Affiliation:
1. Institute of Biological Sciences
2. Department of Computer Science, University of Wales, Aberystwyth
3. School of Biological Sciences, University of Manchester, Manchester, United Kingdom
Abstract
ABSTRACT
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.
Publisher
American Society for Microbiology
Subject
Ecology,Applied Microbiology and Biotechnology,Food Science,Biotechnology
Cited by
69 articles.
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