Hancock Timothy, Takigawa Ichigaku, Mamitsuka Hiroshi
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan.
Methods Mol Biol. 2013;939:69-85. doi: 10.1007/978-1-62703-107-3_7.
Methods capable of identifying genetic pathways with coordinated expression signatures are critical to advance our understanding of the functions of biological networks. Currently, the most comprehensive and validated biological networks are metabolic networks. Complete metabolic networks are easily sourced from multiple online databases. These databases reveal metabolic networks to be large, highly complex structures. This complexity is sufficient to hide the specific details on which pathways are interacting to produce an observed network response. In this chapter we will outline a complete framework for identifying the metabolic pathways that relate to an observed phenomenon. To illuminate the functional metabolic pathways, we overlay microarray experiments on top of a complete metabolic network. We then extract the functional components within a metabolic network through a combination of novel pathway ranking, clustering, and classification algorithms. This chapter is designed as a simple tutorial which enables this framework to be applied to any metabolic network and microarray data.
能够识别具有协同表达特征的遗传途径的方法对于推进我们对生物网络功能的理解至关重要。目前，最全面且经过验证的生物网络是代谢网络。完整的代谢网络很容易从多个在线数据库中获取。这些数据库显示代谢网络是大型、高度复杂的结构。这种复杂性足以隐藏关于哪些途径相互作用以产生观察到的网络反应的具体细节。在本章中，我们将概述一个完整的框架，用于识别与观察到的现象相关的代谢途径。为了阐明功能性代谢途径，我们将微阵列实验叠加在完整的代谢网络之上。然后，我们通过结合新颖的途径排名、聚类和分类算法，提取代谢网络中的功能成分。本章设计为一个简单的教程，使这个框架能够应用于任何代谢网络和微阵列数据。
