Faculty Profiles - MAEDA Kazuhiro

Papers - MAEDA Kazuhiro

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Wireframe DNA Origami Capable of Vertex-protruding Transformation Reviewed International journal

ChemBioChem 2025.02

PredIL13: stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides Reviewed

Hiroyuki Kurata, Md. Harun-Or-Roshid, Sho Tsukiyama, Kazuhiro Maeda

PLOS ONE 2024.08

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models Reviewed International journal

Methods 2024.05

Elucidating key characteristics of PFAS binding to human peroxisome proliferator-activated receptor alpha: an explainable machine learning approach Reviewed International journal

Kazuhiro Maeda, Masashi Hirano, Taka Hayashi, Midori Iida, Hiroyuki Kurata, Hiroshi Ishibashi

Environmental Science & Technology 2023.12

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach Reviewed International journal

Md. Harun-Or-Roshid, Kazuhiro Maeda,Phan Thi Le, Balachandran Manavalan, Hiroyuki Kurata

Computers in Biology and Medicine 2023.12

Automatic Generation of SBML Kinetic Models from Natural Language Texts using GPT Reviewed International journal

Kazuhiro Maeda, Hiroyuki Kurata

International Journal of Molecular Sciences 2023.04

MLAGO: Machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling Reviewed International journal

Kazuhiro Maeda, Aoi Hatae, Yukie Sakai, Fred C. Boogerd, Hiroyuki Kurata

BMC Bioinformatics 2022.11

Simulation of the crosstalk between glucose- and acetaminophen metabolism in a liver zonation model "jointly worked" Reviewed International journal

Kazuhiro Maeda, Shuta Hagimori, Masahiro Sugimoto, Yasuyuki Sakai, Masaki Nishikawa

Frontiers in Pharmacology 2022.08

RCGAToolbox: 動力学モデルのパラメーター推定のための実数値遺伝的アルゴリズムソフトウェア

前田和勲,Fred C. Boogerd,倉田博之

IPSJ SIG Technical Report 2022.03

RCGAToolbox: A real-coded genetic algorithm software for parameter estimation of kinetic models Reviewed

Kazuhiro Maeda, Fred C. Boogerd, Hiroyuki Kurata

IPSJ Transactions on Bioinformatics 2021.09

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network Reviewed

5 ( 1 ) 2019.12

動力学パラメータ推定問題：大腸菌アンモニア輸送-同化ネットワークの事例

前田和勲

IPSJ SIG Technical Report 2019.09

libRCGA: 動力学モデルの高速なパラメータ推定のための遺伝的アルゴリズムライブラリ

前田和勲, Fred C. Boogerd, 倉田博之

IPSJ SIG Technical Report 2018.09

Long negative feedback loop enhances period tunability of biological oscillators Reviewed

440 21 - 31 2018.03

libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models Reviewed

11 ( 0 ) 31 - 40 2018.01

Web application for genetic modification flux with database to estimate metabolic fluxes of genetic mutants Reviewed

122 ( 1 ) 111 - 116 2016.07

Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli Reviewed

15 ( 1 ) 2016.06

Analytical study of robustness of a negative feedback oscillator by multiparameter sensitivity Reviewed

8 ( 5 ) 2014.12

Dynamic modeling of metabolic and gene regulatory systems toward developing virtual microbes Reviewed

47 ( 1 ) 1 - 9 2014.01

CADLIVE toolbox for MATLAB: Automatic dynamic modeling of biochemical networks with comprehensive system analysis Reviewed

37 ( 9 ) 1925 - 1927 2014.01

大腸菌アンモニア同化制御機構のダイナミックモデルの構築

前田和勲, FredC.Boogerd, FrankJ.Bruggeman, HansV.Westerhoff, 倉田博之

研究報告バイオ情報学（BIO） 2013 ( 7 ) 1 - 6 2013.09

Language：Japanese Publishing type：Research paper (scientific journal)

アンモニアは大腸菌にとって最適な窒素源である．大腸菌は，TCA 回路の中間代謝物である α-ケトグルタル酸にアンモニアを付加し，グルタミン酸とグルタミンを合成する．細胞内の窒素のほとんどはこの２つの代謝物に由来するものである．このような理由から，大腸菌がどのようにアンモニアを同化し，グルタミン酸とグルタミンの合成を制御しているのか明らかにすることは重要である．我々は，大腸菌アンモニア同化システムの新しいダイナミックモデルを構築した．動力学パラメータ推定では，推定値と測定値の差が最小になるように遺伝的アルゴリズムにペナルティ戦略を組み合わせた．我々のモデルは，近年発表されたメタボロームデータを定量的に再現できる．最近，グルタミン酸合成酵素に対するアスパラギン酸の競争阻害の重要性が指摘されたが，本稿ではその検証も行う．また GOGAT 欠損株と GOGAT 欠損サプレッサー変異株の解析も行う．アンモニアが豊富な条件では，グルタミン酸の過剰な蓄積によってサプレッサー変異株は野生株よりも増殖が遅いことが予測された．

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/110009605329

Flux module decomposition for parameter estimation in a multiple-feedback loop model of biochemical networks Reviewed

36 ( 3 ) 333 - 344 2013.03

BioFNet: Biological functional network database for analysis and synthesis of biological systems Reviewed

15 ( 5 ) 699 - 709 2013.01

CADLIVE optimizer: web-based parameter estimation for dynamic models Reviewed

7 2012.08

A symmetric dual feedback system provides a robust and entrainable oscillator Reviewed

7 ( 2 ) 2012.02

Biological design principles of complex feedback modules in the e. coli ammonia assimilation system Reviewed

18 ( 1 ) 53 - 90 2012.01

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

34 ( 4 ) 433 - 446 2011.05

Quasi-multiparameter sensitivity measure for robustness analysis of complex biochemical networks Reviewed

272 ( 1 ) 174 - 186 2011.03

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

2 2 - 14 2009.12

Robustness Analysis of Interlocked Feedbacks of Circadian Rhythm

MAEDA KAZUHIRO, KURATA HIROYUKI

2009 ( 15 ) 1 - 5 2009.05

A gradual update method for simulating the steady-state solution of stiff differential equations in metabolic circuits Reviewed

32 ( 2 ) 283 - 288 2009.02

Two-phase Search (TPS) Method: Nonbiased and High-speed Parameter Search for Dynamic Models of Biochemical Networks Reviewed

4 ( 2 ) 377 - 389 2009.01

Two-phase search (TPS) 法：動的生化学ネットワークモデルの偏りのない効率的なパラメータ探索

前田和勲, 倉田博之

情報処理学会研究報告バイオ情報学（BIO） 2008 ( 86 ) 9 - 12 2008.09

Language：Japanese Publishing type：Research paper (scientific journal)

ダイナミックシミュレーションは環境ストレスや遺伝的変化に対してロバストネスを生み出す生化学ネットワークのメカニズムの解明に不可欠である。しかし、これまでに行われた多くのシミュレーションや解析は特定の速度パラメータ値に依存している。速度パラメータの正確な測定は困難であるので、特定のパラメータ値に依存するのではなく、パラメータ値の変化が与える影響を考慮した解析が必要である。これを可能にするためには細胞内の挙動を再現しうる全ての速度パラメータ値を広い探索空間から偏りなく探し出さなければならない。本稿では、ランダム探索と進化的探索を組み合わせた新しいパラメータ探索法、Two-phase search 法を提案する。そして、この手法が偏りなく効率的に速度パラメータを探索できることを示す。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. 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. In this paper, 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. It enables a nonbiased and high-speed parameter search for dynamic models of biochemical networks.

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/110006967151

Extended CADLIVE: A novel graphical notation for design of biochemical network maps and computational pathway analysis Reviewed

35 ( 20 ) 2007.11

Global Optimization of Large-scale Dynamic Biomolecular Network Model

MAEDA KAZUHIRO, YOSHIDA KEISUKE, KURATA HIROYUKI

IPSJ SIG Notes 2007 ( 86 ) 91 - 94 2007.09

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Papers - MAEDA Kazuhiro