2021 Fiscal Year Research-status Report
複雑生物情報ネットワークにおけるダイナミクスと制御性の統合情報解析
| Project/Area Number |
18K11535
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| Research Institution | Toho University |
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Principal Investigator |
ホセ ナチェル 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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| Keywords | 可制御性 / 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 / 最大マッチング / 複雑生物情報ネットワーク |
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| Outline of Annual Research Achievements |
Network controllability offers a promising theoretical framework to investigate and achieve control of complex biological systems by identifying a relatively small number of molecules.
In this research, we have analysed and integrated several types of biological data, from gene expression profiles and metabolic fluxes to protein interaction networks and metabolic pathways, and proposed efficient algorithms to investigate controllability in biological systems, especially when transitioning between different states.
A probabilistic control model analysed the transition between healthy to cancer states using metabolic fluxes and determined how differently the controllers are in each state. Moreover, we proposed an algorithm that efficiently determines those nodes engaged in control that always appear in all possible solutions. We also generated protein networks dynamically by using extensive datasets of gene expression profiles that correspond to the brains of individuals at different ages. This allowed us to investigate human aging process from a dynamic control perspective. A new model to address directed biological networks such as signaling pathways is also being developed. Moreover, the importance of intermittent nodes, that partially exert control on the system, is also being investigated using a new algorithmic approach. Controllability frameworks that use maximum matching approach have also been explored and a new algorithm was proposed to detect the critical driven nodes. The data analysis showed the biological importance of driven nodes in metabolic pathways.
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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
The analysis of the dynamical transition between healthy and cancer states in several tissues using metabolic fluxes unveiled that the required number of driver nodes seems to be smaller in the disease state. This suggested that the cancer metabolism consists of a more streamlined flux distribution. Moreover, in order to be able to identify those controllers that appear in all possible solutions we proposed a new algorithm and applied it to dynamically constructed protein interaction networks. These networks were constructed using gene expression profiles obtained from the human brain at different ages. This dynamic generation of networks via multiple gene expression data is interesting because it allows us to apply the developed algorithm to networks that actually have different structure, which changes according to human age. This allowed us to identify specific proteins and genes that are involved in critical control at specific ages. This also showed the similarities and differences between females and males. To address directed networks using a probabilistic control framework, we have developed a new algorithm which is being applied to signal pathways as well as other biological systems. We also developed a new algorithm to address the importance of intermittent nodes which is also being applied to directed networks. By using the maximum matching approach we also identified critical driven roles and demonstrated their biological importance in metabolic pathways. Further analyses of this framework are also being considered to analyse related specific control features.
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| Strategy for Future Research Activity |
While the proposed probabilistic control algorithm can identify critical nodes in dynamically constructed networks, modifications and variants are needed to be done to address directionality in biological networks and pathways. We have been developing new algorithms that can identify critical driver nodes in directed biological pathways such as signaling pathways, metabolic networks and cytokine interaction networks among others. The importance of the nodes that partially exert control, namely intermittent nodes, is also being investigated by using a newly developed algorithm. The large-scale computer simulations of artificially constructed networks as well as the preliminary data analysis are showing promising results on the significant associations between driver nodes and biological features. Finally, investigation of related controllability features using complementary and alternative controllability frameworks are also being considered in our computational experiments.
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| Causes of Carryover |
今年も出張する予定でしたが、コロナの関係で行けませんでした。今後執筆予定の複数の新しい論文の出版料及び別刷り料が必要となる可能性があります。パンデミックの状況による国内発表・出張なども行う可能性あります。また、コンピュータシミュレーションを行うため、コンピュータ・ワークステーションを購入する必要がある可能性があります。
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