Faculty Profiles - KURATA Hiroyuki

Papers - KURATA Hiroyuki

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CADLIVE Converter for constructing a biochemical network map Reviewed

Kentaro Inoue, Sayaka Tomeda, Shinpei Tonami, Yuki Shimokawa, Masayo Ono, Hiroyuki Kurata

Biochem Eng J 54 200 - 206 2011.08

An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks Reviewed

Kazuhiro Maeda, Yuya Fukano, Shunsuke Yamamichi, Daichi Nitta, Hiroyuki Kurata

Bioproc Biosys Eng 34 433 - 446 2011.08

Maximum entropy principle optimizes the large-scale elementary modes for a broad range of genetic mutants Reviewed

Hiroyuki Kurata, Quanyu Zhao

Proceedings of Asian Congress on Biotechnology B0768 2011.05

生体分子ネットワークレイアウトのための2次元近似照合によるハイブリッドレイアウトアルゴリズム

井上健太郎、下園真一、吉田英聡、倉田博之

情報処理学会研究報告 BIO-２４ ( 6 ) 1 - 6 2011.03

概日リズムのフィードバックシステムのロバストネス解析

前田和勲、倉田博之

情報処理学会研究報告 BIO-２４ ( 7 ) 1 - 6 2011.03

Diffusion model based spectral clustering for protein-protein interaction networks. Reviewed

Inoue K., Li W., Kurata H.

PloS one 5 ( 9 ) 2010.12

Authorship：Corresponding author Publishing type：Research paper (scientific journal)

A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple.

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952277084&origin=inward

バイオテクノロジー Reviewed

池田宰, 上田宏, 大河内美奈, 吉見靖男, 倉田博之, 木村幸敬

化学工学 = Chemical engineering 74 ( 10 ) 570 - 576 2010.10

Use of maximum entropy principle with Lagrange multipliers extends the feasibility of elementary mode analysis Reviewed

Zhao Quanyu, Kurata Hiroyuki

Journal of bioscience and bioengineering 110 ( 2 ) 254 - 261 2010.08

Use of maximum entropy principle with Lagrange multipliers extends the feasibility of elementary mode analysis Reviewed

J Biosci Bioeng 110 254 - 261 2010.08

JABEE認定校のカリキュラムと教育内容の特色 : (3) 九州工業大学生命情報工学科のプログラム Reviewed

倉田博之, 清水和幸

生物工学会誌 : seibutsu-kogaku kaishi 88 ( 5 ) 233 - 236 2010.05

Diffusion Model Based Spectral Clustering For Protein-Protein Interaction Networks Reviewed

Kentaro Inoue, Weijiang Li and Hiroyuki Kurata

PLOS ONE 5 e12623 2010.04

Two-phase search (TPS) method: Nonbiased and high-speed parameter search for dynamic models of biochemical networks Reviewed

Maeda K., Kurata H.

IPSJ Transactions on Bioinformatics 2 2 - 14 2009.12

Module-based analysis of the robustness generated by complex feedback regulations in biological networks Reviewed

Kurata H.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings 192 - 195 2009.12

Authorship：Corresponding author Publishing type：Research paper (international conference proceedings)

Bacterial cells evolve complex networks to survive nutrient starvation. The E. coli ammonia assimilation system consists of many positive and negative feedbacks for synthesizing glutamine and glutamate, the source of most nitrogen-containing compounds. Our objectives are to understand the molecular architecture of how those feedback loops act in concert for enhanced robustness with respect to ammonia depletion. The ammonia assimilation system is decomposed into the hierarchical modular structure in analogy to engineering control architecture. This modular architecture is elaborately designed to solve contradictory design issues. Comparison of biological modules with engineering systems identifies interesting biologically specific design features. © 2009 SICE.

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77951104212&origin=inward

バイオテクノロジー Reviewed

倉田博之, 中野秀雄, 大河内美奈, 吉見靖男, 池田宰, 吉井英文

化学工学 = Chemical engineering 73 ( 10 ) 506 - 512 2009.10

Module-based analysis of the robustness generated by complex feedback regulations in biological networks Reviewed

Hiroyuki Kurata

Proceedings of ICROS-SICE International Joint Conference 2009 1A11-101 2009.08

Elementary model-based prediction of the flux distribution for a variety of genetic mutants Reviewed

Quanyu Zhao, Hiroyuki Kurata

J Biosci Bioeng: Proceedings of APBioCHEC'2009 108 S165 2009.08

Genetic modification of flux for flux prediction of mutants Reviewed

Bioinformatics 25 1702 - 1708 2009.07

Genetic modification of flux for flux prediction of mutants Reviewed

Zhao Q., Kurata H.

Bioinformatics 25 ( 13 ) 1702 - 1708 2009.06

Authorship：Corresponding author Publishing type：Research paper (scientific journal)

Motivation: Gene deletion and overexpression are critical technologies for designing or improving the metabolic flux distribution of microbes. Some algorithms including flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) predict a flux distribution from a stoichiometric matrix in the mutants in which some metabolic genes are deleted or non-functional, but there are few algorithms that predict how a broad range of genetic modifications, such as over- and underexpression of metabolic genes, alters the phenotypes of the mutants at the metabolic flux level. Results: To overcome such existing limitations, we develop a novel algorithm that predicts the flux distribution of the mutants with a broad range of genetic modification, based on elementary mode analysis. It is denoted as genetic modification of flux (GMF), which couples two algorithms that we have developed: modified control effective flux (mCEF) and enzyme control flux (ECF). mCEF is proposed based on CEF to estimate the gene expression patterns in genetically modified mutants in terms of specific biological functions. GMF is demonstrated to predict the flux distribution of not only gene deletion mutants, but also the mutants with underexpressed and overexpressed genes in Escherichia coli and Corynebacterium glutamicum. This achieves breakthrough in the a priori flux prediction of a broad range of genetically modified mutants. © The Author 2009. Published by Oxford University Press. All rights reserved.

DOI： 10.1093/bioinformatics/btp298

Scopus

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スペクトル法によるタンパク質相互作用ネットワークのモジュール分解,

井上健太郎 Weijiang Li, 倉田博之

情報処理学会研究報告 BIO-17 ( 14 ) 1 - 4 2009.05

概日リズムのインターロックフィードバックのロバストネス解析

前田和勲倉田博之

情報処理学会研究報告 BIO-17 ( 15 ) 1 - 5 2009.05

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Papers - KURATA Hiroyuki