PCD-GED: Protein complex detection considering PPI dynamics based on time series gene expression data

Amir Lakizadeh; Saeed Jalili; Sayed-Amir Marashi

doi:10.1016/j.jtbi.2015.04.020

PCD-GED: Protein complex detection considering PPI dynamics based on time series gene expression data

J Theor Biol. 2015 Aug 7:378:31-8. doi: 10.1016/j.jtbi.2015.04.020. Epub 2015 Apr 29.

Authors

Amir Lakizadeh¹, Saeed Jalili², Sayed-Amir Marashi³

Affiliations

¹ Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
² Computer Engineering Department, Tarbiat Modares University, Tehran, Iran. Electronic address: sjalili@modares.ac.ir.
³ Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.

PMID: 25934349
DOI: 10.1016/j.jtbi.2015.04.020

Abstract

Detection of protein complexes from protein-protein interaction (PPI) networks is essential to understand the function of cell machinery. However, available PPIs are static, and cannot reflect the dynamics inherent in real networks. Our method uses time series gene expression data in addition to PPI networks to detect protein complexes. The proposed method generates a series of time-sequenced subnetworks (TSN) according to the time that the interactions are activated. It finds, from each TSN, the protein complexes by employing the weighted clustering coefficient and maximal weighted density concepts. The final set of detected protein complexes are obtained from union of all complexes from different subnetworks. Our findings suggest that by employing these considerations can produce far better results in protein complex detection problem.

Keywords: Clustering coefficient; Protein complex; Protein–protein interaction; Weighted density.

MeSH terms

Algorithms
Animals
Cluster Analysis
Computational Biology / methods
Gene Expression
Protein Binding / physiology*
Protein Interaction Mapping / methods*
Protein Interaction Maps