2004 Fiscal Year Final Research Report Summary
Information Technoloy for Gene Network Analysis
Project/Area Number |
15014205
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Research Category |
Grant-in-Aid for Scientific Research on Priority Areas
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Allocation Type | Single-year Grants |
Review Section |
Biological Sciences
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Research Institution | The University of Tokyo |
Principal Investigator |
MIYANO Satoru The Univeristy of Tokyo, Institute of Medical Science, Professor, 医科学研究所, 教授 (50128104)
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Co-Investigator(Kenkyū-buntansha) |
IMOTO Seiya Institute of Medical Science, Assistant Professor, 医科学研究所, 助手 (10345027)
DE Hoon Michiel J.L. 東京大学, 医科学研究所, 科学技術振興特任教員
HOON Michiel J.I. de Institute of Medical Science, Assistant Professor
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Project Period (FY) |
2003 – 2004
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Keywords | gene network / Bayesian network / nonparametric regression / simulation / microarray analysis / Petri net / systems biology / search algorithm |
Research Abstract |
We developed information technology for estimating gene networks accurately from multiple source of genetic information including microarray gene expression data. We also developed an XML format for describing dynamic models of gene networks. The following results are obtained: (1)We developed a computational method for estimating gene networks from microarray data obtained from various perturbations such as gene disruptions, gene overexpressions, drug responses, etc. The method combines the Bayesian network approach with nonparametric regression, where genes are regarded as random variables and the nonparametric regression enables us to capture from linear to nonlinear structures between genes. As a criterion for choosing good networks, we introduced the BNRC (Bayesian network and Nonparametric Regression Criterion) score. Naturally, the sole use of microarray data has limitations on gene network estimation. For improving the biological accuracy of estimated gene networks, we have made
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a general framework by extending this method so that it can employ genome-wide other biological information such as sequence information on promoter regions, protein-protein interactions. Computational experiments were conducted with yeast data and they show that cascades of gene regulations were effectively extracted. (2)The problem of finding an optimal Bayesian network is known computationally intractable for BNRC, BDe, MDL scores. For example, in order to find an optimal Bayesian network of 20 genes from 100 microarray data, the brute force algorithm employing all computing resources in the world even requires the time exceeding the life time of the solar system. Our recent computational challenge has made possible to search and enumerate optimal and suboptimal Bayesian networks in feasible time on supercomputers. Computational experiments with this search algorithm have provided evidences of the biological rationality of our computational strategy We obtained the following scientific knowledge: (i) Optimal Bayesian networks do not necessarily reflect the most accurate biological knowledge. (ii) Gene・gene relations which appear very frequently in optimal and suboptimal networks are biologically very likely. (iii) BNRC score is much better than BDe and MDL scores for gene network estimation. Furthermore, by putting constraints arising from biological knowledge, a faster algorithm is also developed. (3)We designed and developed an XML called CSML Version 1.0 (Cell System ML) for describing and simulating dynamic models of gene networks. Based on this, we developed programs which automatical convert pathway models in KEGG and BioCyc to CSML models so that dynamic models can be constructed. Cell Illustrator is used for modeling and simulation of the models which employs CSML for model description. This framework enables us to develope large-scale metabolic pathway models for simulation. Less
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Research Products
(38 results)
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[Journal Article] Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks2004
Author(s)
Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., Miyano, S.
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Journal Title
Journal of Bioinformatics and Computational Biology 2(1)
Pages: 77-98
Description
「研究成果報告書概要(和文)」より
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[Journal Article] Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data2004
Author(s)
De Hoon, M.J.L., Makita, Y., Imoto, S., Kobayashi, K., Ogasawara, N., Nakai, K., Miyano, S.
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Journal Title
Bioinformatics 20 (Suppl.1)
Pages: i101-i108
Description
「研究成果報告書概要(和文)」より
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[Journal Article] An 0(N^2) algorithm for discovering optimal Boolean pattern pairs2004
Author(s)
Bannai, H., Hyyro, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S.
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Journal Title
IEEE/ACM Transactions on Computational Biology and Bioinformatics 1
Pages: 159-170
Description
「研究成果報告書概要(和文)」より
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[Journal Article] Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information2004
Author(s)
De Hoon, M.J., Imoto, S., Kobayashi, K., Ogasawara, N., Miyano, S.
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Journal Title
Pacific Symposium on Biocomputing 9
Pages: 276-287
Description
「研究成果報告書概要(和文)」より
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[Journal Article] Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks2004
Author(s)
Imoto, S.., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., Miyano, S.
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Journal Title
Journal of Bioinformatics and Computational Biology. 2(1)
Pages: 77-98
Description
「研究成果報告書概要(欧文)」より
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[Journal Article] Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data2004
Author(s)
De Hoon, M.J.L., Makita, Y., Imoto, S., Kobayashi, K., Ogasawara, N., Nakai, K., Miyano, S.
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Journal Title
Bioinformatics. 20 (Supp1.1)
Pages: i101-i108
Description
「研究成果報告書概要(欧文)」より
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[Journal Article] An 0(N'2) algorithm for discovering optimal Boolean pattern pairs2004
Author(s)
Bannai, H., Hyyro, H., Shinohara, A., Takeda, M., Nakai, K., Miyano, S.
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Journal Title
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 1(4)
Pages: 159-170
Description
「研究成果報告書概要(欧文)」より
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[Journal Article] Predicting the operon structure of Bacillus subtilis using operon length, intergene distance, and gene expression information2004
Author(s)
De Hoon, M.J., Imoto, S., Kobayashi, K., Ogasawara, N., Miyano, S.
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Journal Title
Pacific Symposium on Biocomputing. 9
Pages: 276-287
Description
「研究成果報告書概要(欧文)」より
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[Journal Article] Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network2003
Author(s)
Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S.
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Journal Title
Journal of Bioinformatics and Computational Biology 1(2)
Pages: 231-252
Description
「研究成果報告書概要(和文)」より
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[Journal Article] Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection2003
Author(s)
Tamada, Y., Kim, S., Bannai, H., Imoto, S., Tashiro, K., Kuhara, S., Miyano, S.
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Journal Title
Bioinformatics 19 (Suppl. 2)
Pages: ii227-ii236
Description
「研究成果報告書概要(和文)」より
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[Journal Article] Bayesian network and nonparametric heteroscedastic regression for nonlinear modeling of genetic network2003
Author(s)
Imoto, S., Kim, S., Goto, T., Aburatani, S., Tashiro, K., Kuhara, S., Miyano, S.
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Journal Title
Journal of Bioinformatics and Computational Biology. 1(2)
Pages: 231-252
Description
「研究成果報告書概要(欧文)」より
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[Journal Article] Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection2003
Author(s)
Tamada, Y., Kim, S., Bannai, H., Imoto, S., Tashiro, K., Kuhara, S., Miyano, S.
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Journal Title
Bioinformatics. 19(Supp1.2)
Pages: ii227-ii236
Description
「研究成果報告書概要(欧文)」より
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