Determining modular organization of protein interaction networks by maximizing modularity density.
Determining modular organization of protein interaction networks by maximizing modularity density.
复制标题
通过最大化模块化密度来确定蛋白质相互作用网络的模块化组织
DOI：
10.1186/1752-0509-4-s2-s10
复制
发表时间：
2010-09-13
影响因子：
--
通讯作者：
Zhang XS
中科院分区：
文献类型：
--
作者：
Zhang S;Ning XM;Ding C;Zhang XS
背景随着生物网络数据的不断增加，对这些大型网络的结构进行建模和理解是一个具有深远生物学意义的重要问题。细胞功能和生化事件是由生物模块中相互作用的蛋白质组协调进行的。识别蛋白质相互作用网络中的这些模块对于理解这些基本细胞网络的结构和功能非常重要。因此，开发一个有效的计算方法来发现生物moduls.ResultsThe的目的是引入一个新的定量测量模块密度到生物分子网络领域，并开发新的算法，用于检测蛋白质-蛋白质相互作用（PPI）网络中的功能模块。具体来说，我们采用模拟退火（SA），以最大限度地提高模块密度，并评估其效率模拟网络。为了解决SA过程的计算复杂性，我们设计了一个光谱方法优化的指数，并将其应用到酵母PPI network.ConclusionsOur检测模块的分析表明，通过本方法，这些模块中的大多数具有良好的生物意义的蛋白质复合物的背景下。与MCL和模块化方法的比较表明了该方法的有效性。
BackgroundWith ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable.ResultsThe purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network.ConclusionsOur analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method.