Copyright © 2006 The Institute of Electronics, Information and Communication Engineers
Regular Section -- Papers -- Biological Engineering |
Graphical Gaussian Modeling for Gene Association Structures Based on Expression Deviation Patterns Induced by Various Chemical Stimuli
1 The author is with the Department of Electronics and Information Engineering, Ariake National College of Technology, Omuta-shi, 836–8585 Japan. E-mail: tetsuya{at}ariake-nct.ac.jp, 2 The author is with the Department of Chemical and Biological Engineering, Ariake National College of Technology, Omuta-shi, 836–8585 Japan., 3 The author is with the Division of Food and Health Environment, Faculty of Environmental and Symbiotic Sciences, Prefectural University of Kumamoto, Kumamoto-shi, 862–8502 Japan., 4 The author is with the Department of Bio-enviromental Research Center for Integrative Bioscience, National Institute for Basic Biology, Okazaki-shi, 444–8585 Japan., 5 The author is with the Structural Biology Center, National Institute of Genetics, Mishima-shi, 411–8540 Japan.
Activity patterns of metabolic subnetworks, each of which can be regarded as a biological function module, were focused on in order to clarify biological meanings of observed deviation patterns of gene expressions induced by various chemical stimuli. We tried to infer association structures of genes by applying the multivariate statistical method called graphical Gaussian modeling to the gene expression data in a subnetwork-wise manner. It can be expected that the obtained graphical models will provide reasonable relationships between gene expressions and macroscopic biological functions. In this study, the gene expression patterns in nematodes under various conditions (stresses by chemicals such as heavy metals and endocrine disrupters) were observed using DNA microarrays. The graphical models for metabolic subnetworks were obtained from these expression data. The obtained models (independence graph) represent gene association structures of cooperativities of genes. We compared each independence graph with a corresponding metabolic subnetwork. Then we obtained a pattern that is a set of characteristic values for these graphs, and found that the pattern of heavy metals differs considerably from that of endocrine disrupters. This implies that a set of characteristic values of the graphs can representative a macroscopic biological meaning.
Key Words: gene expression pattern, graphical Gaussian modeling, association structure, metabolic network
Manuscript received October 20, 2004. Manuscript revised July 6, 2005.