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
Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA.
Nat Biotechnol. 2008 Oct;26(10):1155-60. doi: 10.1038/nbt1492.
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.
基因组数据允许对代谢网络重建进行大规模的人工或半自动组装，这些重建提供了高度精确的特定生物体知识库。尽管有几个基因组规模的网络重建描述了酿酒酵母的代谢，但它们在范围和内容上存在差异，并且使用不同的术语来描述相同的化学实体。这使得它们之间的比较变得困难，并突出了一个整合代谢网络的必要性，该网络收集并整理酵母代谢的“群体知识”。我们描述了我们如何为酿酒酵母构建一个共识代谢网络重建。在起草过程中，我们特别强调将分子引用到持久数据库或使用与数据库无关的形式，如SMILES或InChI字符串，因为这允许它们的化学结构以明确且能进行自动推理的方式表示。该重建可通过一个公开访问的数据库以及以系统生物学标记语言（http://www.comp-sys-bio.org/yeastnet）获取。它可以作为一种资源进行维护，作为研究酵母系统生物学的一个共同标准。类似的策略应该会使研究其他生物体基因组规模代谢网络的群体受益。
