title: Phenotypic signatures arising from unbalanced bacterial growth.
abstract: 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.
authors.affiliation: Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Department of Biomedical Engineering, University of California Davis, Davis, California, United States of America.
authors.forename: Cheemeng
authors.initials: C
authors.lastname: Tan
authors.affiliation: Division of Mathematics, Science and Technology, Nova Southeastern University, Fort Lauderdale, Florida, United States of America; Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America.
authors.forename: Robert Phillip
authors.initials: RP
authors.lastname: Smith
authors.affiliation: Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Ming-Chi
authors.initials: MC
authors.lastname: Tsai
authors.affiliation: Lane Center of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America; Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Russell
authors.initials: R
authors.lastname: Schwartz
authors.affiliation: Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America; Institute for Genome Sciences and Policy, Duke University, Durham, North Carolina, United States of America.
authors.forename: Lingchong
authors.initials: L
authors.lastname: You
mesh_terms: D000465:Algorithms; D001419:Bacteria / Q000254:growth & development*; D018407:Bacterial Physiological Phenomena; D057005:Bioengineering; D010641:Phenotype; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
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