Department of Physics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
BMC Bioinformatics. 2013;14 Suppl 9(Suppl 9):S5. doi: 10.1186/1471-2105-14-S9-S5. Epub 2013 Jun 28.
There has been a growing interest in identifying context-specific active protein-protein interaction (PPI) subnetworks through integration of PPI and time course gene expression data. However the interaction dynamics during the biological process under study has not been sufficiently considered previously.
Here we propose a topology-phase locking (TopoPL) based scoring metric for identifying active PPI subnetworks from time series expression data. First the temporal coordination in gene expression changes is evaluated through phase locking analysis; The results are subsequently integrated with PPI to define an activity score for each PPI subnetwork, based on individual member expression, as well topological characteristics of the PPI network and of the expression temporal coordination network; Lastly, the subnetworks with the top scores in the whole PPI network are identified through simulated annealing search.
Application of TopoPL to simulated data and to the yeast cell cycle data showed that it can more sensitively identify biologically meaningful subnetworks than the method that only utilizes the static PPI topology, or the additive scoring method. Using TopoPL we identified a core subnetwork with 49 genes important to yeast cell cycle. Interestingly, this core contains a protein complex known to be related to arrangement of ribosome subunits that exhibit extremely high gene expression synchronization.
Inclusion of interaction dynamics is important to the identification of relevant gene networks.
通过整合蛋白质-蛋白质相互作用（PPI）和时间过程基因表达数据，识别特定于上下文的活跃蛋白质-蛋白质相互作用（PPI）子网络的兴趣日益浓厚。然而，以前没有充分考虑研究中生物过程的相互作用动态。
在这里，我们提出了一种基于拓扑相位锁定（TopoPL）的评分指标，用于从时间序列表达数据中识别活跃的 PPI 子网。首先，通过相位锁定分析评估基因表达变化的时间协调；随后，将结果与 PPI 相结合，根据单个成员的表达以及 PPI 网络和表达时间协调网络的拓扑特征，为每个 PPI 子网定义一个活动评分；最后，通过模拟退火搜索识别整个 PPI 网络中得分最高的子网。
将 TopoPL 应用于模拟数据和酵母细胞周期数据表明，与仅利用静态 PPI 拓扑或加性评分方法相比，它可以更敏感地识别具有生物学意义的子网。使用 TopoPL，我们鉴定了一个与酵母细胞周期相关的重要核心子网，包含 49 个基因。有趣的是，该核心包含一个已知与核糖体亚基排列有关的蛋白质复合物，核糖体亚基的基因表达具有极高的同步性。
纳入相互作用动态对于识别相关基因网络很重要。
