This article appears in the following Briefings in Bioinformatics issue: Special Issue: Challenges in Bioinformatics and Computational Biology [View the issue table of contents]
Computational methods for discovering gene networks from expression data
Corresponding authors. Wei-Po Lee, Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan. E-mail: wplee{at}mail.nsysu.edu.tw
Wen-Shyong Tzou, Institute of Bioscience and Biotechnology, National Taiwan Ocean University, Keelung, Taiwan. E-mail: wstzou{at}ntou.edu.tw
Designing and conducting experiments are routine practices for modern biologists. The real challenge, especially in the post-genome era, usually comes not from acquiring data, but from subsequent activities such as data processing, analysis, knowledge generation and gaining insight into the research question of interest. The approach of inferring gene regulatory networks (GRNs) has been flourishing for many years, and new methods from mathematics, information science, engineering and social sciences have been applied. We review different kinds of computational methods biologists use to infer networks of varying levels of accuracy and complexity. The primary concern of biologists is how to translate the inferred network into hypotheses that can be tested with real-life experiments. Taking the biologists’ viewpoint, we scrutinized several methods for predicting GRNs in mammalian cells, and more importantly show how the power of different knowledge databases of different types can be used to identify modules and subnetworks, thereby reducing complexity and facilitating the generation of testable hypotheses.
Keywords: gene expression profiling, gene regulatory network, reverse engineering, transcription factor binding site, protein–protein interaction
Submitted: March 14, 2009. Received (in revised form): May 8, 2009.