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Model-driven Study of Host Cell Multiscale Dynamics in Viral Infection

Research Project

Project/Area Number 22KJ1417
Project/Area Number (Other) 21J22938 (2021-2022)
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeMulti-year Fund (2023)
Single-year Grants (2021-2022)
Section国内
Review Section Basic Section 62010:Life, health and medical informatics-related
Research InstitutionThe Graduate University for Advanced Studies

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)

Project Period (FY) 2023-03-08 – 2024-03-31
Project Status Completed (Fiscal Year 2023)
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)
KeywordsScientific 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
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.

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.

Report

(3 results)
  • 2023 Annual Research Report
  • 2022 Annual Research Report
  • 2021 Annual Research Report
  • Research Products

    (15 results)

All 2024 2023 2022 2021 Other

All Int'l Joint Research (3 results) Journal Article (4 results) (of which Int'l Joint Research: 2 results,  Peer Reviewed: 2 results,  Open Access: 4 results) Presentation (5 results) (of which Int'l Joint Research: 3 results) Remarks (3 results)

  • [Int'l Joint Research] Centrale Nantes/LS2N(フランス)

    • Related Report
      2023 Annual Research Report
  • [Int'l Joint Research] Ecole centrale de Nantes/Laboratory of Digital Sciences of Nantes(フランス)

    • Related Report
      2022 Annual Research Report
  • [Int'l Joint Research] Ecole Centrale de Nantes/Laboratory of Digital Sciences of Nantes(フランス)

    • Related Report
      2021 Annual Research Report
  • [Journal Article] Data-driven and knowledge-based multiscale modeling of viral dynamics2024

    • Author(s)
      Odaka Mitsuhiro
    • Journal Title

      The Graduate University for Advanced Studies, SOKENDAI dissertation

      Volume: -

    • Related Report
      2023 Annual Research Report
    • Open Access
  • [Journal Article] Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specific ICAM1-associated pathways2023

    • Author(s)
      Odaka Mitsuhiro, Magnin Morgan, Inoue Katsumi
    • Journal Title

      Frontiers in Genetics

      Volume: 14

    • DOI

      10.3389/fgene.2023.1250545

    • Related Report
      2023 Annual Research Report
    • Peer Reviewed / Open Access / Int'l Joint Research
  • [Journal Article] Gene Network Inference from Single-Cell Omics Data and Domain Knowledge for Constructing COVID-19-Specific ICAM1-Associated Pathways2022

    • Author(s)
      Mitsuhiro Odaka, Morgan Magnin, and Katsumi Inoue
    • Journal Title

      Research Square

      Volume: -

    • DOI

      10.21203/rs.3.rs-1300133/v1

    • Related Report
      2021 Annual Research Report
    • Open Access / Int'l Joint Research
  • [Journal Article] Modeling Viral Dynamics in SARS-CoV-2 Infection Based on Differential Equations and Numerical Analysis2021

    • Author(s)
      Mitsuhiro Odaka, Katsumi Inoue
    • Journal Title

      Heliyon

      Volume: 7 (10) Issue: 10 Pages: e08207-e08207

    • DOI

      10.1016/j.heliyon.2021.e08207

    • Related Report
      2021 Annual Research Report
    • Peer Reviewed / Open Access
  • [Presentation] 多変量時系列からの因果ネットワーク発見による微分方程式系の学習2023

    • Author(s)
      小髙充弘, モルガンマニャン, 井上克巳
    • Organizer
      2023年度人工知能学会第37回全国大会
    • Related Report
      2023 Annual Research Report 2022 Annual Research Report
  • [Presentation] データ・知識融合型アプローチによる感染ダイナミクスの解明2023

    • Author(s)
      小髙充弘, モルガンマニャン, 井上克巳
    • Organizer
      AIロボット駆動科学シンポジウム2023
    • Related Report
      2023 Annual Research Report
  • [Presentation] Data-Driven and Knowledge-Based Causal Network Discovery for Identifying Differential Equations2023

    • Author(s)
      Mitsuhiro Odaka, Morgan Magnin, and Katsumi Inoue
    • Organizer
      AAAI 2023 Spring Symposium Series (AAAI-SSS23)
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] A Data-Driven and Knowledge-Based Approach to Inferring Temporal Gene Networks for COVID-192022

    • Author(s)
      Mitsuhiro Odaka, Morgan Magnin, and Katsumi Inoue
    • Organizer
      Critical Assessment of Massive Data Analysis (CAMDA)
    • Related Report
      2022 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Exploring Differential Equations for Modeling SARS-CoV-2 Dynamics with Sensitivity and Stability Analysis2022

    • Author(s)
      Mitsuhiro Odaka, Morgan Magnin, and Katsumi Inoue
    • Organizer
      Statistical Methods for Post Genomic Data (SMPGD)
    • Related Report
      2021 Annual Research Report
    • Int'l Joint Research
  • [Remarks] 小髙充弘ウェブサイト

    • URL

      https://mitsuhiro-odaka.github.io/

    • Related Report
      2022 Annual Research Report 2021 Annual Research Report
  • [Remarks] 学習・推論(井上克巳)研究室

    • URL

      https://research.nii.ac.jp/il/

    • Related Report
      2022 Annual Research Report
  • [Remarks] 学習・推論(井上克巳)研究室ウェブサイト

    • URL

      https://research.nii.ac.jp/il

    • Related Report
      2021 Annual Research Report

URL: 

Published: 2021-05-27   Modified: 2024-12-25  

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