研究者詳細 - 倉田　博之

論文 - 倉田　博之

分割表示251 件中 101 - 120 件目／全件表示 >>

CADLIVE Converter for constructing a biochemical network map 査読有り

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

Biochem Eng J 54 200 - 206 2011年08月

An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks 査読有り

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

Bioproc Biosys Eng 34 433 - 446 2011年08月

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

Hiroyuki Kurata, Quanyu Zhao

Proceedings of Asian Congress on Biotechnology B0768 2011年05月

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年03月

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年03月

Diffusion model based spectral clustering for protein-protein interaction networks. 査読有り

Inoue K., Li W., Kurata H.

PloS one 5 ( 9 ) 2010年12月

担当区分：責任著者掲載種別：研究論文（学術雑誌）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=79952277084&origin=inward

バイオテクノロジー査読有り

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年10月

Use of maximum entropy principle with Lagrange multipliers extends the feasibility of elementary mode analysis 査読有り

Zhao Quanyu, Kurata Hiroyuki

Journal of bioscience and bioengineering ( 公益社団法人日本生物工学会 ) 110 ( 2 ) 254 - 261 2010年08月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Elementary mode (EM) analysis is potentially effective in integrating transcriptome or proteome data into metabolic network analyses and in exploring the mechanism of how phenotypic or metabolic flux distribution is changed with respect to environmental and genetic perturbations. The EM coefficients (EMCs) indicate the quantitative contribution of their associated EMs and can be estimated by maximizing Shannon's entropy as a general objective function in our previous study, but the use of EMCs is still restricted to a relatively small-scale networks. We propose a fast and universal method that optimizes hundreds of thousands of EMCs under the constraint of the Maximum entropy principle (MEP). Lagrange multipliers (LMs) are applied to maximize the Shannon's entropy-based objective function, analytically solving each EMC as the function of LMs. Consequently, the number of such search variables, the EMC number, is dramatically reduced to the reaction number. To demonstrate the feasibility of the MEP with Lagrange multipliers (MEPLM), it is coupled with enzyme control flux (ECF) to predict the flux distributions of Escherichia coli and Saccharomyces cerevisiae for different conditions (gene deletion, adaptive evolution, temperature, and dilution rate) and to provide a quantitative understanding of how metabolic or physiological states are changed in response to these genetic or environmental perturbations at the elementary mode level. It is shown that the ECF-based method is a feasible framework for the prediction of metabolic flux distribution by integrating enzyme activity data into EMs to genetic and environmental perturbations.

CiNii Article

その他リンク： https://ci.nii.ac.jp/naid/110007700578

Use of maximum entropy principle with Lagrange multipliers extends the feasibility of elementary mode analysis 査読有り

Quanyu Zhao, Hiroyuki Kurata

J Biosci Bioeng 110 254 - 261 2010年08月

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査読有り

倉田博之, 清水和幸

生物工学会誌 : seibutsu-kogaku kaishi ( 日本生物工学会 ) 88 ( 5 ) 233 - 236 2010年05月

Diffusion Model Based Spectral Clustering For Protein-Protein Interaction Networks 査読有り

Kentaro Inoue, Weijiang Li and Hiroyuki Kurata

PLOS ONE 5 e12623 2010年04月

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

Maeda K., Kurata H.

IPSJ Transactions on Bioinformatics ( 社団法人情報処理学会 ) 2 2 - 14 2009年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Dynamic simulations are essential for understanding the mechanism of how biochemical networks generate robust properties to environmental stresses or genetic changes. However, typical dynamic modeling and analysis yield only local properties regarding a particular choice of plausible values of kinetic parameters, because it is hard to measure the exact values in vivo. Global and firm analyses are needed that consider how the changes in parameter values affect the results. A typical solution is to systematically analyze the dynamic behaviors in large parameter space by searching all plausible parameter values without any biases. However, a random search needs an enormous number of trials to obtain such parameter values. Ordinary evolutionary searches swiftly obtain plausible parameters but the searches are biased. To overcome these problems, we propose the two-phase search method that consists of a random search and an evolutionary search to effectively explore all possible solution vectors of kinetic parameters satisfying the target dynamics. We demonstrate that the proposed method enables a nonbiased and high-speed parameter search for dynamic models of biochemical networks through its applications to several benchmark functions and to the E. coli heat shock response model. © 2009 Information Processing Society of Japan.

DOI： 10.2197/ipsjtbio.2.2

Scopus

CiNii Article

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77951099999&origin=inward

Module-based analysis of the robustness generated by complex feedback regulations in biological networks 査読有り

Kurata H.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings 192 - 195 2009年12月

担当区分：責任著者掲載種別：研究論文（国際会議プロシーディングス）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77951104212&origin=inward

バイオテクノロジー査読有り

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年10月

Module-based analysis of the robustness generated by complex feedback regulations in biological networks 査読有り

Hiroyuki Kurata

Proceedings of ICROS-SICE International Joint Conference 2009 1A11-101 2009年08月

Elementary model-based prediction of the flux distribution for a variety of genetic mutants 査読有り

Quanyu Zhao, Hiroyuki Kurata

J Biosci Bioeng: Proceedings of APBioCHEC'2009 108 S165 2009年08月

Genetic modification of flux for flux prediction of mutants 査読有り

Quanyu Zhao, Hiroyuki Kurata

Bioinformatics 25 1702 - 1708 2009年07月

Genetic modification of flux for flux prediction of mutants 査読有り

Zhao Q., Kurata H.

Bioinformatics 25 ( 13 ) 1702 - 1708 2009年06月

担当区分：責任著者掲載種別：研究論文（学術雑誌）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=67649218402&origin=inward

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年05月

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年05月

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