax.+49 30 838 45 18 51
    email:mikrobiologie@vetmed.fu-berlin.de

    Abstract / Zusammenfassung

    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.