Interaction network

About: Interaction network is a research topic. Over the lifetime, 2700 publications have been published within this topic receiving 113372 citations.

Effect of Localization on the Stability of Mutualistic Ecological Networks

Abstract: The relationships between the core-periphery architecture of the species interaction network and the mechanisms ensuring the stability in mutualistic ecological communities are still unclear. In particular, most studies have focused their attention on asymptotic resilience or persistence, neglecting how perturbations propagate through the system. Here we develop a theoretical framework to evaluate the relationship between architecture of the interaction networks and the impact of perturbations by studying localization, a measure describing the ability of the perturbation to propagate through the network. We show that mutualistic ecological communities are localized, and localization reduces perturbation propagation and attenuates its impact on species abundance. Localization depends on the topology of the interaction networks, and it positively correlates with the variance of the weighted degree distribution, a signature of the network topological hetereogenity. Our results provide a different perspective on the interplay between the architecture of interaction networks in mutualistic communities and their stability.

Experimental evaluation of topological-based fitness functions to detect complexes in PPI networks

Abstract: The detection of groups of proteins sharing common biological features is an important research issue, intensively investigated in the last few years, because of the insights it can give in understanding cell behavior. In this paper we present an extensive experimental evaluation campaign aiming at exploring the capability of Genetic Algorithms (GAs) to find clusters in protein-protein interaction networks, when different topological-based fitness functions are employed. A complete experimentation on the yeast protein-protein interaction network, along with a comparative evaluation of the effectiveness in detecting true complexes on the yeast and human networks, reveals GAs as a feasible and competitive computational technique to cope with this problem.

The C-terminal region of E1A: a molecular tool for cellular cartography.

Abstract: The adenovirus E1A proteins function via protein–protein interactions. By making many connections with the cellular protein network, individual modules of this virally encoded hub reprogram numerous aspects of cell function and behavior. Although many of these interactions have been thoroughly studied, those mediated by the C-terminal region of E1A are less well understood. This review focuses on how this region of E1A affects cell cycle progression, apoptosis, senescence, transformation, and conversion of cells to an epithelial state through interactions with CTBP1/2, DYRK1A/B, FOXK1/2, and importin-α. Furthermore, novel potential pathways that the C-terminus of E1A influences through these connections with the cellular interaction network are discussed.

Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks.

Abstract: Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.

2SCENT: an efficient algorithm for enumerating all simple temporal cycles

Abstract: In interaction networks nodes may interact continuously and repeatedly Not only which nodes interact is important, but also the order in which interactions take place and the patterns they form These patterns cannot be captured by solely inspecting the static network of who interacted with whom and how frequently, but also the temporal nature of the network needs to be taken into account In this paper we focus on one such fundamental interaction pattern, namely a temporal cycle Temporal cycles have many applications and appear naturally in communication networks In financial networks, on the other hand, the presence of a temporal cycle could be indicative for certain types of fraud, and in biological networks, feedback loops are a prime example of this pattern type We present 2SCENT, an efficient algorithms to find all temporal cycles in a directed interaction network 2SCENT consist of a non-trivial temporal extension of a seminal algorithm for finding cycles in static graphs, preceded by an efficient candidate root filtering technique which can be based on Bloom filters to reduce the memory footprint We tested 2SCENT on six real-world data sets, showing that it is up to 300 times faster than the only existing competitor and scales up to networks with millions of nodes and hundreds of millions of interactions Results of a qualitative experiment indicate that different interaction networks may have vastly different distributions of temporal cycles, and hence temporal cycles are able to characterize an important aspect of the dynamic behavior in the networks