The TAO-Gen algorithm for identifying gene interaction networks with application to SOS repair in E. coli.
One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts.
Yamanaka
Takeharu
T
Laboratory of Computational Biology and Risk Analysis, National Institute of Environmental Health Sciences, National Institutes of Health/DHHS, 111 Alexander Drive, Research Triangle Park, NC 27709, USA.
Toyoshiba
Hiroyoshi
H
Sone
Hideko
H
Parham
Frederick M
FM
Portier
Christopher J
CJ
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics*; D029968:Escherichia coli Proteins / Q000235:genetics*; D020869:Gene Expression Profiling; D015964:Gene Expression Regulation, Bacterial; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D013014:SOS Response, Genetics / Q000235:genetics*
D029968:Escherichia coli Proteins