CHAPTER 30

COMPUTATIONAL MODELS FOR CONDITION-SPECIFIC GENE AND PATHWAY INFERENCE

Yu-Qing Qiu, Shihua Zhang, Xiang-Sun Zhang, and Luonan Chen

30.1 INTRODUCTION

High-throughput experimental data such as protein–protein interaction [49, 57], gene expression [11], and ChIP-chip data [48], are now explored widely to study the complicated behaviors of living organisms from various aspects at a molecular level. For example, DNA microarrays provide us with a key step toward the goal of uncovering gene function on a global scale and biomolecular networks pave the way for understanding the whole biological systems on a systematic level [28, 69]. These studies are very important because proteins do not function in isolation but rather interact with one another and with various molecules (e.g., DNA, RNA, and small molecules) to form molecular machines (pathways, complexes, or functional modules). These machines shape the modular organization of biological systems, represent static and dynamic biological processes, and transmit/respond to intra- and extracellular signals.

In other words, modules are basic functional building block of biological systems that have been observed in various types of network data including protein–protein interaction networks, metabolic networks, transcriptional regulation networks, and gene coexpression networks [63]. In contrast to individual components, it has been recognized that modules as well as biomolecular networks are ultimately responsible to the forms ...

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