PREDICTING DIFFERENCES IN GENE REGULATORY SYSTEMS BY STATE SPACE MODELS
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- YAMAGUCHI RUI
- Human Genome Center, Institute of Medical Science, University of Tokyo
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- IMOTO SEIYA
- Human Genome Center, Institute of Medical Science, University of Tokyo
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- YAMAUCHI MAI
- Division of Systems Biomedical Technology, Institute of Medical Science, University of Tokyo
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- NAGASAKI MASAO
- Human Genome Center, Institute of Medical Science, University of Tokyo
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- YOSHIDA RYO
- Institute of Statistical Mathematics
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- SHIMAMURA TEPPEI
- Human Genome Center, Institute of Medical Science, University of Tokyo
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- HATANAKA YOSUKE
- Human Genome Center, Institute of Medical Science, University of Tokyo
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- UENO KAZUKO
- Human Genome Center, Institute of Medical Science, University of Tokyo
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- HIGUCHI TOMOYUKI
- Institute of Statistical Mathematics
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- GOTOH NORIKO
- Division of Systems Biomedical Technology, Institute of Medical Science, University of Tokyo
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- MIYANO SATORU
- Human Genome Center, Institute of Medical Science, University of Tokyo
Abstract
We propose a statistical strategy to predict differentially regulated genes of case and control samples from time-course gene expression data by leveraging unpredictability of the expression patterns from the underlying regulatory system inferred by a state space model. The proposed method can screen out genes that show different patterns but generated by the same regulations in both samples, since these patterns can be predicted by the same model. Our strategy consists of three steps. Firstly, a gene regulatory system is inferred from the control data by a state space model. Then the obtained model for the underlying regulatory system of the control sample is used to predict the case data. Finally, by assessing the significance of the difference between case and predicted-case time-course data of each gene, we are able to detect the unpredictable genes that are the candidate as the key differences between the regulatory systems of case and control cells. We illustrate the whole process of the strategy by an actual example, where human small airway epithelial cell gene regulatory systems were generated from novel time courses of gene expressions following treatment with (case)/without (control) the drug gefitinib, an inhibitor for the epidermal growth factor receptor tyrosine kinase. Finally, in gefitinib response data we succeeded in finding unpredictable genes that are candidates of the specific targets of gefitinib. We also discussed differences in regulatory systems for the unpredictable genes. The proposed method would be a promising tool for identifying biomarkers and drug target genes.
Journal
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- Genome Informatics
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Genome Informatics 21 101-113, 2008
Japanese Society for Bioinformatics