Understanding the intracellular transport of endosomes using machine learning approach

2020 Fiscal Year Final Research Report

• Project/Area Number: 19K23717
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund
• Review Section: 0701:Biology at molecular to cellular levels, and related fields
• Research Institution: The University of Tokyo
• Principal Investigator: Lee Seohyun 東京大学, 情報基盤センター, 特任研究員 (00847973)
• Project Period (FY): 2019-08-30 – 2021-03-31
• Keywords: 細胞内物質輸送 / 小胞 / 細胞骨格 / 機械学習

Outline of Final Research Achievements

The movement of the intracellular vesicle, which carries important information from the extracellular area, includes essential information for developing and inspecting the mechanism of pharmaceutical delivery. However, because the movement of the vesicle occurs on a nanometer scale which hinders accurate observation and analysis, the pattern of vesicle movement has not yet been clearly understood. In this study, we aimed to elucidate the patterns of intracellular motions of vesicles based on machine learning. Using a supervised learning algorithm, we succeeded in the classification of vesicle transfer among a cytoskeletal network with the physical properties extracted from the interaction between the vesicles and cytoskeletons.

Free Research Field

生物物理学

Academic Significance and Societal Importance of the Research Achievements

本研究の学術的な意義は、今までの伝統的な生物物理学の接近法では分析が難しかった細胞内物質輸送のパターンを、小胞と細胞骨格との相互作用の物理特性に着目し、機械学習アルゴリズムに基づいて解明した最初の接近法である。尚、社会的意義において、本研究で解明された細胞内の物質輸送のパターンは薬物伝達プロセスに関する研究にも繋がるため、今後新薬の開発や検証を促進させる重要な基礎研究になると期待される。