Model-driven Study of Host Cell Multiscale Dynamics in Viral Infection
| Project/Area Number |
22KJ1417
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| Project/Area Number (Other) |
21J22938 (2021-2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2021-2022) |
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| Section | 国内 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | The Graduate University for Advanced Studies |
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Principal Investigator |
小高 充弘 総合研究大学院大学, 複合科学研究科, 特別研究員(DC1)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 2023: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2022: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2021: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Scientific discovery / DD-KB approach / Multiscale modeling / Viral dynamics / COVID-19 / 方程式発見 / 因果ネットワーク / 深層学習 / データ・知識融合型アプローチ / 遺伝子ネットワーク推定 / オミクス解析 / モデル妥当性確認 / 新型コロナウイルス感染症 / 微分方程式系 |
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| Outline of Research at the Start |
The doctoral researcher develops and applies fundamental technologies for discovery science based on physics simulation and artificial intelligence. In particular, learning and inference methods for causal networks and differential equations will be improved based on a data-driven and knowledge-based approach. Deep learning techniques for inferring causal models among multivariate time series will be applied in a scalable manner. To map causal models to differential equations, reachability and attractors of dynamical behavior and topological properties of causal networks will be evaluated.
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| Outline of Annual Research Achievements |
Throughout the DC1 term, we conducted informatics research on the global issue of COVID-19: discovering unknown knowledge as a hypothesis. The project's overall strategy was to spin a loop for scientific discovery consisting of three reasoning processes: deduction, induction, and abduction. Two different scales of studies were mapped on this loop. Specifically, one study found a hypothesis on viral dynamics on a macroscopic scale (Study A), and the other verified such a hypothesis from a microscopic scale (Study B).
In Study A, we built equation-based viral dynamics models, conducted numerical analyses, and then fitted the models with viral load data from mild and severe cases to estimate parameters. The results suggested that the assumed effect of viral cell-to-cell transmission is associated with the severity of COVID-19.
In Study B, proceeded this academic year, we proposed a Data-Driven and Knowledge-Based (DD-KB) approach that validates the hypothesis with data and knowledge. This approach was applied to large-scale gene expression data and seven knowledge bases. As a result of the spatiotemporal analysis and integration of the constructed signaling pathways, existing pathways were reproduced, and unknown ones were discovered. Additionally, we attempted to improve the gene network inference method with causal discovery.
Overall, the above studies found and verified the hypothesis of a within-host mechanism of COVID-19. Furthermore, the DD-KB framework remains applicable not only to COVID-19 but also to other possible emerging infectious diseases in the future.
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Report
(3 results)
Research Products
(15 results)
