title: Identifying pathways of coordinated gene expression.
abstract: 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.
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Japan. timhancock@kuicr.kyoto-u.ac.jp
authors.forename: Timothy
authors.initials: T
authors.lastname: Hancock
authors.forename: Ichigaku
authors.initials: I
authors.lastname: Takigawa
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Mamitsuka
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D015870:Gene Expression; D053858:Metabolic Networks and Pathways; D046228:Microarray Analysis; D008954:Models, Biological; D015233:Models, Statistical; D015398:Signal Transduction