Minimal Physical Model of Crawling and Dividing Cells
| Project/Area Number |
17K17825
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| Research Category |
Grant-in-Aid for Young Scientists (B)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Biological physics/Chemical physics/Soft matter physics
Mathematical physics/Fundamental condensed matter physics
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| Research Institution | Kyoto University |
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Principal Investigator |
Molina John 京都大学, 工学研究科, 助教 (20727581)
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| Project Period (FY) |
2017-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | Crawling Cells / Dividing Cells / Mechanosenstitivity / Contact Inhibition / Machine Learning / Constitutive Relations / Multi-Scale Simulations / Optimal Control / machine learning / constitutive relations / multi-scale simulations / cell migration / cell proliferation / crawling cells / mechanosensitivity / fast-crawling cells / substrate crawling / phase field modeling / Active Matter / Cell Motility |
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| Outline of Final Research Achievements |
In purpose of this study is to clarify how crawling cells respond to signals from their environment. First, a detailed cell model, which accounts for the deformable shape, the propulsion and the adhesions to the substrate, was used to study the cell-specific reorientation on cyclically-stretched substrates. We found that any asymmetry during extension/compression can be used to align the cells parallel/perpendicular to the stretching. As observed experimentally, this response depends strongly on the frequency. Second, a minimal physical model was used to study the collective motion of crawling and dividing cells. We found that local mechanical interactions, which couple to the shape, motility, and division, can explain the large-scale collective motion seen experimentally.
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| Academic Significance and Societal Importance of the Research Achievements |
We have developed physical models that can be used to understand the way in which interactions with the environment determine the behavior of crawling and dividing cells. Our models help explain how external stretching can reorient cells, and how local interactions can give rise to ordered motion.
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Report
(6 results)
Research Products
(64 results)
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[Presentation] Nash Neural Networks2022
Author(s)
John J. Molina (*), Simon K. Schnyder, Matthew S. Turner, Ryoichi Yamamoto
Organizer
American Physical Society (APS) March Meeting 2022
Related Report
Int'l Joint Research
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