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

Herrgard, Markus J., Swainston, Neil, Dobson, Paul, Dunn, Warwick B., Arga, K. Yalcin, Arvas, Mikko, Buthgen, Nils, Borger, Simon, Costenoble, Roeland, Heinemann, Matthias, Hucka, Michael, Le Novere, Nicolas, Li, Peter, Liebermeister, Wolfram, Mo, Monica L., Oliveira, Ana Paula, Petranovic, Dina, Pettifer, Stephen, Simeonidis, Evangelos, Smallbone, Kieran, Spasic, Irena, Weichart, Dieter, Brent, Roger, Broomhead, David S., Westerhoff, Hans V., Kurdar, Betul, Penttila, Merja, Klipp, Edda, Palsson, Bernhard O., Sauer, Uwe, Oliver, Stephen G., Mendes, Pedro, Nielsen, Jens and Kell, Douglas B. 2008. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nature Biotechnology 26 (10) , pp. 1155-1160. 10.1038/nbt1492

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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 ( 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.

Item Type: Article
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QH Natural history > QH301 Biology
Uncontrolled Keywords: Mcisb; Spasic
Publisher: Nature Publishing Group
ISSN: 1087-0156
Last Modified: 04 Jun 2017 01:56

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