| Project/Area Number |
15300099
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Bioinformatics/Life informatics
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| Research Institution | The University of Tokyo |
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Principal Investigator |
MIYANO Satoru The University of Tokyo, Institute of Medical Science, Professor, 医科学研究所, 教授 (50128104)
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| Co-Investigator(Kenkyū-buntansha) |
OHM Shinobu The University of Tokyo, Institute of Medical Science, Associate Professor, 医科学研究所, 助教授 (20160046)
AKUTSU Tatsuya Kyoto University, Institute for Chemical Research, Professor, 化学研究所, 教授 (90261859)
IMOTO Seiya The University of Tokyo, Institute of Medical Science, Assistant Professor, 医科学研究所, 助手 (10345027)
BANNAI Hideo Kyushu University, Department of Informatics, Lecturer, 大学院・システム情報学研究院, 講師 (20323644)
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| Project Period (FY) |
2003 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥16,500,000 (Direct Cost: ¥16,500,000)
Fiscal Year 2005: ¥4,100,000 (Direct Cost: ¥4,100,000)
Fiscal Year 2004: ¥4,800,000 (Direct Cost: ¥4,800,000)
Fiscal Year 2003: ¥7,600,000 (Direct Cost: ¥7,600,000)
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| Keywords | protein network / gene network / protein-protein interaction / protein-RNA interaction prediction / pathway modeling / simulation / タンパク質相互作用 / マイクロアレイ解析 / パスウェイシミュレーション |
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| Research Abstract |
This research developed information scientific foundations of computational knowledge discovery from proteome data which came out from the genome-wide analysis of proteins and their related information. Our contributions are classified in two subjects :
1.Inferrining protein networks
We developed a statistical and computational method for inferring and estimating gene networks by using microarray gene expression data and protein-protein interaction data. This method uses a Bayesian network method combined with nonparametric regression which employs protein-protein interaction information obtained by Y2H and MS as its prior. We also developed a computational method for estimating protein complexes by applying the principal component analysis to microarray gene expression data. Further, we constructed a model which combines Bayesian networks for gene regulatory networks and Markov networks for protein networks. With this model, more accurate protein networks can be estimated from protein-protein interaction information and microarray gene expression data.
2.Computational method for pathway modeling and simulation
We defined a new basic architecture called Hybrid Functional Petri Net with extension on which a new software tool for pathway modeling and simulation was developed. This architecture allows us to use protein subcellular localization information and protein modification information in pathway modeling. By using the literature and attached data, we constructed models of pathways including protein networks with the software tool. We interpreted and evaluated these models and demonstrated the effectiveness of this method through modeling of a apoptosis signaling pathway, a cell cycle pathway model of fission yeast, and a network around p53.
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