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Detecting temporal protein complexes from dynamic protein-protein interaction networks.

Ou-Yang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P - BMC Bioinformatics (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.

ABSTRACT

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.

Results: 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.

Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Show MeSH
Schematic overview of the algorithm. TS-OCD consists of two stages. First, it constructs dynamic PPI networks by integrating physical protein interaction data and time-course gene expression data. Second, it detects temporal protein complexes from the constructed dynamic PPI networks.
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Fig1: Schematic overview of the algorithm. TS-OCD consists of two stages. First, it constructs dynamic PPI networks by integrating physical protein interaction data and time-course gene expression data. Second, it detects temporal protein complexes from the constructed dynamic PPI networks.

Mentions: where, and denotes the set of nonnegative real numbers. The model is solved by DTU:Toolbox[37] via multiplicative update method[34]. After calculating the solutions and, we need to infer the group relationship of each complex l from. Here, complex l is assigned to a group z if. Finally, we merge complexes within same groups and obtain the final set of predicted complexes. The flow-chart of our proposed algorithm, including 2 key steps, namely, constructing dynamic PPI networks, and detecting temporal protein complexes, is shown in Figure1.Figure 1


Detecting temporal protein complexes from dynamic protein-protein interaction networks.

Ou-Yang L, Dai DQ, Li XL, Wu M, Zhang XF, Yang P - BMC Bioinformatics (2014)

Schematic overview of the algorithm. TS-OCD consists of two stages. First, it constructs dynamic PPI networks by integrating physical protein interaction data and time-course gene expression data. Second, it detects temporal protein complexes from the constructed dynamic PPI networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4288635&req=5

Fig1: Schematic overview of the algorithm. TS-OCD consists of two stages. First, it constructs dynamic PPI networks by integrating physical protein interaction data and time-course gene expression data. Second, it detects temporal protein complexes from the constructed dynamic PPI networks.
Mentions: where, and denotes the set of nonnegative real numbers. The model is solved by DTU:Toolbox[37] via multiplicative update method[34]. After calculating the solutions and, we need to infer the group relationship of each complex l from. Here, complex l is assigned to a group z if. Finally, we merge complexes within same groups and obtain the final set of predicted complexes. The flow-chart of our proposed algorithm, including 2 key steps, namely, constructing dynamic PPI networks, and detecting temporal protein complexes, is shown in Figure1.Figure 1

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou 510275, China. stsddq@mail.sysu.edu.cn.

ABSTRACT

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.

Results: 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.

Conclusions: Extensive experimental results demonstrate the proposed method is very effective in detecting temporal protein complexes than the state-of-the-art complex detection techniques.

Show MeSH