Reviews, Commentaries, etc. - FUNAHASHI Akira

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

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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

Google Colaboratory入門―機械学習を体験しよう

舟橋 啓, 徳岡 雄大, 山田 貴大

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   16 - 35 2020.12

Joint Work, Lead author, Corresponding author

クラシフィケーションの原理と生物・医療への応用

山田 貴大, 徳岡 雄大, 尾関 光徳, 井伊 海人, 広井 賀子, 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   109 - 117 2020.12

Joint Work, Last author, Corresponding author

【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?】(第1章)機械学習入門　Google Colaboratory入門　機械学習を体験しよう

舟橋 啓, 徳岡 雄大, 山田 貴大

実験医学 ((株)羊土社)  38 ( 20 ) 3322 - 3341 2020.12

Joint Work,  ISSN  0288-5514

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本稿はGoogle Colaboratoryを使用して機械学習を体験していただくことを目的としている。題材は位相差顕微鏡で撮像されたHeLa細胞の生死分類である。できるだけ機械学習の前提知識のない読者でもとりあえず機械学習のプログラムを実行し、精度評価までたどり着けるような構成にしたつもりである。プログラムはgithubにアップロードされているものを実行してもらう形式にしているため、インターネットに接続されているPC、Macや、iPad、iPhone等のタブレット、スマートフォンさえあれば誰でも試せる構成になっている。本稿を契機に機械学習を利用した生命科学研究が増えることを期待している。(著者抄録)

【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?】(第3章)画像解析　画像の分類・特徴化　クラシフィケーションの原理と生物・医療への応用

山田 貴大, 徳岡 雄大, 尾関 光徳, 井伊 海人, 広井 賀子, 舟橋 啓

実験医学 ((株)羊土社)  38 ( 20 ) 3415 - 3423 2020.12

Joint Work,  ISSN  0288-5514

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"分類"を意味するクラシフィケーションの応用事例は幅広く、迷惑メールフィルタのような一般生活に根ざしたものから、近年では病理診断や再生医療などの医療分野への適用事例が報告されはじめている。本稿では、クラシフィケーションの数理的な原理を概説したうえで、近年発展の著しい深層学習に基づくクラシフィケーションについて解説する。最後にクラシフィケーションを生命現象の解明や医療応用へ向けた研究事例としてわれわれが取り組んでいる課題について紹介する。(著者抄録)

【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?】(第3章)画像解析　画像からの細胞・運動情報抽出　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

実験医学 ((株)羊土社)  38 ( 20 ) 3440 - 3447 2020.12

Joint Work,  ISSN  0288-5514

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近年、機械学習を用いたセグメンテーション技術は目まぐるしい進歩を遂げており、バイオイメージへの応用も多数報告されている。本稿では、そのような機械学習によるバイオイメージセグメンテーションの技術的基礎と背景を中心に概説する。また、近年の研究動向や技術的課題などについても包括的に触れ、最後にわれわれが取り組んでいる課題についても紹介したい。(著者抄録)

SBML Level 3: an extensible format for the exchange and reuse of biological models

Sarah M Keating, Dagmar Waltemath, Matthias König, Fengkai Zhang, Andreas Dräger, Claudine Chaouiya, Frank T Bergmann, Andrew Finney, Colin S Gillespie, Tomáš Helikar, Stefan Hoops, Rahuman S Malik‐Sheriff, Stuart L Moodie, Ion I Moraru, Chris J Myers, Aurélien Naldi, Brett G Olivier, Sven Sahle, James C Schaff, Lucian P Smith, Maciej J Swat, Denis Thieffry, Leandro Watanabe, Darren J Wilkinson, Michael L Blinov, Kimberly Begley, James R Faeder, Harold F Gómez, Thomas M Hamm, Yuichiro Inagaki, Wolfram Liebermeister, Allyson L Lister, Daniel Lucio, Eric Mjolsness, Carole J Proctor, Karthik Raman, Nicolas Rodriguez, Clifford A Shaffer, Bruce E Shapiro, Joerg Stelling, Neil Swainston, Naoki Tanimura, John Wagner, Martin Meier‐Schellersheim, Herbert M Sauro, Bernhard Palsson, Hamid Bolouri, Hiroaki Kitano, Akira Funahashi, Henning Hermjakob, John C Doyle, Michael Hucka, Richard R Adams, Nicholas A Allen, Bastian R Angermann, Marco Antoniotti, Gary D Bader, Jan Červený, Mélanie Courtot, Chris D Cox, Piero Dalle Pezze, Emek Demir, William S Denney, Harish Dharuri, Julien Dorier, Dirk Drasdo, Ali Ebrahim, Johannes Eichner, Johan Elf, Lukas Endler, Chris T Evelo, Christoph Flamm, Ronan MT Fleming, Martina Fröhlich, Mihai Glont, Emanuel Gonçalves, Martin Golebiewski, Hovakim Grabski, Alex Gutteridge, Damon Hachmeister, Leonard A Harris, Benjamin D Heavner, Ron Henkel, William S Hlavacek, Bin Hu, Daniel R Hyduke, Hidde Jong, Nick Juty, Peter D Karp, Jonathan R Karr, Douglas B Kell, Roland Keller, Ilya Kiselev, Steffen Klamt, Edda Klipp, Christian Knüpfer, Fedor Kolpakov, Falko Krause, Martina Kutmon, Camille Laibe, Conor Lawless, Lu Li, Leslie M Loew, Rainer Machne, Yukiko Matsuoka, Pedro Mendes, Huaiyu Mi, Florian Mittag, Pedro T Monteiro, Kedar Nath Natarajan, Poul MF Nielsen, Tramy Nguyen, Alida Palmisano, Jean‐Baptiste Pettit, Thomas Pfau, Robert D Phair, Tomas Radivoyevitch, Johann M Rohwer, Oliver A Ruebenacker, Julio Saez‐Rodriguez, Martin Scharm, Henning Schmidt, Falk Schreiber, Michael Schubert, Roman Schulte, Stuart C Sealfon, Kieran Smallbone, Sylvain Soliman, Melanie I Stefan, Devin P Sullivan, Koichi Takahashi, Bas Teusink, David Tolnay, Ibrahim Vazirabad, Axel Kamp, Ulrike Wittig, Clemens Wrzodek, Finja Wrzodek, Ioannis Xenarios, Anna Zhukova, Jeremy Zucker

Molecular Systems Biology (EMBO)  16 ( 8 )  2020.08

Joint Work,  ISSN  1744-4292

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Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.

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Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11

Takahiro G. Yamada, Axel Imhof, Yusuke Hiki, Noriko F. Hiroi, Elena Shagimardanova, Oleg Gusev, Richard Cornette, Takahiro Kikawada, Akira Funahashi

PLOS ONE (PLoS ONE)  15 ( 3 ) e0230218 2020.03

Joint Work, Last author, Corresponding author

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Water is essential for living organisms. Terrestrial organisms are incessantly exposed to the stress of losing water, desiccation stress. Avoiding the mortality caused by desiccation stress, many organisms acquired molecular mechanisms to tolerate desiccation. Larvae of the African midge, Polypedilum vanderplanki, and its embryonic cell line Pv11 tolerate desiccation stress by entering an ametabolic state, anhydrobiosis, and return to active life after rehydration. The genes related to desiccation tolerance have been comprehensively analyzed, but transcriptional regulatory mechanisms to induce these genes after desiccation or rehydration remain unclear. Here, we comprehensively analyzed the gene regulatory network in Pv11 cells and compared it with that of Drosophila melanogaster, a desiccation sensitive species. We demonstrated that nuclear transcription factor Y subunit gamma-like, which is important for drought stress tolerance in plants, and its transcriptional regulation of downstream positive feedback loops have a pivotal role in regulating various anhydrobiosis-related genes. This study provides an initial insight into the systemic mechanism of desiccation tolerance.

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Neural Differentiation Dynamics Controlled by Multiple Feedback Loops in a Comprehensive Molecular Interaction Network

Tsuyoshi Iwasaki, Ryo Takiguchi, Takumi Hiraiwa, Takahiro G. Yamada, Kazuto Yamazaki, Noriko F. Hiroi, Akira Funahashi

Processes (Processes)  8 ( 2 )  2020.02

Joint Work, Last author

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Mathematical model simulation is a useful method for understanding the complex behavior of a living system. The construction of mathematical models using comprehensive information is one of the techniques of model construction. Such a comprehensive knowledge-based network tends to become a large-scale network. As a result, the variation of analyses is limited to a particular kind of analysis because of the size and complexity of the model. To analyze a large-scale regulatory network of neural differentiation, we propose a contractive method that preserves the dynamic behavior of a large network. The method consists of the following two steps: comprehensive network building and network reduction. The reduction phase can extract network loop structures from a large-scale regulatory network, and the subnetworks were combined to preserve the dynamics of the original large-scale network. We confirmed that the extracted loop combination reproduced the known dynamics of HES1 and ASCL1 before and after differentiation, including oscillation and equilibrium of their concentrations. The model also reproduced the effects of the overexpression and knockdown of the Id2 gene. Our model suggests that the characteristic change in HES1 and ASCL1 expression in the large-scale regulatory network is controlled by a combination of four feedback loops, including a large loop, which has not been focused on. The model extracted by our method has the potential to reveal the critical mechanisms of neural differentiation. The method is applicable to other biological events.

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Direct Cell Counting Using Macro-Scale Smartphone Images of Cell Aggregates

Chikahiro Imashiro, Yuta Tokuoka, Kaito Kikuhara, Takahiro G. Yamada, Kenjiro Takemura, Akira Funahashi

IEEE Access (Institute of Electrical and Electronics Engineers (IEEE))  8   170033 - 170043 2020

Joint Work, Last author, Corresponding author

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The field of bioengineering depends on technologies for stable cell culture. Conventionally, every process involved in cell culture has been performed manually, so the culture efficiency and stability can vary between trials or depending on the technician. Among these processes, cell counting is particularly important because cell density affects cell function. Conventional cell counting techniques for cell number estimation are inefficient and unstable because they involve the manual work of collecting a sample of the cell suspension. Thus, a cell counting method that is not susceptible to human error is needed. In this study, we present a novel cell counting method based on smartphone imaging and convolutional neural network-based image processing. Cells are aggregated by centrifuging in a tube and then imaged using a smartphone. The image is transferred to a server, and the cell number is predicted using convolutional neural networks on the server. All processes are performed by a custom-developed smartphone-compatible web app. Compared with the conventional method using a hemocytometer, our method yields more stable cell counting. Furthermore, the time and labor required for cell counting are significantly reduced. Our new method could potentially replace conventional cell counting techniques and thus enhance the stability and efficiency of bioengineering studies that require cell culture.

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Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs

Maya Ooka, Yuta Tokuoka, Shori Nishimoto, Noriko Hiroi, Takahiro G Yamada, Akira Funahashi

Applied Sciences (Applied Sciences (Switzerland))  9 ( 24 )  2019.12

Joint Work, Last author, Corresponding author

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Regenerative medicine using neural stem cells (NSCs), which self-renew and have pluripotency, has recently attracted a lot of interest. Much research has focused on the transplantation of differentiated NSCs to damaged tissues for the treatment of various neurodegenerative diseases and spinal cord injuries. However, current approaches for distinguishing differentiated from non-differentiated NSCs at the single-cell level have low reproducibility or are invasive to the cells. Here, we developed a fully automated, non-invasive convolutional neural network-based model to determine the differentiation status of human NSCs at the single-cell level from phase-contrast photomicrographs; after training, our model showed an accuracy of identification greater than 94%. To understand how our model distinguished between differentiated and non-differentiated NSCs, we evaluated the informative features it learned for the two cell types and found that it had learned several biologically relevant features related to NSC shape during differentiation. We also used our model to examine the differentiation of NSCs over time; the findings confirmed our model's ability to distinguish between non-differentiated and differentiated NSCs. Thus, our model was able to non-invasively and quantitatively identify differentiated NSCs with high accuracy and reproducibility, and, therefore, could be an ideal means of identifying differentiated NSCs in the clinic.

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XitoSBML: A Modeling Tool for Creating Spatial Systems Biology Markup Language Models From Microscopic Images

Kaito Ii, Kota Mashimo, Mitsunori Ozeki, Takahiro G. Yamada, Noriko Hiroi, Akira Funahashi

Frontiers in Genetics (Frontiers Media SA)  10   1027 2019.10

Joint Work, Last author, Corresponding author

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XitoSBML is a software tool designed to create an SBML (Systems Biology Markup Language) Level 3 Version 1 document from microscopic cellular images. It is implemented as an ImageJ plug-in and is designed to create spatial models that reflect the three-dimensional cellular geometry. With XitoSBML, users can perform spatial model simulations based on realistic cellular geometry by using SBML-supported software tools, including simulators such as Virtual Cell and Spatial Simulator. XitoSBML is open-source and is available at https://github.com/spatialsimulator/XitoSBML/. XitoSBML is confirmed to run on most 32/64-bit operating systems: Windows, MacOS, and Linux.

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電気式焦点可変レンズによる三次元計測システムの構築

舟橋 啓, 仲井 祐一郎, 広井 賀子

実験医学 ((株)羊土社)  37 ( 18 ) 3116 - 3120 2019.10

Article, review, commentary, editorial, etc. (scientific journal), Joint Work,  ISSN  0288-5514

Predicting the future direction of cell movement with convolutional neural networks

Shori Nishimoto, Kaiming Li, Yuta Tokuoka, Takahiro G. Yamada, Noriko F. Hiroi, Akira Funahashi

PLOS ONE (PLoS ONE)  14 ( 9 ) e0221245 2019.09

Joint Work, Last author, Corresponding author,  ISSN  1932-6203

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© 2019 Nishimoto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Image-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell movement. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future direction of cell movement from current cell shape, and can be used to automatical

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【病理形態学の新技術】画像解析と深層学習を用いた診断の基礎

舟橋 啓, 徳岡 雄大, 大岡 麻耶, 西本 勝利, 山田 貴大, 広井 賀子

病理と臨床 ((株)文光堂)  37 ( 7 ) 631 - 635 2019.07

Article, review, commentary, editorial, etc. (scientific journal), Joint Work,  ISSN  0287-3745

ネムリユスリカの培養細胞の乾燥耐性に迫る

山田 貴大, 舟橋 啓, 黄川田 隆洋

バイオサイエンスとインダストリー(B&I) ((一般財団法人)バイオインダストリー協会(JBA))  77 ( 4 )  2019.07

Article, review, commentary, editorial, etc. (scientific journal)