Simulation study of organ/tissue dynamics model based on complex shapes of single cells
| Project/Area Number |
17K00410
|
|---|
| Research Category |
Grant-in-Aid for Scientific Research (C)
|
| Allocation Type | Multi-year Fund |
|---|
| Section | 一般 |
|---|
| Research Field |
Life / Health / Medical informatics
|
|---|
| Research Institution | Akita Prefectural University |
|---|
Principal Investigator |
|
|---|
| Project Period (FY) |
2017-04-01 – 2020-03-31
|
| Project Status |
Completed (Fiscal Year 2019)
|
| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
|
|---|
| Keywords | コンピュータシミュレーション / 細胞・組織・器官 / 発生・分化 / 粘弾性 / データ解析 |
|---|
| Outline of Final Research Achievements |
On the cell vertex model and the bubbly vertex model, various computer viscoelasticity experiments were performed to show differences. Since this is mainly due to the shape change of each cell, it was clarified numerically that the cellular shape changes are reflected in the physical properties at the tissue level. The wing formation mechanics model was expressed by the cell vertex model, and the results showed that the organs significantly changed depending on the wing margin modulus. We experimentally and analytically revealed that the three-dimensional morphological changes of wing organs and the spatiotemporal dynamics of signaling molecules in the fly pupal stage are closely related.
As additional results, we measured and analyzed dynamical information of cell elasticity by AFM. By quantifying 4D microscopic data of epithelial tissue, we developed a program that recognizes the cell nucleus position by deep learning.
|
| Academic Significance and Societal Importance of the Research Achievements |
数理モデルを用いた計算機粘弾性実験により、一細胞形状が組織レベルの物性に反映されることを数値計算上で明らかにした点で、学術的に重要な知見を与えた。また、我々の翅外形力学モデルを一細胞の形状変化をもつものに刷新した。外形力学情報が重要な情報であることを示し、意義深い。シグナル分子の時空間動態と器官形成の研究では、これらが密接に関連することを初めて示した。AFMによって細胞間力学情報に複数種類の距離相関が現れることを示した。深層学習による細胞移動定量化への道を示した。
上記学術的意義に加え、これらの結果は器官・組織の再生技術などへの応用に向けて大きな前進といえ、社会的意義のある成果となった。
|
Report
(4 results)
Research Products
(35 results)
