动态-静态混合的时序蛋白质网络构建方法
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摘要
目前已公开的蛋白质网络多为静态网络,不能有效描述细胞中蛋白质的动态活动特点.通过融合基因表达数据,研究人员可以构建出描述蛋白质动态性的时序蛋白质网络.现有方法假设所有蛋白质都是动态变化的,而事实上除动态蛋白质外细胞中还包含相对稳定的静态蛋白质.为此,提出了一种基于动态-静态蛋白质混合的时序网络构建新方法.该方法根据基因表达变化情况将蛋白质分为动态和静态两类,并在构建各时刻网络时考虑动态与静态蛋白质之间的相互作用关系.实验结果表明,利用本文方法构建的时序蛋白质网络可以提高蛋白质复合体识别的准确性,从而验证了本文方法的可行性.
Public available protein networks at present are static,which could not be used to describe the dynamic characteristics of proteins in a cell effectively. It is necessary to construct temporal protein network by integrating other biological data,which reflects the dynamic activities of proteins. Most of previous methods assume that all proteins are dynamic. However,in addition to dynamic protein,there are many static proteins in the cell. To this end,this paper proposes a new method to construct a temporal protein network both with dynamic and static proteins. In the method,proteins are classified into two types of dynamic and static,and then a protein network is constructed on each time point by both considering the interactions of dynamic and static proteins. Experimental test results show that the temporal protein network constructed by using the proposed method can improve the accuracy of the identification of protein complexes,which verified the reliability of the proposed method.
Public available protein networks at present are static,which could not be used to describe the dynamic characteristics of proteins in a cell effectively. It is necessary to construct temporal protein network by integrating other biological data,which reflects the dynamic activities of proteins. Most of previous methods assume that all proteins are dynamic. However,in addition to dynamic protein,there are many static proteins in the cell. To this end,this paper proposes a new method to construct a temporal protein network both with dynamic and static proteins. In the method,proteins are classified into two types of dynamic and static,and then a protein network is constructed on each time point by both considering the interactions of dynamic and static proteins. Experimental test results show that the temporal protein network constructed by using the proposed method can improve the accuracy of the identification of protein complexes,which verified the reliability of the proposed method.
引文
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[2]SPIRIN V,MIRNY L A.Protein complexes and functional modules in molecular networks[J].Proceedings of the National Academy of Sciences,2003,100(21):12123-12128.
[3]冀俊忠,刘志军,刘红欣,等.蛋白质相互作用网络功能模块检测的研究综述[J].自动化学报,2014,40(4):577-593.JI Junzhong,LIU Zhijun,LIU Hongxin,et al.An overview of research on functional module detection for protein-protein interaction networks[J].Acta Automatica Sinica,2014,40(4):577-593.
[4]鱼亮,高琳,孙鹏岗.蛋白质网络中复合体和功能模块预测算法研究[J].计算机学报,2011,34(7):1239-1251.YU Liang,GAO Lin,SUN Penggang.Research on algorithms for complexes and functional modules prediction in protein-protein interaction networks[J].Chinese Journal of Computers,2011,34(7):1239-1251.
[5]汤希玮,王建新,胡秋玲.蛋白质复合物预测方法分析与比较[J].计算机应用研究,2011,28(10):3611-3614.TANG Xiwei,WANG Jianxin,HU Qiuling.Analysis and compare of methods predicting protein complex[J].Application Research of Computers[J].2011,28(10):3611-3614.
[6]黄海滨,杨路明,王建新,等.基于复合参数的蛋白质网络关键节点识别技术[J].自动化学报,2008,34(11):1388-1395.HUANG Haibin,YANG Luming,WANG Jianxin,et al.Identification technique of essential nodes in protein networks based on combined parameters[J].Acta Automatica Sinica,2008,34(11):1388-1395.
[7]LICHTENBERG U,JENSEN L J,BRUNAK S,et al.Dynamic complex formation during the yeast cell cycle[J].Science,2005,307(5710):724-727.
[8]TANG Xiwei,WANG Jianxin,LIU Binbin,et al.A comparison of the functional modules identified from time course and static PPI network data[J].BMC Bioinformatics,2011,12(1):1-15.
[9]WANG Jianxin,PENG Xiaoqing,PENG Wei,et al.Dynamic protein interaction network construction and applications[J].Proteomics,2014,14(4-5):338-352.
[10]HEGDE S R,MANIMARAN P,MANDE S C.Dynamic changes in protein functional linkage networks revealed by integration with gene expression data[J].PLo S Computational Biology,2008,4(11):e1000237.
[11]WANG Jianxin,PENG Xiaoqing,LI Min,et al.Construction and application of dynamic protein interaction network based on time course gene expression data[J].Proteomics,2013,13(2):301-312.
[12]KOMUROV K,WHITE M.Revealing static and dynamic modular architecture of the eukaryotic protein interaction network[J].Molecular Systems Biology,2007,3(1):110.
[13]JANJIC V,SHARAN R,PRZULJ N.Modelling the Yeast Interactome[J].Scientific Reports,2014,4:4273.
[14]PU S,WONG J,TURNER B,et al.Up-to-date catalogues of yeast protein complexes[J].Nucleic Acids Research,2009,37(3):825-831.
[15]DAI Qiguo,GUO Maozu,GUO Yingjie,et al.A least square method based model for identifying protein complexes in protein-protein interaction network[J].Biomed Research International,2013,2014:720960-720960.
[16]DAI Qiguo,GUO Maozu,LIU Xiaoyan,et al.CPL:Detecting protein complexes by propagating labels on protein-protein interaction network[J].Journal of Computer Science and Technology,2014,29(6):1083-1093.
[17]NEPUSZ T,YU H,PACCANARO A.Detecting overlapping protein complexes in protein-protein interaction networks[J].Nature Methods,2012,9(5):471-472.
[18]TU B P,KUDLICKI A,ROWICKA M,et al.Logic of the yeast metabolic cycle:temporal compart-mentalization of cellular processes[J].Science,2005,310(5751):1152-1158.
[19]STARK C,BREITKREUTZ B J,REGULY T,et al.Bio GRID:a general repository for interaction datasets[J].Nucleic Acids Research,2006,34(suppl 1):D535-D539.
[20]BOYLE E I,WENG S,GOLLUB J,et al.GO:Term Finder—open source software for accessing Gene Ontology information and finding significantly enriched Gene Ontology terms associated with a list of genes[J].Bioinformatics,2004,20(18):3710-3715.