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The Plant Cell:拟南芥分子相互作用网络预测数据库及基于网络拓扑结构的系统生物学分析方法

来源:浙江大学 2011-04-06 11:59

2011年3月25日,《植物细胞》 The Plant Cell 在线发表了生命科学学院陈新课题组的原创性研究成果:拟南芥分子相互作用网络预测数据库及基于网络拓扑结构的系统生物学分析方法。通过整合分子相互作用的多种侧面证据,陈新课题组建立了目前最为全面的拟南芥分子相互作用网络,可覆盖24%的所有可能相互作用,单个预测相互作用的可靠性大于40%。进一步分析表明,尽管这一组预测相互作用的覆盖面仍然有限,但它们已经能够反映很多高级生命系统(生物途径和生物过程)之间的关联,可以支持生物途径交互关系分析、基因功能预测、寻找表达变化不显著的关键调控基因等多种重要的系统生物学分析。很多在该预测网络发布后新发表的生物学关联可以从这一预测网络的拓扑结构中发现。上述工作完全由陈新课题组在国内实验室完成。

陈新课题组的核心研究方向是在分子相互作用网络的层面上研究药物多靶标协同调控的理论。目前正在进行人类分子相互作用网络的重建,以分析已知有效的多成分药物协同作用产生治疗效果的分子机制。(生物谷Bioon.com)

生物谷推荐原文出处:

The Plant Cell   doi: 10.1105/tpc.110.082529

The Predicted Arabidopsis Interactome Resource and Network Topology-Based Systems Biology Analyses.

Lin M, Zhou X, Shen X, Mao C, Chen X.

State Key Laboratory of Plant Physiology and Biochemistry, Zhejiang University, Hangzhou 310058, People's Republic of China.

Abstract

Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the Predicted Arabidopsis Interactome Resource (PAIR;http://www.cls.zju.edu.cn/pair/) presents 149,900 potential molecular interactions, which are expected to cover ~24% of the entire interactome with ~40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks.

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