Jin Ruoming, McCallen Scott, Liu Chun-Chi, Xiang Yang, Almaas Eivind, Zhou Xianghong Jasmine
Department of Computer Science, Kent State University, Kent, OH, USA.
Pac Symp Biocomput. 2009:203-14.
Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult task. The reason is that most biological data are static, or only correspond to snapshots of cellular activity. In this study, we explicitly attempt to detangle the temporal complexity of biological networks by using compilations of time-series gene expression profiling data. We define a dynamic network module to be a set of proteins satisfying two conditions: (1) they form a connected component in the protein-protein interaction (PPI) network; and (2) their expression profiles form certain structures in the temporal domain. We develop an efficient mining algorithm to discover dynamic modules in a temporal network. Using yeast as a model system, we demonstrate that the majority of the identified dynamic modules are functionally homogeneous. Additionally, many of them provide insight into the sequential ordering of molecular events in cellular systems. Finally, we note that the applicability of our algorithm is not limited to the study of PPI networks, instead it is generally applicable to the combination of any type of network and time-series data.
尽管系统层面的生物学数据迅速积累，但理解细胞活动的动态本质仍然是一项艰巨的任务。原因在于大多数生物学数据是静态的，或者仅对应于细胞活动的快照。在本研究中，我们明确尝试通过使用时间序列基因表达谱数据的汇编来解开生物网络的时间复杂性。我们将动态网络模块定义为一组满足两个条件的蛋白质：（1）它们在蛋白质 - 蛋白质相互作用（PPI）网络中形成一个连通组件；（2）它们的表达谱在时间域中形成特定结构。我们开发了一种高效的挖掘算法来发现时间网络中的动态模块。以酵母作为模型系统，我们证明大多数已识别的动态模块在功能上是同质的。此外，其中许多模块为细胞系统中分子事件的顺序排列提供了见解。最后，我们指出我们算法的适用性不限于PPI网络的研究，相反，它通常适用于任何类型的网络和时间序列数据的组合。
