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 】

  • 三次元組織の高度成熟化を自律的に達成する知能化培養システム基盤の創出

    2023.04
    -
    2026.03

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

    　View Summary

    本研究は筋組織のin vitro 3次元組織形成において「人の介在と思い込み」を排除し，生命科学実験を自動的・自律的に実行可能とする「知能化培養システム」を構築する．この培養システムは，3次元組織培養・力学刺激・蛍光観察が可能な培養モジュールを複数並列して順次的に培養することで，「観察」「計画」「行動」「発見」という科学者の研究サイクルをアルゴリズムとシステムインテグレーション技術により自律的に行い，生命科学の重要な課題である組織成熟化と力学刺激の関連性の解明を目指す．さらに本技術をヒトiPS由来心筋組織に適用することで高度に成熟化したin vitro心筋組織を構築し，医療応用展開を探索する．

  • 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, Funahashi Akira, Grant-in-Aid for Scientific Research (B), No Setting

    　View Summary

    Using deep learning, we have developed an image processing algorithm that can accurately track both cell migration and cell division in 3D time-series fluorescence microscopy images of mouse embryonic development, which was previously difficult. Existing tracking algorithms have difficulty detecting both cell migration and cell division simultaneously, and the accuracy of cell division tracking is very low. In this research project, we developed a tracking algorithm that combined deep learning and integer programming and accurately tracked cells up to the 40-cell stage.

  • 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.

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 】

  • SEMINAR IN BIOSCIENCES AND INFORMATICS

    2024

  • MOLECULAR SCIENCE OF LIFE

    2024

  • METHODOLOGY FOR POST-GENOME BIOSCIENCES

    2024

  • INTRODUCTION TO INTERDISCIPLINARY SCIENCE AND TECHNOLOGY

    2024

  • INTRODUCTION TO COMPUTER SCIENCE

    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 ▲