Interaction network

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

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Abstract: Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Integration of biological networks and gene expression data using Cytoscape

Abstract: Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

BIND: the Biomolecular Interaction Network Database

Abstract: The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.

PID: the Pathway Interaction Database.

Abstract: The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.

Discovering regulatory and signalling circuits in molecular interaction networks.

Abstract: Motivation: In model organisms such as yeast, large databases of protein–protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score. Results: We evaluated our procedure on a small network of 332 genes and 362 interactions and a large network of 4160 genes containing all 7462 protein–protein and protein-DNA interactions in the yeast public databases. In the case of the small network, we identified five significant subnetworks that covered 41 out of 77 (53%) of all significant changes in expression. Both network analyses returned several top-scoring subnetworks with good correspondence to known regulatory mechanisms in the literature. These results demonstrate how large-scale genomic approaches may be used to uncover signalling and regulatory pathways in a systematic, integrative fashion. Availability: The methods presented in this paper are implemented in the Cytoscape software package which is available to the academic community at http://www.