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Applied and Environmental Microbiology, October 2004, p. 6157-6165, Vol. 70, No. 10
0099-2240/04/$08.00+0 DOI: 10.1128/AEM.70.10.6157-6165.2004
Copyright © 2004, American Society for Microbiology. All Rights Reserved.
Institute of Biological Sciences,1 Department of Computer Science, University of Wales, Aberystwyth,2 School of Biological Sciences, University of Manchester, Manchester, United Kingdom3
Received 29 February 2004/ Accepted 22 June 2004
Diploid cells of Saccharomyces cerevisiae were grown under controlled conditions with a Bioscreen instrument, which permitted the essentially continuous registration of their growth via optical density measurements. Some cultures were exposed to concentrations of a number of antifungal substances with different targets or modes of action (sterol biosynthesis, respiratory chain, amino acid synthesis, and the uncoupler). Culture supernatants were taken and analyzed for their "metabolic footprints" by using direct-injection mass spectrometry. Discriminant function analysis and hierarchical cluster analysis allowed these antifungal compounds to be distinguished and classified according to their modes of action. Genetic programming, a rule-evolving machine learning strategy, allowed respiratory inhibitors to be discriminated from others by using just two masses. Metabolic footprinting thus represents a rapid, convenient, and information-rich method for classifying the modes of action of antifungal substances.
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