FUNAHASHI Akira

写真a

Affiliation

Faculty of Science and Technology, Department of Biosciences and Informatics （Yagami）

Position

Professor

Related Websites

External Links
Page Top ▲

Profile Summary 【 Display / hide 】

  • '''''''There are many ways to answer the question: What is life?. Our approach is to understand biological phenomena through dynamic models with mathematics, simulation and experiment. Our lab also focuses on providing computational platform to support integration between theoretical and experimental work, which plays a key role in systems biology.'''''''

Page Top ▲

Career 【 Display / hide 】

  • 1997.04
    -
    2000.03

    Research Fellow, Japan Society of the Promotion of Science (DC1)

  • 2000.05
    -
    2002.03

    Research Associate, Department of Information Technology, Mie University

  • 2002.04
    -
    2007.03

    Researcher, ERATO-SORST Kitano Symbiotic Systems Project, JST

  • 2004.04
    -
    2005.03

    Lecturer, Dept. of Electrical Engineering and Bioscience, Waseda University

  • 2004.04
    -
    2007.03

    Researcher, The Systems Biology Institute

display all >>

Page Top ▲

Academic Background 【 Display / hide 】

  • 2000.03

    Keio University, Graduate School, Division of Science and Engineering, Dept. of Computer Science

    Graduate School, Completed, Doctoral course

  • 1997.03

    Keio University, Graduate School, Division of Science and Engineering, Dept. of Computer Science

    Graduate School, Completed, Master's course

  • 1995.03

    Keio University, Faculty of Science and Engineering, Department of Electrical Engineering

    University, Graduated

Page Top ▲

Academic Degrees 【 Display / hide 】

  • Ph.D., Keio University, Coursework, 2000.03

Page Top ▲
 

Research Areas 【 Display / hide 】

  • Life Science / System genome science

  • Informatics / Life, health and medical informatics (Biological/Living Body Informatics)

Page Top ▲

Research Keywords 【 Display / hide 】

  • Systems Biology

  • Quantitative Biology

  • Computational Biology

Page Top ▲
 

Books 【 Display / hide 】

display all >>

Page Top ▲

Papers 【 Display / hide 】

  • The systems biology graphical notation.

    Novère Nicolas Le, Hucka Michael, Mi Huaiyu, Moodie Stuart, Schreiber Falk, Sorokin Anatoly, Demir Emek, Wegner Katja, Aladjem Mirit I, Wimalaratne Sarala M, Bergman Frank T, Gauges Ralph, Ghazal Peter, Kawaji Hideya, Li Lu, Matsuoka Yukiko, Villéger Alice, Boyd Sarah E, Calzone Laurence, Courtot Melanie, Dogrusoz Ugur, Freeman Tom C, FUNAHASHI AKIRA, Ghosh Samik, Jouraku Akiya, Kim Sohyoung, Kolpakov Fedor, Luna Augustin, Sahle Sven, Schmidt Esther, Watterson Steven, Wu Guanming, Goryanin Igor, Kell Douglas B, Sander Chris, Sauro Herbert, Snoep Jacky, L Kohn Kurt, Kitano Hiroaki

    Nature Biotechnology 27 （ 8 ） 735-741 - 741 2009.08

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  1087-0156

  • Using process diagrams for the graphical representation of biological networks

    Kitano Hiroaki, Funahashi Akira, Matsuoka Yukiko, Oda Kanae

    Nature Biotechnology 23 （ 8 ） 961 - 966 2005.08

    ISSN  1087-0156

    　View Summary

    <p>With the increased interest in understanding biological networks, such as protein-protein interaction networks and gene regulatory networks, methods for representing and communicating such networks in both human- and machine-readable form have become increasingly important. Although there has been significant progress in machine-readable representation of networks, as exemplified by the Systems Biology Mark-up Language (SBML) (http://www.sbml.org) issues in human-readable representation have been largely ignored. This article discusses human-readable diagrammatic representations and proposes a set of notations that enhances the formality and richness of the information represented. The process diagram is a fully state transition-based diagram that can be translated into machine-readable forms such as SBML in a straightforward way. It is supported by CellDesigner, a diagrammatic network editing software (http://www.celldesigner. org/), and has been used to represent a variety of networks of various sizes (from only a few components to several hundred components).</p>

  • A comprehensive pathway map of epidermal growth factor receptor signaling.

    Oda, K., Matsuoka, Y., Funahashi, A., and Kitano, H.

    Nature Molecular Systems Biology 1 （ 1 ） 1-17 2005.05

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  1744-4292

  • 3D convolutional neural networks-based segmentation to acquire quantitative criteria of the nucleus during mouse embryogenesis

    Tokuoka Y, Yamada T.G, Mashiko D, Ikeda Z, Hiroi N.F, Kobayashi T.J, Yamagata K, Funahashi A

    npj Systems Biology and Applications （npj Systems Biology and Applications)  6 （ 1 ） 32 2020.12

    Research paper (scientific journal), Joint Work, Accepted

    　View Summary

    © 2020, The Author(s). During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.

  • BioSimulators: a central registry of simulation engines and services for recommending specific tools

    B Shaikh, LP Smith, D Vasilescu, G Marupilla, M Wilson, E Agmon, ...

    Nucleic acids research 50 (W1), W108-W114 （Nucleic Acids Research)  50 （ W1 ） W108 - W114 2022.07

    Joint Work, Accepted,  ISSN  03051048

    　View Summary

    Computational models have great potential to accelerate bioscience, bioengineering, and medicine. However, it remains challenging to reproduce and reuse simulations, in part, because the numerous formats and methods for simulating various subsystems and scales remain siloed by different software tools. For example, each tool must be executed through a distinct interface. To help investigators find and use simulation tools, we developed BioSimulators (https://biosimulators.org), a central registry of the capabilities of simulation tools and consistent Python, command-line and containerized interfaces to each version of each tool. The foundation of BioSimulators is standards, such as CellML, SBML, SED-ML and the COMBINE archive format, and validation tools for simulation projects and simulation tools that ensure these standards are used consistently. To help modelers find tools for particular projects, we have also used the registry to develop recommendation services. We anticipate that BioSimulators will help modelers exchange, reproduce, and combine simulations.

display all >>

Page Top ▲

Papers, etc., Registered in KOARA 【 Display / hide 】

display all >>

Page Top ▲

Reviews, Commentaries, etc. 【 Display / hide 】

  • がん医療におけるAI-ここまで進んだ臨床応用-　乳がんの形態学的特徴を規定する分子メカニズム解明のための新たな医学研究手法の開発

    黒住 献, 片山 彩香, 渡辺 由佳子, 本田 周子, 関根 速子, 横堀 武彦, 潮見 隆之, 舟橋 啓, 調 憲, Ball Graham, Rakha Emad, 浅尾 高行, 小山 徹也, 藤井 孝明, 堀口 淳

    日本癌治療学会学術集会抄録集 ((一社)日本癌治療学会)  60回   SSY2 - 6 2022.10

    Joint Work

  • 生物学者が機械学習を導入するための基礎知識

    舟橋 啓

    日本獣医学会学術集会講演要旨集 ((公社)日本獣医学会)  165回   [K3A - S 2022.09

    Joint Work,  ISSN  1347-8621

  • クローズアップ実験法(series334)　遠心が1回で済む!スマートフォンによる細胞計数

    舟橋 啓, 徳岡 雄大, 今城 哉裕

    実験医学 ((株)羊土社)  39 ( 8 ) 1283 - 1290 2021.05

    Joint Work,  ISSN  0288-5514

  • COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

    Marek Ostaszewski, Alexander Mazein, Marc E. Gillespie, Inna Kuperstein, Anna Niarakis, Henning Hermjakob, Alexander R. Pico, Egon L. Willighagen, Chris T. Evelo, Jan Hasenauer, Falk Schreiber, Andreas Dräger, Emek Demir, Olaf Wolkenhauer, Laura I. Furlong, Emmanuel Barillot, Joaquin Dopazo, Aurelio Orta-Resendiz, Francesco Messina, Alfonso Valencia, Akira Funahashi, Hiroaki Kitano, Charles Auffray, Rudi Balling, Reinhard Schneider

    Scientific Data (Springer Science and Business Media LLC)  7 ( 1 )  2020.12

    Article, review, commentary, editorial, etc. (scientific journal), Joint Work,  ISSN  2052-4463

  • 機械学習によるバイオイメージセグメンテーション

    徳岡 雄大, 山田 貴大, 舟橋 啓

    機械学習を生命科学に使う! シークエンスや画像データをどう解析し、新たな生物学的発見につなげるか? 38   134 - 141 2020.12

    Joint Work, Last author, Corresponding author

display all >>

Page Top ▲

Presentations 【 Display / hide 】

  • 腫瘍内不均一性を考慮した予後の予測に向けた深層学習ベースの組織画像解析

    FUNAHASHI Akira

    群馬大学数理データ科学教育研究センター主催第1回レギュラトリー サイエンスセミナー (群馬県) , 

    2019.09
    , 

    Oral presentation (invited, special)

  • 機械学習による画像分類

    FUNAHASHI Akira, Tokuoka, Y.

    AIによる生物画像解析トレーニングコース (熊本県) , 

    2019.08
    , 

    Oral presentation (invited, special)

  • Inference of transcriptional regulatory network driven by desiccation and rehydration in Polypedilum vanderplanki

    Hiki, Y., Yamada, T.G., Kozlova, O., Cornette, R., Gusev, O., Kikawada, T., FUNAHASHI Akira

    Moscow Conference on Computational Molecular Biology 2019 (Moscow, Russia) , 

    2019.07
    , 

    Oral presentation (invited, special)

  • Mathematical Modeling with CellDesigner.

    FUNAHASHI Akira

    Computational and Mathematical Biology Course, Okinawa Institute of Science and Technology (沖縄県) , 

    2019.07
    , 

    Oral presentation (invited, special)

  • CellDesigner: A modeling tool for biochemical networks

    FUNAHASHI Akira

    COMBINE 2019 (Heidelberg, Germany) , 

    2019.07
    , 

    Oral presentation (invited, special)

display all >>

Page Top ▲

Research Projects of Competitive Funds, etc. 【 Display / hide 】

  • Advanced Bioimaging Support

    2022.04
    -
    2028.03

    Grants-in-Aid for Scientific Research, Grant-in-Aid for Transformative Research Areas (platforms for Advanced Technologies and Research Resources), No Setting

    　View Summary

    生命科学の研究領域において、イメージング技術は分子・細胞から個体に至るまで広く汎用されており、その必要性は益々増加している。
    本支援では、生理学研究所と基礎生物学研究所を中核機関として、各種の先端・特殊イメージング機器を運用している共共拠点や大学・研究機関イメージング関連施設が連携するネットワークに個別支援項目を加えたプラットフォームを組織し、光学顕微鏡技術、電子顕微鏡技術、磁気共鳴画像技術、及び画像解析技術支援を行う。加えて、国際的バイオイメージングコンソーシアム（Global　BioImaging）との連携により、日本におけるバイオイメージング技術の高度化と支援体制の充実を図る。

  • Genome protection and repair mechanisms for the extreme desiccation tolerance, anhydrobiosis

    2022.04
    -
    2026.03

    Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (A), No Setting

    　View Summary

    本研究は、ネムリユスリカの乾燥・再水和の過程で生じる”DNAの障害”が、”どの場所”で生じて、”どのような因子で保護・修復”しているのかを知ることで、"乾燥耐性をもたらすDNA修復機構"の全容を解明することを目的とする。新規DNA保護・修復因子を利用することで、乾燥などのストレスに強い作物の育種や細胞を作出することが可能となる。DNA修飾活性因子を使えば、新たなゲノム編集技術への応用も期待できる。

  • Development of a cell tracking algorithm using deep learning

    2020.04
    -
    2023.03

    Keio University, Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), No Setting

    　View Summary

    生命の発生は多数の細胞分裂により成り立ち、その現象の理解には細胞分裂の過程を記述できる細胞系譜の作成が有用である。本研究の目的は、時系列で取得される顕微鏡画像から物体(細胞)を間違いなく捕らえ、追い続けることで自動的かつ正確な細胞系譜を構築するアルゴリズムを開発することである。これにより、定量生物学やシステム生物学に代表される画像解析を活用した生命科学研究の効率化への貢献を目指す。
    令和2年度は山縣研究室から提供いただいたマウス胚4次元蛍光顕微鏡画像を用いて畳み込みニューラルネットワーク(CNN)の構造、学習アルゴリズムの適否について詳細に検討を行った。研究協力者である東京大学小林徹也博士が構築した整数計画法によるトラッキングアルゴリズムを提供いただき、この整数計画法によるトラッキングアルゴリズムを基盤に深層学習による拡張を行う手法を検討した。提供いただいた整数計画法によるトラッキングアルゴリズムはトラッキングのコストを最小化する最適化問題を解くアルゴリズムとして構成され、コストは時系列画像のフレーム間毎の各細胞同士の重心間距離が採用されている。コストの和が最小となる対応関係が時刻間の細胞の接続関係として推定されるため、本年度では上記アルゴリズムを拡張し、細胞間距離のみをコストとして採用するのではなく、他の指標を含めた最適なコスト関数を導出する学習器の設計を行った。具体的には、細胞核の形状から特徴を抽出し、コスト関数に組み込む手法を提案した。当研究室により開発された細胞同定アルゴリズムであるQCANet は世界最高精度で細胞核の形状を同定(セグメンテーション)することが可能である。次年度以降の実装方針として、QCANet により取得された細胞の形状を学習器の入力に与える手法を採用することが決定した。
    <BR>
    研究開始初年度であるため学術論文はまだ出ていないが、招待講演3回、基調講演1回、総説2報、ポスター発表1報にて本研究の進捗を報告した。
    当初の今年度の予定ではマウス胚4次元蛍光顕微鏡画像の解析及びニューラルネットワークの構造と特徴抽出についての詳細な検討、既存の画像処理アルゴリズムを採用しているトラッキングアルゴリズムとの比較を主眼としていた。実際には調査のみにとどまらず、機械学習アルゴリズムの試験的実装まで到達した。また、現状では予備評価でしかないが、既存のアルゴリズムとのトラッキング精度の比較まで進めることが出来た。今年度の成果は次年度以降の開発方針を決定する上で重要な情報であり、十分な成果を得られたと考えられる。
    今後は畳み込みニューラルネットワークを用いて物体の特徴を抽出し、形態的特徴を活用したトラッキングアルゴリズムの開発に注力する。既に深層学習を用いた細胞同定アルゴリズムであるQCANetの実装は完了している。QCANetは世界最高精度で細胞核を同定(セグメンテーション)することが可能であり、QCANetで得られた細胞核の特徴を活用し、トラッキングアルゴリズムに活用することを計画している。

  • Development of the evaluation method for differentiation and maturity of human ES, iPS cell-derived cardiomyocytes with deep learning

    2019.06
    -
    2021.03

    Keio University, Grants-in-Aid for Scientific Research, FUJITA Jun, Challenging Research (Exploratory), No Setting

    　View Summary

    We have developed a video imaging system for beating cardiomyocytes (CMs). The system was able to recognize beating CMs by vector analysis. It automatically captured their images and accurately identified regions of both CMs and non-CMs. In addition, we developed an epoch-making deep learning method that efficiently evaluated beating CMs by a segmentation algorithm, which enabled the more precise judgement of cell region. A classifier that was effective in distinguishing between CMs and non-CMs was also constructed by machine learning. Moreover, we succeeded in presenting the basis of classification by visualizing the learning device. The imaging system made it possible to efficiently acquire a large number of teaching data, which was a bottle neck in the existing analysis with artificial intelligence, and a large quantity of data was analyzed by the developed learning machine. Furthermore, we confirmed important genes in the process of differentiation into CMs and maturation of CMs.

  • Efficient maturation of 3D tissues in vitro by intelligent mechanical stimuli

    2019.04
    -
    2022.03

    Keio University, Grants-in-Aid for Scientific Research, Onoe Hiroaki, Grant-in-Aid for Scientific Research (A), No Setting

    　View Summary

    A three-dimensional culture chamber with mechanical or electrical stimulation was constructed as an intelligent culture system, enabling real-time fluorescent observation of morphology while culturing tissue. We cultured myoblast cell line C2C12 and human iPS-derived cardiomyocytes, evaluated their maturation while applying stimuli, and established an experimental cycle to improve maturation indices by extracting maturation indices by image processing and optimizing conditions by Bayesian optimization in the C2C12 and electrical stimulation system. In addition, we constructed human iPS-derived cardiomyocytes in the shape of 3D fibers and matured them by electrical stimulation and observed changes in tissue contractility when drugs were applied, indicating the possibility of applying the formed cardiomyocytes to drug testing.

display all >>

Page Top ▲

Awards 【 Display / hide 】

  • 平成27年度科学研究費審査委員表彰

    2015.10, Japan Society for the Promotion of Science

Page Top ▲
 

Courses Taught 【 Display / hide 】

  • TOPICS IN BIOSCIENCES AND INFORMATICS 1

    2024

  • SYSTEMS BIOLOGY

    2024

  • SEMINAR IN BIOSCIENCES AND INFORMATICS

    2024

  • MOLECULAR SCIENCE OF LIFE

    2024

  • METHODOLOGY FOR POST-GENOME BIOSCIENCES

    2024

display all >>

Page Top ▲

Courses Previously Taught 【 Display / hide 】

  • 生命情報実験D

    Keio University

    2017.04
    -
    2018.03
    , 

    Autumn Semester, Laboratory work/practical work/exercise

  • 生命情報実験C

    Keio University

    2017.04
    -
    2018.03
    , 

    Autumn Semester, Laboratory work/practical work/exercise

  • 基礎生命実験

    Keio University

    2017.04
    -
    2018.03
    , 

    Autumn Semester, Laboratory work/practical work/exercise, 40people

  • 生命情報特別講義第1

    Keio University

    2017.04
    -
    2018.03
    , 

    Autumn Semester, Lecture

  • 先端創薬科学

    Keio University

    2017.04
    -
    2018.03
    , 

    Autumn Semester, Lecture, 40people

display all >>

Page Top ▲
 

Committee Experiences 【 Display / hide 】

  • 2019.04
    -
    Present

    JST未来事業「共通基盤」領域 専門アドバイザー, Japan Science and Technology Agency

  • 2023.04
    -
    Present

    学術変革領域研究(B) スケール横断分析 アドバイザー, 日本学術振興会

  • 2020.05
    -
    Present

    Google Summer of Code Mentors, Google Summer of Code 2020

  • 2019.05
    -
    2019.08

    Google Summer of Code Mentors, Google Summer of Code 2019

  • 2018.08
    -
    2019.11

    International Conference on Systems Biology (ICSB) 2018 Scientific Committee, International Conference on Systems Biology (ICSB)

display all >>

Page Top ▲