HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: M400110-MCP200v1 4/5/683    most recent Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Glossary Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Scott, M. S. Articles by Hallett, M. Search for Related Content PubMed PubMed Citation Articles by Scott, M. S. Articles by Hallett, M. Social Bookmarking                      What's this?

Molecular & Cellular Proteomics 4:683-692, 2005.
© 2005 by The American Society for Biochemistry and Molecular Biology, Inc.

---

Research

Identifying Regulatory Subnetworks for a Set of Genes^*

Michelle S. Scott^,,¶, Theodore Perkins, Scott Bunnell^,,||, François Pepin^,,||, David Y. Thomas and Michael Hallett^,,||,**

From the McGill Centre for Bioinformatics, 3775 University Street, Montreal H3A 2B4 and the Department of Biochemistry, 3655 Promenade Sir William Osler, McGill University, Montreal H3G 1Y6, Canada

High throughput genomic/proteomic strategies, such as microarray studies, drug screens, and genetic screens, often produce a list of genes that are believed to be important for one or more reasons. Unfortunately it is often difficult to discern meaningful biological relationships from such lists. This study presents a new bioinformatic approach that can be used to identify regulatory subnetworks for lists of significant genes or proteins. We demonstrate the utility of this approach using an interaction network for yeast constructed from BIND, TRANSFAC, SCPD, and chromatin immunoprecipitation (ChIP)-Chip data bases and lists of genes from well known metabolic pathways or differential expression experiments. The approach accurately rediscovers known regulatory elements of the heat shock response as well as the gluconeogenesis, galactose, glycolysis, and glucose fermentation pathways in yeast. We also find evidence supporting a previous conjecture that approximately half of the enzymes in a metabolic pathway are transcriptionally co-regulated. Finally we demonstrate a previously unknown connection between GAL80 and the diauxic shift in yeast.

CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				M. Suderman and M. Hallett Tools for visually exploring biological networks Bioinformatics, October 15, 2007; 23(20): 2651 - 2659. [Abstract] [Full Text] [PDF]

				Z. Guo, Y. Li, X. Gong, C. Yao, W. Ma, D. Wang, Y. Li, J. Zhu, M. Zhang, D. Yang, et al. Edge-based scoring and searching method for identifying condition-responsive protein protein interaction sub-network Bioinformatics, August 15, 2007; 23(16): 2121 - 2128. [Abstract] [Full Text] [PDF]

				H. B. Pollard, O. Eidelman, C. Jozwik, W. Huang, M. Srivastava, X. D. Ji, B. McGowan, C. F. Norris, T. Todo, T. Darling, et al. De Novo Biosynthetic Profiling of High Abundance Proteins in Cystic Fibrosis Lung Epithelial Cells Mol. Cell. Proteomics, September 1, 2006; 5(9): 1628 - 1637. [Abstract] [Full Text] [PDF]