BiCAMWI: A Genetic-Based Biclustering Algorithm for Detecting Dynamic Protein Complexes

Abstract

Considering the roles of protein complexes in many biological processes in the cell, detection of protein complexes from available protein-protein interaction (PPI) networks is a key challenge in the post genome era. Despite high dynamicity of cellular systems and dynamic interaction between proteins in a cell, most computational methods have focused on static networks which cannot represent the inherent dynamicity of protein interactions. Recently, some researchers try to exploit the dynamicity of PPI networks by constructing a set of dynamic PPI subnetworks correspondent to each time-point (column) in a gene expression data. However, many genes can participate in multiple biological processes and cellular processes are not necessarily related to every sample, but they might be relevant only for a subset of samples. So, it is more interesting to explore each subnetwork based on a subset of genes and conditions (i.e., biclusters) in a gene expression data. Here, we present a new method, called BiCAMWI to employ dynamicity in detecting protein complexes. The preprocessing phase of the proposed method is based on a novel genetic algorithm that extracts some sets of genes that are co-regulated under some conditions from input gene expression data. Each extracted gene set is called bicluster. In the detection phase of the proposed method, then, based on the biclusters, some dynamic PPI subnetworks are extracted from input static PPI network. Protein complexes are identified by applying a detection method on each dynamic PPI subnetwork and aggregating the results. Experimental results confirm that BiCAMWI effectively models the dynamicity inherent in static PPI networks and achieves significantly better results than state-of-the-art methods. So, we suggest BiCAMWI as a more reliable method for protein complex detection.

It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data

“…Lakizadeh et al integrated time series expression data and static PPI networks to extract dynamic PPI subnetwork and then detected protein complex based on these subnetworks. They concluded that this method could model the dynamicity inherent in static PPI networks [111].…”

Network clustering algorithm for dynamic protein complex detection fused graph embedding

“…These methodologies involve the extraction of dynamic subnetworks through integrating diverse data sources and subsequently achieve dynamic protein complex identification by filtering clustering outcomes from all subnetworks. Among these methods, the BiCAMWI algorithm [5] , based on genetic algorithms, introduces a novel adaptive degree function for dual cluster evaluation. It then extracts the gene set within these dual clusters to form a subnetwork, i.e., a dynamic subnetwork.…”

DPCT: A Dynamic Method for Detecting Protein Complexes From TAP-Aware Weighted PPI Network

“…BiCAMWI (Lakizadeh and Jalili, 2016 ) is one of the methods that uses a genetic algorithm to detect dynamic biclusters from gene expression data. For each detected bicluster, BiCAMWI makes a dynamic subnetwork of PPI and protein complexes are detected from each subnetwork separately by applying CAMWI.…”