High-throughput classification of yeast mutants for functional genomics using metabolic footprinting

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Jess Allen and Hazel M. Davey and David Broadhurst and 
                 Jim K. Heald and Jem J. Rowland and 
                 Stephen G. Oliver and Douglas B. Kell",
  title =        "High-throughput classification of yeast mutants for
                 functional genomics using metabolic footprinting",
  journal =      "Nature Biotechnology",
  year =         "2003",
  volume =       "21",
  number =       "6",
  pages =        "692--696",
  month =        jun,
  email =        "dbk@umist.ac.uk",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dbkgroup.org/Papers/NatureBiotechnology21(692-696).pdf",
  DOI =          "doi:10.1038/nbt823",
  abstract =     "Many technologies have been developed to help explain
                 the function of genes discovered by systematic genome
                 sequencing. At present, transcriptome and proteome
                 studies dominate large-scale functional analysis
                 strategies. Yet the metabolome, because it is
                 'downstream', should show greater effects of genetic or
                 physiological changes and thus should be much closer to
                 the phenotype of the organism. We earlier presented a
                 functional analysis strategy that used metabolic
                 fingerprinting to reveal the phenotype of silent
                 mutations of yeast genes1. However, this is difficult
                 to scale up for high-throughput screening. Here we
                 present an alternative that has the required throughput
                 (2 min per sample). This 'metabolic footprinting'
                 approach recognizes the significance of 'overflow
                 metabolism' in appropriate media. Measuring
                 intracellular metabolites is time-consuming and subject
                 to technical difficulties caused by the rapid turnover
                 of intracellular metabolites and the need to quench
                 metabolism and separate metabolites from the
                 extracellular space. We therefore focused instead on
                 direct, noninvasive, mass spectrometric monitoring of
                 extracellular metabolites in spent culture medium.
                 Metabolic footprinting can distinguish between
                 different physiological states of wild-type yeast and
                 between yeast single-gene deletion mutants even from
                 related areas of metabolism. By using appropriate
                 clustering and machine learning techniques, the latter
                 based on genetic programming2-8, we show that metabolic
                 footprinting is an effective method to classify
                 'unknown' mutants by genetic defect.",

Genetic Programming entries for Jess Allen Hazel M Davey David I Broadhurst Jim K Heald Jem J Rowland Stephen G Oliver Douglas B Kell