| Project/Area Number |
18K11535
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | Toho University |
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Principal Investigator |
Nacher Jose 東邦大学, 理学部, 教授 (60452984)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2021: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2020: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2019: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 制御性 / 情報解析 / 代謝経路 / タンパク質相互作用ネットワーク / 遺伝子発現データ / 支配集合 / 複雑生物情報ネットワーク / 可制御性 / たんぱく質相互作用ネットワーク / 最大マッチング / ネットワークダイナミクス |
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| Outline of Final Research Achievements |
In this research, we examined different types of biological data, from metabolic fluxes to gene expression profiles and protein interaction networks, and proposed several control methods and algorithms to study controllability in biological systems, specially those that are transitioning or evolving from one state to another. The integration of metabolic flux correlation with control theory algorithms captured new insights on the dynamic transition from healthy to cancer states. On the other hand, ageing process was investigated using a new controllability model that analysed dynamically generated probabilistic protein networks across lifespan by integrating gene expression profiles. Moreover, viral infection that involves the transition from uninfected to infected cells was also studied using two independent controllability models. A multi-layer network control model as well as other models using different control methods such as maximum matching were also proposed.
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| Academic Significance and Societal Importance of the Research Achievements |
複雑なシステムの完全制御の実現は、生物学的、社会的に強い意味を持つかもしれない。車をコントロールできるのは、車のシステムを完全に理解しているからである。同じ原理が、複雑な生物学的プロセスやヒトの疾患にも適用されるはずである。したがって、提案するアルゴリズムと計算手法は、複雑な生物学的システムの知識と制御を強化するのに役立つと信じている。いくつかの論文や学会での発表に加え、制御やネットワーク、複雑系に関する深い知識を持たない社会人にも理解できるよう、主な研究成果を書籍にまとめた。
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