Analysis of Protein-Protein Interaction Networks through Computational Approaches

2020 ◽  
Vol 27 (4) ◽  
pp. 265-278 ◽  
Author(s):  
Ying Han ◽  
Liang Cheng ◽  
Weiju Sun
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Networks ◽  
Computational Techniques ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Comprehensive Information ◽  
Protein Interaction Prediction ◽  
Or Gene ◽  
Experimental Findings

The interactions among proteins and genes are extremely important for cellular functions. Molecular interactions at protein or gene levels can be used to construct interaction networks in which the interacting species are categorized based on direct interactions or functional similarities. Compared with the limited experimental techniques, various computational tools make it possible to analyze, filter, and combine the interaction data to get comprehensive information about the biological pathways. By the efficient way of integrating experimental findings in discovering PPIs and computational techniques for prediction, the researchers have been able to gain many valuable data on PPIs, including some advanced databases. Moreover, many useful tools and visualization programs enable the researchers to establish, annotate, and analyze biological networks. We here review and list the computational methods, databases, and tools for protein−protein interaction prediction.

scholarly journals Refining modules to determine functionally significant clusters in molecular networks

2019 ◽  
Author(s):  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Molecular Networks ◽  
Resolution Limit ◽  
Protein Protein Interaction ◽  
Functional Coverage ◽  
Topological Modules ◽  
Protein Protein Interaction Networks

AbstractModule detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. Results were also compared with six existing methods available for clustering biological networks. In conclusion, the proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

Incorporating Network Topology Improves Prediction of Protein Interaction Networks from Transcriptomic Data

2013 ◽  
pp. 203-221
Author(s):  
Peter E. Larsen ◽  
Frank Collart ◽  
Yang Dai
Keyword(s):  
Experimental Data ◽  
Protein Interaction ◽  
Network Topology ◽  
Biological Networks ◽  
Dimensional Space ◽  
Interaction Networks ◽  
Protein Protein Interactions ◽  
Transcriptomic Data ◽  
Protein Protein Interaction ◽  
Ppi Networks

The reconstruction of protein-protein interaction (PPI) networks from high-throughput experimental data is one of the most challenging problems in bioinformatics. These biological networks have specific topologies defined by the functional and evolutionary relationships between the proteins and the physical limitations imposed on proteins interacting in the three-dimensional space. In this paper, the authors propose a novel approach for the identification of potential protein-protein interactions based on the integration of known PPI network topology and transcriptomic data. The proposed method, Function Restricted Value Neighborhood (FRV-N), was used to reconstruct PPI networks using an experimental data set consisting of 170 yeast microarray profiles. The results of this analysis demonstrate that incorporating knowledge of interactome topology improves the ability of transcriptome analysis to reconstruct interaction networks with a high degree of biological relevance.

scholarly journals Dynamic Circadian Protein–Protein Interaction Networks Predict Temporal Organization of Cellular Functions

PLoS Genetics ◽  
2013 ◽  
Vol 9 (3) ◽  
pp. e1003398 ◽  
Author(s):  
Thomas Wallach ◽  
Katja Schellenberg ◽  
Bert Maier ◽  
Ravi Kiran Reddy Kalathur ◽  
Pablo Porras ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Protein Interaction Networks ◽  
Temporal Organization ◽  
Interaction Networks ◽  
Cellular Functions ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Networks

scholarly journals Refining modules to determine functionally significant clusters in molecular networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Rama Kaalia ◽  
Jagath C. Rajapakse
Keyword(s):  
Protein Interaction ◽  
Biological Networks ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Molecular Networks ◽  
Resolution Limit ◽  
Protein Protein Interaction ◽  
Functional Coverage ◽  
Topological Modules ◽  
Protein Protein Interaction Networks

Abstract Background Module detection algorithms relying on modularity maximization suffer from an inherent resolution limit that hinders detection of small topological modules, especially in molecular networks where most biological processes are believed to form small and compact communities. We propose a novel modular refinement approach that helps finding functionally significant modules of molecular networks. Results The module refinement algorithm improves the quality of topological modules in protein-protein interaction networks by finding biologically functionally significant modules. The algorithm is based on the fact that functional modules in biology do not necessarily represent those corresponding to maximum modularity. Larger modules corresponding to maximal modularity are incrementally re-modularized again under specific constraints so that smaller yet topologically and biologically valid modules are recovered. We show improvement in quality and functional coverage of modules using experiments on synthetic and real protein-protein interaction networks. We also compare our results with six existing methods available for clustering biological networks. Conclusion The proposed algorithm finds smaller but functionally relevant modules that are undetected by classical quality maximization approaches for modular detection. The refinement procedure helps to detect more functionally enriched modules in protein-protein interaction networks, which are also more coherent with functionally characterised gene sets.

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