前田 和勲 (マエダ カズヒロ)

MAEDA Kazuhiro

写真a

職名

助教

研究室住所

福岡県飯塚市川津680-4

研究分野・キーワード

システム生物学、代謝ネットワーク、ハイパフォーマンスコンピューティング、スーパーコンピュータ、生化学パラメータ推定

取得学位 【 表示 / 非表示

  • 九州工業大学 -  博士(情報工学)  2011年03月

学内職務経歴 【 表示 / 非表示

  • 2019年12月
    -
    継続中

    九州工業大学   大学院情報工学研究院   生命化学情報工学研究系   助教  

  • 2015年03月
    -
    2019年11月

    九州工業大学   若手研究者フロンティア研究アカデミー   特任助教  

 

論文 【 表示 / 非表示

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

    Maeda K., Westerhoff H., Kurata H., Boogerd F.

    npj Systems Biology and Applications    5 ( 1 )   2019年12月  [査読有り]

     概要を見る

    © 2019, The Author(s). The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a ‘rubber band method’ to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

    DOI Scopus

  • Long negative feedback loop enhances period tunability of biological oscillators

    Maeda K., Kurata H.

    Journal of Theoretical Biology    440   21 - 31   2018年03月  [査読有り]

     概要を見る

    © 2017 Elsevier Ltd Oscillatory phenomena play a major role in organisms. In some biological oscillations such as cell cycles and heartbeats, the period can be tuned without significant changes in the amplitude. This property is called (period) tunability, one of the prominent features of biological oscillations. However, how biological oscillators produce tunable oscillations remains largely unexplored. We tackle this question using computational experiments. It has been reported that positive-plus-negative feedback oscillators produce tunable oscillations through the hysteresis-based mechanism. First, in this study, we confirmed that positive-plus-negative feedback oscillators generate tunable oscillations. Second, we found that tunability is positively correlated with the dynamic range of oscillations. Third, we showed that long negative feedback oscillators without any additional positive feedback loops can produce tunable oscillations. Finally, we computationally demonstrated that by lengthening the negative feedback loop, the Repressilator, known as a non-tunable synthetic gene oscillator, can be converted into a tunable oscillator. This work provides synthetic biologists with clues to design tunable gene oscillators.

    DOI Scopus

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

    Maeda Kazuhiro, Boogerd Fred C., Kurata Hiroyuki

    IPSJ Transactions on Bioinformatics    11 ( 0 ) 31 - 40   2018年01月  [査読有り]

     概要を見る

    <p>Kinetic modeling is a powerful tool to understand how a biochemical system behaves as a whole. To develop a realistic and predictive model, kinetic parameters need to be estimated so that a model fits experimental data. However, parameter estimation remains a major bottleneck in kinetic modeling. To accelerate parameter estimation, we developed a C library for real-coded genetic algorithms (libRCGA). In libRCGA, two real-coded genetic algorithms (RCGAs), viz. the Unimodal Normal Distribution Crossover with Minimal Generation Gap (UNDX/MGG) and the Real-coded Ensemble Crossover star with Just Generation Gap (REX<sup> star</sup>/JGG), are implemented in C language and paralleled by Message Passing Interface (MPI). We designed libRCGA to take advantage of high-performance computing environments and thus to significantly accelerate parameter estimation. Constrained optimization formulation is useful to construct a realistic kinetic model that satisfies several biological constraints. libRCGA employs stochastic ranking to efficiently solve constrained optimization problems. In the present paper, we demonstrate the performance of libRCGA through benchmark problems and in realistic parameter estimation problems. libRCGA is freely available for academic usage at http://kurata21.bio.kyutech.ac.jp/maeda/index.html.</p>

    DOI CiNii

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

    Mohd Ali N., Tsuboi R., Matsumoto Y., Koishi D., Inoue K., Maeda K., Kurata H.

    Journal of Bioscience and Bioengineering    122 ( 1 ) 111 - 116   2016年07月  [査読有り]

     概要を見る

    © 2015 The Society for Biotechnology, Japan. Computational analysis of metabolic fluxes is essential in understanding the structure and function of a metabolic network and in rationally designing genetically modified mutants for an engineering purpose. We had presented the genetic modification flux (GMF) that predicts the flux distribution of a broad range of genetically modified mutants. To enhance the feasibility and usability of GMF, we have developed a web application with a metabolic network database to predict a flux distribution of genetically modified mutants. One hundred and twelve data sets of Escherichia coli, Corynebacterium glutamicum, Saccharomyces cerevisiae, and Chinese hamster ovary were registered as standard models.

    DOI Scopus

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

    Jahan N., Maeda K., Matsuoka Y., Sugimoto Y., Kurata H.

    Microbial Cell Factories    15 ( 1 )   2016年06月  [査読有り]

     概要を見る

    © 2016 The Author(s). Background: A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters. Results: In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture. Conclusions: This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.

    DOI Scopus

全件表示 >>

作品 【 表示 / 非表示

  • libRCGA

    2018年06月
     
     
     

     概要を見る

    libRCGA is a C library for real-coded genetic algorithms (RCGAs). Currently, two RCGAs, UNDX/MGG and REXstar/JGG, are implemented. For constrained optimization problems, the stochastic ranking can be used. RCGAs are paralleled by MPI. For details, see the original paper: Kazuhiro Maeda, Fred C. Boogerd, and Hiroyuki Kurata, libRCGA: a C library for real-coded genetic algorithms for rapid parameter estimation of kinetic models, IPSJ Transactions on Bioinformatics, 11: 31-40, 2018 (https://www.jstage.jst.go.jp/article/ipsjtbio/11/0/11_31/_article/-char/en).

    libRCGA is available from https://github.com/kmaeda16/libRCGA.

学術関係受賞 【 表示 / 非表示

  • 情報処理学会 山下記念研究賞

    2020年03月01日   情報処理学会   日本国

    受賞者:  前田和勲

  • 情報処理学会第55回バイオ情報学研究会 SIGBIO優秀プレゼンテーション賞

    2018年09月   情報処理学会 バイオ情報学研究会   日本国

    受賞者:  前田和勲

  • 情報処理学会第51回バイオ情報学研究会 SIGBIO優秀プレゼンテーション賞

    2017年09月   情報処理学会 バイオ情報学研究会   日本国

    受賞者:  前田和勲

  • 平成21年度IPSJ論文船井若手奨励賞

    2010年03月   船井情報科学振興財団   日本国

    受賞者:  前田和勲

科研費獲得実績 【 表示 / 非表示

  • 大腸菌アンモニア同化制御の定量的ダイナミックモデル構築とシステム的理解

    若手研究(B)

    研究期間:  2014年04月  -  2017年03月

    研究課題番号:  26870432

  • 生体分子ネットワークのシステム的理解に基づく抗がん剤投薬スケジューリング法の開発

    研究活動スタート支援

    研究期間:  2012年08月  -  2014年03月

    研究課題番号:  24800050

  • 生命システムのアドホックな進化

    特別研究員奨励費

    研究期間:  2010年04月  -  2012年03月

    研究課題番号:  10J07813

その他競争的資金獲得実績 【 表示 / 非表示

  • libRCGA: 動力学モデルの高速なパラメータ推定のための実数値遺伝的アルゴリズムのC言語ライブラリ

    提供機関:  九州工業大学 

    研究期間:  2018年09月  -  2018年09月

  • 生体分子ネットワークの効率的なダイナミックモデル構築手法の開発

    提供機関:  九州工業大学 

    研究期間:  2018年04月  -  2018年07月

  • 大腸菌のアンモニア同化制御の理解とグルタミン酸発酵向上

    提供機関:  民間財団等 

    研究期間:  2017年06月  -  2017年07月

  • 大腸菌アンモニア輸送-同化システムのシミュレーションモデル構築とその制御機構の理解

    提供機関:  九州工業大学 

    研究期間:  2017年02月  -  2017年03月

 

教育活動に関する受賞・指導学生の受賞など 【 表示 / 非表示

  • Project Award: Bronze

    2019年10月   Annual biomolecular design competition for students (BIOMOD)

  • 第3位(トラベルアワード)

    2019年08月   分子ロボティクス研究会