Mining metabolic pathways through gene expression
Mining metabolic pathways through gene expression
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DOI：
10.1093/bioinformatics/btq344
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发表时间：
2010-09-01
期刊：
影响因子：
5.8
通讯作者：
Mamitsuka, Hiroshi
中科院分区：
文献类型：
--
作者：
Hancock, Timothy;Takigawa, Ichigaku;Mamitsuka, Hiroshi
动机：观察到的代谢反应是多个遗传途径之间协调激活和相互作用的结果。然而，代谢的复杂结构意味着对产生所观察到的代谢反应所需的途径的完全理解尚未完全理解。在这篇文章中，我们提出了一种方法，可以确定的遗传途径，决定代谢网络的响应特定的实验conditions.Results：我们的方法是一个组合的概率模型的途径排名，聚类和分类。首先，我们使用非参数路径提取方法来识别通过代谢网络的最高度相关的路径。然后，我们提取这些排名靠前的路径使用马尔可夫聚类和分类算法的定义结构。此外，我们定义了详细的节点和边注释，这使我们能够跟踪每个途径，不仅是关于其遗传依赖性，而且还允许分析相互作用的反应，化合物和KEGG子网络。我们表明，我们的方法使用整个KEGG代谢网络在两个微阵列表达数据集内识别具有生物学意义的通路。
Motivation: An observed metabolic response is the result of the coordinated activation and interaction between multiple genetic pathways. However, the complex structure of metabolism has meant that a compete understanding of which pathways are required to produce an observed metabolic response is not fully understood. In this article, we propose an approach that can identify the genetic pathways which dictate the response of metabolic network to specific experimental conditions.Results: Our approach is a combination of probabilistic models for pathway ranking, clustering and classification. First, we use a nonparametric pathway extraction method to identify the most highly correlated paths through the metabolic network. We then extract the defining structure within these top-ranked pathways using both Markov clustering and classification algorithms. Furthermore, we define detailed node and edge annotations, which enable us to track each pathway, not only with respect to its genetic dependencies, but also allow for an analysis of the interacting reactions, compounds and KEGG sub-networks. We show that our approach identifies biologically meaningful pathways within two microarray expression datasets using entire KEGG metabolic networks.