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Genome Informatics 14: 382–383 (2003)
KAREIDMAP: A System for Predicting and Mining Gene Regulatory Networks Hironori Mizuguchi1
Dai Kusui2
[email protected]
[email protected]
Taku Oshima2
Shigehiko Kanaya3
Hirotada Mori2,4
[email protected]
[email protected]
[email protected]
1 2
3 4
Internet Systems Research Laboratories, NEC Corporation, 8916-47 Takayama, Ikoma, Nara, Japan Research and Education Center for Genetic Information, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, Japan Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, Japan Institute for Advanced Biosciences, Keio University, 403-1 Nipponkoku, Daihouji, Tsuruoka, Yamagata, Japan
Keywords: gene network, gene regulation, pathway, DNA microarray, modeling
1
Introduction
One of the most important objectives of genome-wide analysis is the elucidation of a gene regulatory network. Genome-wide analysis of gene expression profiles has been performed using a DNA microarray, and several methods for predicting the gene regulatory networks from the microarray data have been proposed. However, almost all of them have difficulty in distinguishing direct and indirect regulation of gene expression. To solve this difficulty, we have developed a prediction and mining system for gene regulatory networks, called KAREIDMAP [1]. This system is implemented with a method to distinguish direct regulations from indirect ones. By applying the rules to the networks, it is possible to easily identify the indirect regulation and inconsistency of the regulation of the target genes and propose regulatory relations of interest. We evaluated this system using the gene expression profiles of E.coli.
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Approach
KAREIDMAP shows gene regulatory networks whose nodes represent genes and arcs directed from one node to another represent regulatory relations such as activation or repression. Gene regulatory networks are constructed based on the analysis of expression profiles from DNA microarray experiments and past knowledge from published papers. These regulatory relations contain both direct and indirect regulatory relations. We assume that there are many arcs deleted from and few arcs added from the gene regulatory networks based on the analysis of expression profiles, while the influence of the gene expression data is still maintained. Deleted arcs denote the indirect regulations and added arcs denote the expression data abandoned because of only a small quantity change. It is very difficult, however, to predict deleted and added arcs across the whole regulatory network. In response to this, we target pair nodes of the networks and locally predict arcs around these pair nodes to avoid the difficulties. We examined all link patterns around pair nodes, and extracted the algorithm of transformation to predict deleted and added arcs. Figure 1 shows an example of a pattern of pair nodes (A, B). On applying the algorithm to this pattern, the three deleted arcs and one added arc are predicted. Then, by applying these patterns to all pair nodes, this system can predict indirect
KAREIDMAP: A System for Predicting and Mining Gene Regulatory Networks
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regulations of an entire network and check the consistency of regulatory relations such as activation or repression of those expressions. KAREIDMAP implements this method and creates navigating information that includes the confidence of prediction and inconsistency of regulatory relations, enabling users to locate regulatory relations of interest.
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Development and Evaluation
We evaluated this system with the gene expression profiles, using deletion mutants of two-componentsystems of E.coli [2, 3]. KAREIDMAP can create navigating information with the following steps: 1) Collecting about 10,000 regulatory relations from 37 gene expression profiles. These regulatory relations contain direct and indirect regulatory relations. 2) Displaying the gene regulatory networks from these regulatory relations. 3) Predicting the indirect regulatory relations. 4) Creating navigating information from the prediction. To effectively mine the networks, KAREIDMAP not only features a function to predict the regulatory relations but also includes functions to import and display clusters, and explore the network paths. As a result, we could easily highlight the possibilities of new regulatory relations using predicting and mining functions. D
D A C
A E
B F
C
E
Added arc Deleted arc
B F
Figure 1: An example of the link patterns and the algorithm to predict the deleted and added arcs.
Figure 2: A screen shot of the gene regulatory networks and the navigation window.
References [1] Kusui, D., Mizuguchi, H., Oshima, T., Kanaya, S., and Mori, H., KAREIDMAP: gene regulatory network analysis support system, Proc. of First IECA Conf. on Systems Biology of E.coli, p-123, 2003. [2] Oshima, T., Aiba, H., Masuda, Y., Kanaya, S., Sugiura, M., Wanner, B.L., Mori, H., and Mizuno, T., Transcriptome analysis of all two-component regulatory system mutants of Escherichia coli K-12, Journal of Molecular Microbiology, 46(1):281–91, 2002. [3] GenoBase, http://ecoli.aist-nara.ac.jp/
