Background: Proteins dynamically interact with each other to perform their biological functions. The dynamic operations of protein interaction networks (PPI) are also reflected in the dynamic formations of protein complexes. Existing protein complex detection algorithms usually overlook the inherent temporal nature of protein interactions within PPI networks. Systematically analyzing the temporal protein complexes can not only improve the accuracy of protein complex detection, but also strengthen our biological knowledge on the dynamic protein assembly processes for cellular organization. In this study, we propose a novel computational method to predict temporal protein complexes. Particularly, we first construct a series of dynamic PPI networks by joint analysis of time-course gene expression data and protein interaction data. Then a Time Smooth Overlapping Complex Detection model (TS-OCD) has been proposed to detect temporal protein complexes from these dynamic PPI networks. TS-OCD can naturally capture the smoothness of networks between consecutive time points and detect overlapping protein complexes at each time point. Finally, a nonnegative matrix factorization based algorithm is introduced to merge those very similar temporal complexes across different time points. Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques. According to their life time, PPIs could be classified into stable or transient PPIs [15, 16] . Stable PPIs which are important in maintaining the cell fitness and stability are usually permanent and irreversible. Meanwhile, transient PPIs can associate and dissociate temporarily, and thus they provide a mechanism for the cell to quickly respond to extracellular stimuli. As physical interactions determined by popular high-throughput technologies, e.g. yeast two-hybrid (Y2H) and Tandem Affinity Purification with mass spectrometry (TAP-MS) lack of temporal information, majority of existing complex detection methods treat the PPI network as a static network that can not be used to detect temporal protein complexes. In reality, however, cellular systems are highly dynamic and responsive to environmental cues [17] . The real PPI network in cell keeps changing over different stages of the cell cycle [18] , leading to multiple dynamic protein interaction networks. As such, it
Literature REF propose a novel technique to detect temporal protein complexes from the dynamic PPI networks.
Detecting temporal protein complexes from dynamic protein-protein interaction networks
BMC Bioinformatics
BMC Bioinformatics
Biology Medicine Computer Science