Data assimilation prediction of cell motility processes driven by cooperative molecular information and forces

2022 Fiscal Year Final Research Report

• Project/Area Number: 19K20400
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 62010:Life, health and medical informatics-related
• Research Institution: Fujita Health University (2021-2022); Nara Institute of Science and Technology (2019-2020)
• Principal Investigator: Kunida Katsuyuki 藤田医科大学, 医学部, 講師 (40709888)
• Project Period (FY): 2019-04-01 – 2023-03-31
• Keywords: 細胞運動 / Rho GTPase / 逆相関解析 / 数理モデリング / システム同定

Outline of Final Research Achievements

First, a Motion-Triggered Average (MTA) method was developed to integrate individually measured data from live cell imaging. Based on inverse correlation analysis, we collected the average activity time series for the same movement of the edges. We then identified a mathematical model to convert the activity time series of the three Rho GTPases (Cdc42/Rac1/RhoA) into an edge velocity series. System identification analysis was performed on the regression model derived from biophysical modeling of morphological changes and molecular activities, and revealed that there is a mathematical formula that quantitatively predicts edge velocity from the three Rho GTPase activity levels, which consists of the sum of the activity levels and their time derivatives.

Free Research Field

システム生物学、計算生物学、細胞生物学

Academic Significance and Societal Importance of the Research Achievements

本研究で得られた数理モデルから、細胞の移動方法の普遍的な制御メカニズムが解明される可能性がある。MTAとシステム同定を他の細胞株に適用することで細胞種ごとの移動方法が解明され、メカニズムの違いから新たな生物学的発見が得られることが期待できる。さらにMTAのデータ前処理法は、原因と結果の時系列があれば、ヒトや植物、微生物など様々な生命現象に適用可能であり、複数因子による制御システムの原理解明に繋がる。