出版物

 

査読付き論文等

引用数100以上は赤字、引用数20以上は太字にしています
(引用数の詳細や論文のダウンロード等はGoogle Scholar Citations から。)

  1. Keating SM, Waltemath D, König M, Zhang F, Dräger A, Chaouiya C, et al. SBML Level 3: an extensible format for the exchange and reuse of biological models. Molecular Systems Biology. 2020;16:e9110. https://doi.org/10.15252/msb.20199110
  2. Watabe M, Yoshimura H, Arjunan SNV, Kaizu K, Takahashi K. Signaling activations through G-protein-coupled-receptor aggregations. Phys Rev E. 2020;102:032413. https://doi.org/10.1103/PhysRevE.102.032413
  3. Arjunan, S. N. V., Miyauchi, A., Iwamoto, K., & Takahashi, K. (2020). Pspatiocyte: A high-performance simulator for intracellular reaction-diffusion systems. BMC Bioinformatics, 21(1), 33. https://doi.org/10.1186/s12859-019-3338-8
  4. Watabe, M., Arjunan, S. N. V., Chew, W. X., Kaizu, K., & Takahashi, K. (2019). Cooperativity transitions driven by higher-order oligomer formations in ligand-induced receptor dimerization. Physical Review E, 100(6), 062407. https://doi.org/10.1103/PhysRevE.100.062407
  5. Watabe, M., Arjunan, S. N. V., Chew, W. X., Kaizu, K., & Takahashi, K. (2019). Simulation of live-cell imaging system reveals hidden uncertainties in cooperative binding measurements. Physical Review E, 100(1), 010402. https://doi.org/10.1103/PhysRevE.100.010402
  6. Chew, W.-X., Kaizu, K., Watabe, M., Muniandy, S. V., Takahashi, K., & Arjunan, S. N. V. (2019). Surface reaction-diffusion kinetics on lattice at the microscopic scale. Physical Review E, 99(4), 042411. https://doi.org/10.1103/PhysRevE.99.042411
  7. Sokolowski, T. R., Paijmans, J., Bossen, L., Miedema, T., Wehrens, M., Becker, N. B., Kaizu, K., Takahashi, K., Dogterom, M., ten Wolde, P. R. (2019). Egfrd in all dimensions. The Journal of Chemical Physics, 150(5), 054108. https://doi.org/10.1063/1.5064867
  8. Itaya H, Yamakawa H, Tomita M, Takahashi K, BriCA Kernel: Cognitive Computing Platform for Large-scale Distributed Memory Environments. The 28th Annual Conference of the Japanese Neural Network Society, Okinawa Japan, 26 October 2018.
  9. Chew, W.-X., Kaizu, K., Watabe, M., Muniandy, S. V., Takahashi, K., & Arjunan, S. N. V. (2018). Reaction-diffusion kinetics on lattice at the microscopic scale. Physical Review E, 98(3), 032418. https://doi.org/10.1103/PhysRevE.98.032418
  10. Hiroshima, M., Pack, C., Kaizu, K., Takahashi, K., Ueda, M., & Sako, Y. (2018). Transient acceleration of epidermal growth factor receptor dynamics produces higher-order signaling clusters. Journal of Molecular Biology, 430(9), 1386–1401. https://doi.org/10.1016/j.jmb.2018.02.018
  11. Magi, S., Iwamoto, K., Yumoto, N., Hiroshima, M., Nagashima, T., Ohki, R., …, Sako, Y., Takahashi, K., Kimura, S., Kholodenko, B. N., Okada-Hatakeyama, M. (2018). Transcriptionally inducible Pleckstrin homology-like domain, family A, member 1, attenuates ErbB receptor activity by inhibiting receptor oligomerization. Journal of Biological Chemistry, 293(6), 2206–2218. https://doi.org/10.1074/jbc.M117.778399
  12. Yamakawa H, Arakawa N, Takahashi K: Reinterpreting the cortical circuit. In Architectures for Generality & Autonomy Workshop at IJCAI-17; 2017.
  13. Imai, R., Nozaki, T., Tani, T., Kaizu, K., Hibino, K., Ide, S., … Maeshima, K. (2017). Density imaging of heterochromatin in live cells using orientation-independent-DIC microscopy. Molecular Biology of the Cell, 28(23), 3349–3359. https://doi.org/10.1091/mbc.e17-06-0359
  14. Robotic Biology Consortium (including Takahashi, K.,), Yachie, N., & Natsume, T. (2017). Robotic crowd biology with Maholo LabDroids. Nature Biotechnology, 35(4), 310–312. https://doi.org/10.1038/nbt.3758
  15. Arjunan, S. N. V., & Takahashi, K. (2017). Multi-algorithm particle simulations with spatiocyte. In D. Kihara (Ed.), Protein Function Prediction (Vol. 1611, pp. 219–236). https://doi.org/10.1007/978-1-4939-7015-5_16
  16. Itaya, K., Takahashi, K., Nakamura, M., Koizumi, M., Arakawa, N., Tomita, M., & Yamakawa, H. (2016). Brica: A modular software platform for whole brain architecture. In A. Hirose, S. Ozawa, K. Doya, K. Ikeda, M. Lee, & D. Liu (Eds.), Neural Information Processing (Vol. 9947, pp. 334–341). https://doi.org/10.1007/978-3-319-46687-3_37
  17. Iwamoto, K., Shindo, Y., & Takahashi, K. (2016). Modeling cellular noise underlying heterogeneous cell responses in the epidermal growth factor signaling pathway. PLOS Computational Biology, 12(11), e1005222. https://doi.org/10.1371/journal.pcbi.1005222
  18. Shindo, Y., Iwamoto, K., Mouri, K., Hibino, K., Tomita, M., Kosako, H., … Takahashi, K. (2016). Conversion of graded phosphorylation into switch-like nuclear translocation via autoregulatory mechanisms in ERK signalling. Nature Communications, 7(1), 10485. https://doi.org/10.1038/ncomms10485
  19. Takahashi, K., Itaya, K., Nakamura, M., Koizumi, M., Arakawa, N., Tomita, M., & Yamakawa, H. (2015). A generic software platform for brain-inspired cognitive computing. Procedia Computer Science, 71, 31–37. https://doi.org/10.1016/j.procs.2015.12.185
  20. Karr, J. R., Takahashi, K., & Funahashi, A. (2015). The principles of whole-cell modeling. Current Opinion in Microbiology, 27, 18–24. https://doi.org/10.1016/j.mib.2015.06.004
  21. Watabe, M., Arjunan, S. N. V., Fukushima, S., Iwamoto, K., Kozuka, J., Matsuoka, S., … Takahashi, K. (2015). A computational framework for bioimaging simulation. PLOS ONE, 10(7), e0130089. https://doi.org/10.1371/journal.pone.0130089
  22. Shimo, H., Arjunan, S. N. V., Machiyama, H., Nishino, T., Suematsu, M., Fujita, H., … Takahashi, K. (2015). Particle simulation of oxidation induced band 3 clustering in human erythrocytes. PLOS Computational Biology, 11(6), e1004210. https://doi.org/10.1371/journal.pcbi.1004210
  23. Feig, M., Harada, R., Mori, T., Yu, I., Takahashi, K., & Sugita, Y. (2015). Complete atomistic model of a bacterial cytoplasm for integrating physics, biochemistry, and systems biology. Journal of Molecular Graphics and Modelling, 58, 1–9. https://doi.org/10.1016/j.jmgm.2015.02.004
  24. Maeshima, K., Kaizu, K., Tamura, S., Nozaki, T., Kokubo, T., & Takahashi, K. (2015). The physical size of transcription factors is key to transcriptional regulation in chromatin domains. Journal of Physics: Condensed Matter, 27(6), 064116. https://doi.org/10.1088/0953-8984/27/6/064116
  25. Nishida, K., Ono, K., Kanaya, S., & Takahashi, K. (2014). KEGGscape: A Cytoscape app for pathway data integration. F1000Research, 3, 144. https://doi.org/10.12688/f1000research.4524.1
  26. Kaizu, K., de Ronde, W., Paijmans, J., Takahashi, K., Tostevin, F., & ten Wolde, P. R. (2014). The berg-purcell limit revisited. Biophysical Journal, 106(4), 976–985. https://doi.org/10.1016/j.bpj.2013.12.030
  27. Hayashi, K., Pack, C. G., Sato, M. K., Mouri, K., Kaizu, K., Takahashi, K., & Okada, Y. (2013). Viscosity and drag force involved in organelle transport: Investigation of the fluctuation dissipation theorem. The European Physical Journal E, 36(12), 136. https://doi.org/10.1140/epje/i2013-13136-6
  28. Nozaki, T., Kaizu, K., Pack, C.-G., Tamura, S., Tani, T., Hihara, S., … Maeshima, K. (2013). Flexible and dynamic nucleosome fiber in living mammalian cells. Nucleus, 4(5), 349–356. https://doi.org/10.4161/nucl.26053
  29. Aoki, K., Takahashi, K., Kaizu, K., & Matsuda, M. (2013). A quantitative model of ERK MAP kinase phosphorylation in crowded media. Scientific Reports, 3(1), 1541. https://doi.org/10.1038/srep01541
  30. Hihara, S., Pack, C.-G., Kaizu, K., Tani, T., Hanafusa, T., Nozaki, T., … Maeshima, K. (2012). Local nucleosome dynamics facilitate chromatin accessibility in living mammalian cells. Cell Reports, 2(6), 1645–1656. https://doi.org/10.1016/j.celrep.2012.11.008
  31. Mugler, A., Bailey, A. G., Takahashi, K., & Rein ten Wolde, P. (2012). Membrane clustering and the role of rebinding in biochemical signaling. Biophysical Journal, 102(5), 1069–1078. https://doi.org/10.1016/j.bpj.2012.02.005
  32. Takahashi, K., Tanase-Nicola, S., & ten Wolde, P. R. (2010). Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proceedings of the National Academy of Sciences, 107(6), 2473–2478. https://doi.org/10.1073/pnas.0906885107
  33. Takahashi, K. (2008). An exact brownian dynamics method for cell simulation. In M. Heiner & A. M. Uhrmacher (Eds.), Computational Methods in Systems Biology (Vol. 5307, pp. 5–6). https://doi.org/10.1007/978-3-540-88562-7_3
  34. Takahashi K: The E-Cell project and challenges in computational systems biology. In Proc NIC workshop. Volume 36; 2007:55–60.
  35. Takahashi, K., Arjunan, S. N. V., & Tomita, M. (2005). Space in systems biology of signaling pathways—Towards intracellular molecular crowding in silico. FEBS Letters, 579(8), 1783–1788. https://doi.org/10.1016/j.febslet.2005.01.072
  36. Sugimoto, M., Takahashi, K., Kitayama, T., Ito, D., & Tomita, M. (2005). Distributed cell biology simulations with e-cell system. In A. Konagaya & K. Satou (Eds.), Grid Computing in Life Science (Vol. 3370, pp. 20–31). https://doi.org/10.1007/978-3-540-32251-1_3
  37. Takahashi, K., Kaizu, K., Hu, B., & Tomita, M. (2004). A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics, 20(4), 538–546. https://doi.org/10.1093/bioinformatics/btg442
  38. Takahashi, K., Ishikawa, N., Sadamoto, Y., Sasamoto, H., Ohta, S., Shiozawa, A., … Tomita, M. (2003). E-Cell 2: Multi-platform E-Cell simulation system. Bioinformatics, 19(13), 1727–1729. https://doi.org/10.1093/bioinformatics/btg221
  39. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., … Wang, J. (2003). The systems biology markup language (Sbml): A medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524–531. https://doi.org/10.1093/bioinformatics/btg015
  40. Takahashi, K., Yugi, K., Hashimoto, K., Yamada, Y., Pickett, C. J. F., & Tomita, M. (2002). Computational challenges in cell simulation: A software engineering approach. IEEE Intelligent Systems, 17(5), 64–71. https://doi.org/10.1109/MIS.2002.1039834
  41. Tomita, M., Hashimoto, K., Takahashi, K., Matsuzaki, Y., Matsushima, R., Saito, K., … Nakayama, Y. (2000). The E-CELL project: Towards integrative simulation of cellular processes. New Generation Computing, 18(1), 1–12. https://doi.org/10.1007/BF03037563
  42. Tomita, M., Hashimoto, K., Takahashi, K., Shimizu, T., Matsuzaki, Y., Miyoshi, F., … Hutchison, C. (1999). E-CELL: Software environment for whole-cell simulation. Bioinformatics, 15(1), 72–84. https://doi.org/10.1093/bioinformatics/15.1.72
  43. Tomita M, Shimizu T, Saito K, Venter JC, Hashimoto K, Matsuzaki Y, Tanida S, Hutchison CA, Takahashi K, Miyoshi F, others: E-CELL: Software environment for whole cell simulation. Genome Informatics 1997, 8:147–155.
  44. Shimizu, T. S., Takahashi, K., & Tomita, M. (1997). CpG distribution patterns in methylated and non-methylated species. Gene, 205(1–2), 103–107. https://doi.org/10.1016/S0378-1119(97)00542-8

その他論文等

  1. 西田孝三, 海津一成, 高橋恒一, 「パスウェイ可視化のためのダッシュボードコンポーネント」, 可視化情報学会誌, vol. 40, no. 156, pp. 8-13, 2020年1月.
  2. 三好康祐, 山川宏, 高橋恒一, 「ESNによる階層的予測誤差モデルを用いた音声Local-Global課題の再現シミュレーション」, 信学技報, vol. 119, no. 328, NC2019-46, pp. 61-65, 2019年12月.
  3. 大澤正彦, 大森隆司, 髙橋恒一, 荒川直哉, 坂井尚行, 上野道彦, 今井倫太, 山川宏, 「AGI開発をナビゲートするための能力マップ」, 2018年度 人工知能学会全国大会(第32回), 鹿児島, 2018年6月.
  4. 高橋恒一, 「将来の機械知性に関するシナリオと分岐点」, 人工知能学会全国大会論文集 2018, 32:1-4.
  5. 山川宏, 荒川直哉, 高橋恒一, 「全脳アーキテクチャに必要な新皮質マスターアルゴリズムの検討」, 人工知能学会全国大会論文集 2017
  6. 渡部匡己, 都築拓, 海津一成, 高橋恒一, 「人工知能による科学研究の加速」, 人工知能学会全国大会論文集 2016, 30:1–4.
  7. 高橋恒一, 板谷琴音, 中村政義, 「認知コンピューティングのための汎用ソフトウエアプラットフォームの設計と開発」, 人工知能学会全国大会論文集 2015, 29:1–4.

著書等

  1. 黒田玲子, 高橋恒一, 中釜斉, 唐津治夢, 武田計測先端知財団, 「ここまで来ました: 右卷き左卷き・AI駆動科学・がん医療の革新」丸善プラネット (2020)
  2. 高橋恒一, 「人類を再発明するために必要なこと」, 人工知能美学芸術展 記録集 (人工知能美学芸術研究会), p108-109 (2019) ISBN978-4-9902903-7-5
  3. 高橋恒一, 「第五の科学 自動化」In AI事典 第3版. 近代科学社 (2019)
  4. 高橋恒一, 「将来の機械知性に関するシナリオと分岐点」, 人工知能, Vol 33, No. 6. (2018).
  5. 高橋恒一, 草刈ミカ, 中ザワヒデキ, 「特集「AIと美学・芸術」にあたって」, 人工知能, Vol 33, No. 6. (2018).
  6. 上野聡, 高橋恒一, 中田秀基, 「特集「AI 計算資源」にあたって」, 人工知能 Vol 33, No. 1. (2018).
  7. 高橋恒一, 渡部匡己, 「現代科学を超えて―AI駆動型科学へ」 実験医学別冊 「あなたのラボにAI(人工知能)×ロボットがやってくる」, Eds. 夏目徹,  羊土社 (2017)
  8. 高橋恒一, 井上智洋, 「特集「AI社会論」にあたって」, 人工知能, Vol 32, No. 5. (2017)
  9. Hibino K, Kaizu K, Takahashi K, and Maeshima K, A Combination Approach Based on Live-Cell Imaging and Computational Modeling to Further Our Understanding of Chromatin, Epigenetics, and the Genome, In Epigenetics and Systems Biology, Eds. Ringrose L, Academic Press  (2017)
  10. 高橋恒一, 「学際的な知見の融合・共創による人工知能技術開発」, 赤門マネジメント・レビュー 15巻12号 (2016)
  11. 海津一成, 高橋恒一, 「分子はどこまで正確に情報を伝えられるのか」, 生物物理, 56(6):324–326. (2016)
  12. 今井亮輔, 海津一成, 野崎慎, 前島一博, 高橋恒一, 「定量的1分子蛍光イメージングと計算機シミュレーションを用いたゲノムダイナミクスの解析」, 生化学, Vol.86 No.2 pp.192-200 (2014)
  13. 岩本一成, 海津一成, 高橋恒一, 「生化学反応ネットワークのシミュレーション」, 生体の科学, Vol.65 No.5 pp.446-447 (2014)
  14. Addy N, and Takahashi K, Foundations of E-Cell Simulation Environment Architecture, In E-Cell System – Basic Concepts and Applications, Eds. Arjunan S.N.V., Dhar P, and Tomita M, Landes Biosciences (2013).
  15. 高橋恒一, 「1分子粒度細胞シミュレーション」 シミュレーション辞典,  Eds. 日本シミュレーション学会 (コロナ社) (2012).
  16. 高橋恒一, 海津一成, 「細胞シミュレーション – 計算機上での細胞機能の再構成」 実験医学増刊 Vol.29 No.7 「細胞を創る・生命システムを創る」,  Eds.上田泰己、竹内昌治 (羊土社) (2011).
  17. 高橋恒一, 「システム生物学」 計算力学シミュレーションハンドブック – 超ペタスケールコンピューティングの描像, Eds. 小柳義夫, 土居範久, 松田卓也, 矢川元基, (丸善) (2009).
  18. Dhar P, Takahashi K, Nakayama Y, and Tomita M, Computer Simulation of the Cell, Encyclopedia of Molecular Cell Biology and Molecular Medicine, Wiley VCH, (2003)