Inferring Gene Regulatory Networks from Gene Expression Data

Kaderali, Lars and Radde, Nicole (2008) Inferring Gene Regulatory Networks from Gene Expression Data.
Published in: Computational Intelligence in Bioinformatics., Studies in Computational Intelligence. 94 Springer 2008.

Abstract

Gene regulatory networks describe how cells control the expression of genes, which, together with some additional regulation further downstream, determines the production of proteins essential for cellular function. The level of expression of each gene in the genome is modified by controlling whether and how vigorously it is transcribed to RNA, and subsequently translated to protein. RNA and protein expression will influence expression rates of other genes, thus giving rise to a complicated network structure. An analysis of regulatory processes within the cell will significantly further our understanding of cellular dynamics. It will shed light on normal and abnormal, diseased cellular events, and may provide information on pathways in dire diseases such as cancer. These pathways can provide information on how the disease develops, and what processes are involved in progression. Ultimately, we can hope that this will provide us with new therapeutic approaches and targets for drug design. It is thus no surprise that many efforts have been undertaken to reconstruct gene regulatory networks from gene expression measurements. In this chapter, we will provide an introductory overview over the field. In particular, we will present several different approaches to gene regulatory network inference, discuss their strengths and weaknesses, and provide guidelines on which models are appropriate under what circumstances. In addition, we sketch future developments and open problems.


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Deposit Information:
ZAIK Number: zaik2007-549
Depositing User: Lars Kaderali
Date Deposited: 22 Jun 2007 00:00
Last Modified: 12 Jan 2012 09:40
URI: http://e-archive.informatik.uni-koeln.de/id/eprint/549