Ab initio prediction of metabolic networks using Fourier transform mass spectrometry data

Rainer Breitling, Shawn Ritchie, Dayan Goodenowe, Mhairi L. Stewart, Michael P. Barrett

    Research output: Contribution to journalArticlepeer-review

    Abstract

    Fourier transform mass spectrometry has recently been introduced into the field of metabolomics as a technique that enables the mass separation of complex mixtures at very high resolution and with ultra high mass accuracy. Here we show that this enhanced mass accuracy can be exploited to predict large metabolic networks ab initio, based only on the observed metabolites without recourse to predictions based on the literature. The resulting networks are highly information-rich and clearly non-random. They can be used to infer the chemical identity of metabolites and to obtain a global picture of the structure of cellular metabolic networks. This represents the first reconstruction of metabolic networks based on unbiased metabolomic data and offers a breakthrough in the systems-wide analysis of cellular metabolism. © Springer Science+Business Media, Inc. 2006.
    Original languageEnglish
    Pages (from-to)155-164
    Number of pages9
    JournalMetabolomics
    Volume2
    Issue number3
    DOIs
    Publication statusPublished - Sept 2006

    Keywords

    • Computational methods
    • Fourier transform mass spectrometry
    • Metabolic networks
    • Network reconstruction

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