A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Markus J. Herrgård, Neil Swainston, Paul Dobson, Warwick B. Dunn, K. Yalçin Arga, Mikko Arvas, Nils Büthgen, Simon Borger, Roeland Costenoble, Matthias Heinemann, Michael Hucka, Nicolas Le Novère, Peter Li, Wolfram Liebermeister, Monica L. Mo, Ana Paula Oliveira, Dina Petranovic, Stephen Pettifer, Evangelos Simeonidis, Kieran SmallboneIrena Spasić, Dieter Weichart, Roger Brent, David S. Broomhead, Hans V. Westerhoff, Betül Kirdar, Merja Penttilä, Edda Klipp, Bernhard Palsson, Uwe Sauer, Stephen G. Oliver, Pedro Mendes, Jens Nielsen, Douglas B. Kell

    Research output: Contribution to journalArticleScientificpeer-review

    427 Citations (Scopus)

    Abstract

    Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

    Original languageEnglish
    Pages (from-to)1155-1160
    JournalNature Biotechnology
    Volume26
    Issue number10
    DOIs
    Publication statusPublished - 1 Oct 2008
    MoE publication typeA1 Journal article-refereed

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