| Project/Area Number |
22K14012
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 13040:Biophysics, chemical physics and soft matter physics-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Schnyder Simon 東京大学, 生産技術研究所, 特任助教 (50812616)
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| Project Period (FY) |
2022-04-01 – 2025-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2024: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2023: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2022: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | simulation / collective behavior / control theory / cell cycle / active matter / complex systems |
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| Outline of Research at the Start |
With this project, we aim to advance our understanding of non-volume conserving fluids by studying cell tissue dynamics. We will systematically extend our recently developed hybrid mechanochemical model in three directions: (1) cell motility, (2) apoptosis and (3) friction stemming from hydrodynamic flows. From this this we hope to gain insights into embryogenesis and tumor growth, in particular. We hope to compare our results to those obtained from mechanical models without cell cycle and with experimental data.
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| Outline of Annual Research Achievements |
I worked on simulations of tissues, as outlined in the proposal. This research has not yet lead to publications and is described in “Current Status”.
In addition, in order to maximize the impact of this research project, I studied the dynamics of a complex system of interacting agents in a complementary way.
Hamiltonian dynamics can be interpreted as extremising an action. This has analogies to optimal control theory where the aim is to find a control function which optimizes an objective function. Hoping to exploit these analogies, I completed a study on control theory in a different system, which still shared crucial features with the original idea: Many identical agents are interacting with each other, each behaving as if striving to optimize a utility function.
In a model for epidemics, I studied the social distancing behavior of individuals, each seeking to optimize their own objective function, while reacting to the epidemic. The model predicts that the collective behavior reaches a Nash equilibrium in which no individual can change their behavior without negatively impacting their future. Such a population won’t reach the optimum of the objective function due to a lack of cooperation. We developed a novel approach for designing governmental policy interventions that allow the population to reach the optimum of the objective function even without cooperation. This will not only have an impact on policy design during epidemics but I expect that it can be applied to other self-organizing systems such as optimally controlling collections of cells via external fields.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
I am working on two collaborations:
(1) The first collaboration investigates the cell-size-dependent growth of MDCK colonies and seeks to establish the role of cell cycle regulation. This is a cooperation with an experimental lab which have run extensive experiments which are complimented by my simulations. MDCK colonies are grown in circular stencils to a variety of densities and then released by removing the stencil. These experiments are able to track cell mechanics, cell size, and proxies for the cell cycle state over the duration of a few days in which the cells have time to divide multiple times. We have found a near quantitative fit between experiments and simulations. At this moment we are beginning to write a manuscript.
(2) The second collaboration is concerned with understanding the collective dynamics which arise when tissue formation involves multiple cell types. In particular, I have performed simulations of two cell types which interact non-reciprocally. The goal is to use these simulations to interpret experiments of Dictyostelium discoideum. In addition, we are working on a machine learning approach intended to directly learn interactions between cells of different cells types with each other. The latter is currently in the manuscript stage.
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| Strategy for Future Research Activity |
I intend to bring the above mentioned two projects to completion. We are currently working on two manuscripts.
I hope to develop the preliminary simulations on Dictyostelium discoideum further, with the goal of understanding the non-reciprocal interactions between cell types and their role in organizing the formation of a structurally complex tissue.
In addition, I hope that the collaborations mentioned above can be continued to probe the relationship between cell mechanics and cell cycle regulation even more deeply. This could possibly include analyzing cell colonies in experiments on a per cell basis to reveal principles of structural organisation which could then be compared to my model in detail. In particular, this will include a careful investigation of cell motility and cell-cell adhesion.
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