Computational framework to infer potential novel drug-targets based on network analysis and comparative genomic analysis

2016 Fiscal Year Final Research Report

• Project/Area Number: 25870197
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: System genome science; Life / Health / Medical informatics
• Research Institution: Institute of Physical and Chemical Research (2015-2016); Tokyo Medical and Dental University (2013-2014)
• Principal Investigator: Hase Takeshi 国立研究開発法人理化学研究所, 統合生命医科学研究センター, 客員研究員 (70569285)
• Project Period (FY): 2013-04-01 – 2017-03-31
• Keywords: 生命情報 / システムバイオロジー / ネットワークバイオロジー / 機械学習 / タンパク質ネットワーク / 比較ゲノム / 薬剤標的分子 / ネットワーク進化

Outline of Final Research Achievements

Protein-protein interaction networks are useful resources for investigating gene functions and potential drug targets. In this project, we mainly conducted two studies. ①By using genome-wide data of protein-protein interactions, we developed a computational framework to infer potential drug-target genes. Then, we used the framework and inferred potential novel therapeutic targets for Alzheimer's disease, rheumatoid arthritis, and several cancerous diseases. ②We investigated differences and similarities in the structure of protein-protein interaction networks among various species. In order to investigate potential evolutionary mechanisms that can explain the differences and similarities, we conducted comparative genome analyses and computer simulations that use network growth model based on gene duplications.

Free Research Field

情報学