Biology Faculty Articles

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PLoS Computational Biology


Wavelet Transforms, Cell Signaling, Bacterial Growth, Bacterial Genetics, Gene Expression, Bacterial Physiology, Bacterial Pathogens, Perturbation (geology)







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Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains.


This work was partially supported by a National Science Foundation CAREER Award (CBET-0953202, LY), the National Institutes of Health (1RO1GM098642 (LY), 1R01AI076318 (RS), 1R01CA140214 (RS), the Office of Naval Research (N00014-12-1-0631), a DuPont Young Professorship (LY), a David and Lucile Packard Fellowship (LY), a Medtronic Fellowship (CT), a Lane Fellowship (CT), and a Branco-Weiss Fellowship (CT).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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