Research On Algorithm Of Identifying Protein Complexes And Functional Modules On Dynamic Protein-protein Interaction Network

Identifying protein complexes and functional modules in Protein-Protein Interaction(PPI)networks is essential in understanding the organization of cellular systems and predicting the function of proteins.Many computational methods have been developed to discover protein complexes and functional modules in static PPI network.However,PPI network changes over time,the important dynamics within PPI network is overlooked by these methods,in addition,most of them didn't distinguish between protein complexes and functional modules.Therefore,identifying protein complexes and functional modules in dynamic PPI networks(DPN)is rather crucial.In this paper,we propose a Dynamic Core-attachment Algorithm(DCA)to discover protein complexes and a Stable Core and Adaptive Incremental Algorithm(SCAIA)to detect functional modules in DPN.DCA algorithm based on core-attachment assumption,we creatively define the cores of complexes are small and dense subgraphs which frequently active in the DPN,and then we form complexes by adding short-lived attachments to cores.We apply our DCA to the data of S.cerevisiae and result shows that DCA outperforms seven other complex discovery algorithms,moreover,it reveals that our DCA discovers more accurate and biological significant protein complexes.Functional modules identified by SCAIA algorithm in DPN are much different from protein complexes.We define step-modules which represent specific observations of functional module at a given time point.SCAIA first detects step-modules in the first subnetwork,and then adaptively updates the modular structure in other subnetworks,it last identifies functional modules and their evolutionary trends based on the extracted step-modules of each subnetwork.Extensive experimental results show that SCAIA performs significantly better than seven functional module discovery algorithms on discovering precise and stable functional modules.SCAIA can also track the evolutionary process of functional modules over time.