Skip to main content

Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Table 1 Various types of disease networks

From: Network spectra for drug-target identification in complex diseases: new guns against old foes

To have a better understanding of several cellular processes in complex diseases, it is helpful to study how various components make up the system. Some of the most common types of disease networks are:
1. Protein - protein interaction (PPI) networks: PPI networks are perturbed, those of normal cells, in disease due to sequence mutations and expression changes. There are a multitude of methods to detect PPI’s both high-throughput methods (e.g. Yeast two-hybrid screening, mass spectrometry, protein microarrays) and bioinformatics (e.g. text mining, machine learning) based approaches. There have also been major efforts to curate the interactions that have been validated individually in the literature into databases. These databases are the Munich Information Center for Protein Sequence (MIPS, (Mewes and et al. 2002)), the Biomolecular Interaction Network Database (BIND, (Bader et al. 2003)), the Database of Interacting Proteins (DIP, (Xenarios and et al. 2000)), the Molecular Interaction database (MINT, (Ceol and et al. 2017)),and the protein Interaction database (IntAct, (Kerrien and et al. 2011)), Biological General Repository for Interaction Datasets (BioGRID, (Chatr-Aryamontri and et al. 2017)) and the Human Protein Reference Database (HPRD, (Keshava and et al. 2009)). Our current knowledge of the PPI’s is both incomplete and noisy.
2. Gene (transcriptional) regulatory networks: These subgroup of biological networks describe how gene expression is controlled and regulated. The regulator can be DNA, RNA, protein, ions and molecular complexes. There are various high-throughput experimental approaches were developed to study regulatory activities such as chromatin immuno-precipitation (ChIP) followed by microarrays (ChIP-chip) and ChIP followed by sequencing (ChIP-seq), synthetic genetic arrays.
3. Metabolic and biochemical networks: Metabolic networks represent the relationships among small biomolecules (metabolites) and the enzymes (proteins) to catalyze a biochemical reaction. These reactions allow an organism to grow, reproduce, respond to the environment and maintain its structure. Bioinformatics databases such as Kyoto Encyclopedia of Genes and Genomes (KEGG, (Kanehisa and Goto 2000)), BioCyc, (Caspi and et al. 2007), the Biochemical Genetic and Genomics knowledge base (BIGG, (Schellenberger et al. 2010)), and The Human Metabolome Database (HMDB, (Wishart and et al. 2012)) contain wide range of the metabolic networks.
4. Genetic interaction networks: Genetic interactions occur when mutations in two or more genes combine to generate an unexpected (undesired) phenotype. These networks represent a functional relationship between different genes, rather than physical one, essentially predicted by DNA sequences or gene expression profiling.
5. Cell signaling networks: These are formed when different cell pathways interact and are detected by a combination of experimental and computational methods. Cell signaling networks are systematically represented by two type resources i.e., pathway databases (Reactome, wikiPathways etc.) and cellular signaling network databases (Signor, SigmaLink) (Croft and et al. 2010; Kelder and et al. 2011; Perfetto and et al. 2015; Fazekas and et al. 2013).
Apart from these mentioned categories of networks, there have been several efforts made to integrate information from various databases to a single place, such biological network databases are termed as meta-databases. For example, STRING (Szklarczyk and et al. 2017), BIANA (Garcia-Garcia and et al. 2010), ConsensusPathDB (Kamburov and et al. 2008), Human Integrated Protein-Protein Interaction Reference (HIPPIE, (Schaefer and et al. 2012)), International Molecular Exchange (IMEX, (Orchard and et al. 2012)), Agile Protein Interactomes DataServer (APID, (Prieto and Javier 2006)) etc.