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Extracting quantitative relationships between cell motility and molecular activities (Analytical approaches and implications)
http://hdl.handle.net/10061/0002000571
http://hdl.handle.net/10061/0002000571ac38e4a7-270a-451c-b450-788688a99d7a
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2024-09-27 | |||||
| タイトル | ||||||
| タイトル | Extracting quantitative relationships between cell motility and molecular activities (Analytical approaches and implications) | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Motion-triggered average | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Time-series data | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Molecular signaling | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Cell motility | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Quantitative regression model | |||||
| 資源タイプ | ||||||
| 資源タイプ | journal article | |||||
| アクセス権 | ||||||
| アクセス権 | open access | |||||
| 著者 |
作村, 諭一
× 作村, 諭一× 国田, 勝行 |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Despite considerable advancements in biological measurement technologies, capturing the simultaneous temporal changes in various biomolecular concentrations remains a challenge. Overcoming this technical difficulty via data preprocessing could not only clarify the principles of biological functions but also reduce the costs associated with advancing measurement technologies. This review introduces a novel approach to harmonizing heterogeneous time-series data related to molecular signaling and cellular movement. In response to this challenge, we developed and employed a motion-trigger average (MTA) algorithm. The MTA comprehensively screens and averages intracellular molecular activities that coincide with targeted velocity patterns of the moving cell edge. Given that the MTA filters out cell individuality-dependent noise from the data, a straightforward regression equation can correlate edge moving velocities with the molecular activities of various species within the cell. This methodology not only integrates fragmented datasets but also enables the reuse of past data for new analyses. The crux of our discovery is the elucidation of the role that Rho GTPases play in regulating cellular edge dynamics, a finding made possible by adopting the MTA algorithm. Our study suggests that the MTA could become an indispensable tool in data-driven biology, potentially paving the way for considerable insights into dynamic cellular behaviors and the underlying biological principles. | |||||
| 書誌情報 |
en : Journal of Biomechanical Science and Engineering 巻 18, 号 4, 発行日 2023-10-14 |
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| 出版者 | ||||||
| 出版者 | The Japan Society of Mechanical Engineers | |||||
| ISSN | ||||||
| 収録物識別子タイプ | EISSN | |||||
| 収録物識別子 | 1880-9863 | |||||
| 出版者版DOI | ||||||
| 関連タイプ | isReplacedBy | |||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | https://doi.org/10.1299/jbse.23-00336 | |||||
| 権利 | ||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||
| 権利情報 | $00A9 2023 The Japan Society of Mechanical Engineers. This is an open access article under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). | |||||
| 著者版フラグ | ||||||
| 出版タイプ | NA | |||||
