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

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  • Mikhail Shubin
  • Katharina Schaufler
  • Karsten Tedin
  • Minna Vehkala
  • Jukka Corander

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.

Suggested Citation

  • Mikhail Shubin & Katharina Schaufler & Karsten Tedin & Minna Vehkala & Jukka Corander, 2016. "Identifying Multiple Potential Metabolic Cycles in Time-Series from Biolog Experiments," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.
    • Handle: RePEc:plo:pone00:0162276
    DOI: 10.1371/journal.pone.0162276
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    References listed on IDEAS

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    1. Minna Vehkala & Mikhail Shubin & Thomas R Connor & Nicholas R Thomson & Jukka Corander, 2015. "Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
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