Toward the automated generation of genome-scale metabolic networks in the SEED.
Toward the automated generation of genome-scale metabolic networks in the SEED.
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DOI：
10.1186/1471-2105-8-139
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发表时间：
2007-04-26
影响因子：
3
通讯作者：
Best A
中科院分区：
文献类型：
--
作者：
DeJongh M;Formsma K;Boillot P;Gould J;Rycenga M;Best A
当前的基因组规模代谢网络自动生成的方法集中于基因组注释和初步的生化反应网络组装，但不能充分解决反应网络中识别和填补空白的过程，并验证该网络适用于系统水平是否适用于系统水平分析。因此，当前的方法仅足以生成草稿质量网络，而反应网络的完善基本上仍然是一个手动劳动力密集的过程。
我们已经开发了一种生成基因组规模的代谢网络的方法，该网络可产生基本完整的反应网络，适用于系统级分析。我们的方法将中央和中间代谢的反应空间分解为离散的，相互联系的组件，这些组件可以彼此隔离和隔离，然后在其互连级别进行集成和验证。我们已经开发了一个在生物体之间常见的组件数据库，并创建了基于生物体基因组中编码的代谢途径为特定生物体组装适当组件的工具。这将手动努力集中在数据库中尚未代表的生物体代谢的那部分。我们通过反向工程和自动重生反应网络从已发表的基因组级代谢模型中自动再生反应网络，证明了我们方法的功效。此外，我们已经证实，我们的方法通过使用这些组件来从其他三个已发表的代谢模型（Escherichia Coli，Helicobacter Pylori和Helicobacter Pylori和Pylori和Helicobacter Pylori，and Helicobacter pylori，and Helicobolic pylien）生成反应网络的基本完整重建，从而将我们的方法大写在公共反应网络组件的数据库上。乳酸菌）。我们已经在种子中实施了工具和数据库，这是一种开源软件环境，用于比较基因组注释和分析。
我们的方法为当前在种子中的400多个完整的基因组序列的自动生成的自动生成奠定了基础。使用我们的工具处理的每个基因组，共同组件的数据库会增长，以涵盖更多代谢途径的多样性。这增加了可以从数据库中检索到后来处理的基因组的反应网络的组成部分，而不是手动组装和验证。
Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the stage for the automated generation of substantially complete metabolic networks for over 400 complete genome sequences currently in the SEED. With each genome that is processed using our tools, the database of common components grows to cover more of the diversity of metabolic pathways. This increases the likelihood that components of reaction networks for subsequently processed genomes can be retrieved from the database, rather than assembled and verified manually.
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