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A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology
Authors:Herrgård Markus J  Swainston Neil  Dobson Paul  Dunn Warwick B  Arga K Yalçin  Arvas Mikko  Blüthgen Nils  Borger Simon  Costenoble Roeland  Heinemann Matthias  Hucka Michael  Le Novère Nicolas  Li Peter  Liebermeister Wolfram  Mo Monica L  Oliveira Ana Paula  Petranovic Dina  Pettifer Stephen  Simeonidis Evangelos  Smallbone Kieran  Spasić Irena  Weichart Dieter  Brent Roger  Broomhead David S  Westerhoff Hans V  Kirdar Betül  Penttilä Merja  Klipp Edda  Palsson Bernhard Ø  Sauer Uwe  Oliver Stephen G  Mendes Pedro  Nielsen Jens  Kell Douglas B
Affiliation:Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA.
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.
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