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Preprint article · Journal article

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

From

University of Manchester1

Department of Systems Biology, Technical University of Denmark2

The Molecular Sciences Institute3

Bogazici University4

VTT Technical Research Centre of Finland Ltd.5

Max Planck Institute6

Swiss Federal Institute of Technology Zurich7

California Institute of Technology8

Genome Research Limited9

University of California at San Diego10

Center for Microbial Biotechnology, Department of Systems Biology, Technical University of Denmark11

...and 1 more

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.

Language: English
Publisher: Nature Publishing Group US
Year: 2008
Pages: 1155-1160
Journal subtitle: Science and Business of Biotechnology
ISSN: 10870156 and 15461696
Types: Preprint article and Journal article
DOI: 10.1038/nbt1492
ORCIDs: Herrgard, Markus

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