Development of a multi-state data analysis theory to capture the changes in cell population dynamics.

2023 Fiscal Year Final Research Report

• Project/Area Number: 22K15073
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 43040:Biophysics-related
• Research Institution: Nagoya University
• Principal Investigator: Shoya Iwanami 名古屋大学, 理学研究科, 講師 (10854565)
• Project Period (FY): 2022-04-01 – 2024-03-31
• Keywords: 数理モデル / 個体群動態 / 細胞分化 / データ解析 / 恒常性の維持 / 造血幹細胞

Outline of Final Research Achievements

Based on the mathematical model of HSC differentiation kinetics and estimated parameters, we investigated the characteristics of HSC differentiation during transplantation experiments. We found that the dependence of specific differentiation pathways in transplantation experiments was associated with HSC aging and long-term reconstructive capacity. This can be discriminated with high accuracy from the data of transplantation experiments and is expected to be a novel indicator for characterizing HSCs. We initiated simulations to explain the differentiation of potential stem cells into a limited number of lineages using a stochastic model. The influence of minority sex in differentiation was investigated and is expected to be expanded to clone tracking based on implementation.

Free Research Field

数理生物学

Academic Significance and Societal Importance of the Research Achievements

恒常的な細胞系譜がどのような履歴を持ち、疾患により系譜構成がどのように変容するかを定量できるようになる。例えば、特定の細胞のクローンが大半を占めるようになり、白血病のような重大な疾患へとつながる、クローナル造血が引き起こされる原因を特定することが期待される。また、動物実験でのデータ解析を発展させ、数理モデルから導出される指標を疾患と対応付けることができれば、早期診断が可能になり、疾患発症を予防することも可能になる。