Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.

Interactome networks and human disease

A proteome-scale map of the human interactome network

The Biological General Repository for Interaction Datasets (BioGRID: http//thebiogrid.org) is an open access archive of genetic and protein interactions that are curated from the primary biomedical literature for all major model organism species. As of September 2012, BioGRID houses more than 500 000 manually annotated interactions from more than 30 model organisms. BioGRID maintains complete curation coverage of the literature for the budding yeast Saccharomyces cerevisiae, the fission yeast Schizosaccharomyces pombe and the model plant Arabidopsis thaliana. A number of themed curation projects in areas of biomedical importance are also supported. BioGRID has established collaborations and/or shares data records for the annotation of interactions and phenotypes with most major model organism databases, including Saccharomyces Genome Database, PomBase, WormBase, FlyBase and The Arabidopsis Information Resource. BioGRID also actively engages with the text-mining community to benchmark and deploy automated tools to expedite curation workflows. BioGRID data are freely accessible through both a user-defined interactive interface and in batch downloads in a wide variety of formats, including PSI-MI2.5 and tab-delimited files. BioGRID records can also be interrogated and analyzed with a series of new bioinformatics tools, which include a post-translational modification viewer, a graphical viewer, a REST service and a Cytoscape plugin.

https://www.pure.ed.ac.uk/ws/files/8144915/NAR_2013_Tyres_M.pdf

The BioGRID interaction database: 2013 update

The Biological General Repository for Interaction Datasets (BioGRID) is a public database that archives and disseminates genetic and protein
 interaction data from model organisms and humans
 (http://www.thebiogrid.org). BioGRID currently holds 347 966
 interactions (170 162 genetic, 177 804 protein) curated from both
 high-throughput data sets and individual focused studies, as derived
 from over 23 000 publications in the primary literature. Complete
 coverage of the entire literature is maintained for budding yeast
 (Saccharomyces cerevisiae), fission yeast (Schizosaccharomyces pombe)
 and thale cress (Arabidopsis thaliana), and efforts to expand curation
 across multiple metazoan species are underway. The BioGRID houses 48
 831 human protein interactions that have been curated from 10 247
 publications. Current curation drives are focused on particular areas
 of biology to enable insights into conserved networks and pathways that
 are relevant to human health. The BioGRID 3.0 web interface contains
 new search and display features that enable rapid queries across
 multiple data types and sources. An automated Interaction Management
 System (IMS) is used to prioritize, coordinate and track curation
 across international sites and projects. BioGRID provides interaction
 data to several model organism databases, resources such as Entrez-Gene
 and other interaction meta-databases. The entire BioGRID 3.0 data
 collection may be downloaded in multiple file formats, including PSI MI
 XML. Source code for BioGRID 3.0 is freely available without any
 restrictions.

/pdf/the-biogrid-interaction-database-2011-update-4t23jtqdsl.pdf

The BioGRID Interaction Database: 2011 update

https://www.linkgroup.hu/docs/13PharmTher.pdf

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual geno

MA Calderwood

Papers

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins - eScholarship