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 th...

Recent research efforts have made available genome-wide, high-throughput protein-protein interaction (PPI) maps for several model organisms. This has enabled the systematic analysis of PPI networks, which has become one of the primary challenges for the system biology community. In this study, we attempt to understand better the topological structure of PPI networks by comparing them against ma...

Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (...

UNLABELLED : We introduce Path2PPI, a new R package to identify protein-protein interaction (PPI) networks for fully sequenced organisms for which nearly none PPI are known. Path2PPI predicts PPI networks based on sets of proteins from well-established model organisms, providing an intuitive visualization and usability. It can be used to combine and transfer information of a certain pathway or ...

Many tools visualize protein-protein interaction (PPI) networks. The tool introduced here, CellMap, adds one crucial novelty by visualizing PPI networks in the context of subcellular localization, i.e. the location in the cell or cellular component in which a PPI happens. Users can upload images of cells and define areas of interest against which PPIs for selected proteins are displayed (by def...

Detecting protein complexes from protein-protein interaction (PPI) networks is a challenging task in computational biology. A vast number of computational methods have been proposed to undertake this task. However, each computational method is developed to capture one aspect of the network. The performance of different methods on the same network can differ substantially, even the same method m...

MOTIVATION Recent advances in technology have dramatically increased the availability of protein-protein interaction (PPI) data and stimulated the development of many methods for improving the systems level understanding the cell. However, those efforts have been significantly hindered by the high level of noise, sparseness and highly skewed degree distribution of PPI networks. Here, we present...

With the advent of high-throughput wet lab technlogies the amount of protein interaction data available publicly has increased substantially, in turn spurring a plethora of computational methods for in silico knowledge discovery from this data. In this paper, we focus on parameterized methods for modeling and solving complex computational problems encountered in such knowledge discovery from pr...

We study the question of detecting Conserved Protein Interaction Modules (CPIMs) in two protein-protein interaction (PPI) networks. A CPIM contains a pair of groups of interacting proteins, one in each PPI network, that share a high degree of evolutionary conservation. We propose a novel algorithm called GraphHopper that analyzes two PPI networks to find CPIMs. GraphHopper keeps the two PPI net...

In the last few years a large amount of protein interaction data has been collected and stored in public databases. The automatic analysis and management of such data can provide valuable information on the evolution of different organisms. To this aim, interaction data can be modelled as graphs where nodes represent cellular components and edges are associated to interactions. Such graphs are ...