In mathematics, a network (or a graph) is an abstract representation of a set of objects (or nodes) where some pairs of the objects are connected by links. In biology, the nodes represent molecules and the links between them may include various type of interactions between these molecules.
The interactome is defined as the whole set of molecular interactions in cells. It is usually displayed as a network and the molecular interactions can occur between molecules belonging to different biochemical families (proteins, nucleic acids, lipids, carbohydrates etc.). When spoken in terms of proteomics, interactome refers to the whole protein-protein interaction (PPI) network, or protein interaction network.
miRNA-regulated PPI network
In the NetAge database, a miRNA-regulated interactome is a network whose nodes are genes/proteins and miRNA molecules. The interactions between the nodes include all the physical and genetic interactions reported in the BioGRID database, as well as all the miRNA-gene interactions from the TarBase database.
A node refers to any gene/protein or miRNA molecule from the miRNA-regulated PPI network.
A node’s interacting partners are all the nodes (genes/proteins or miRNA molecules) with which it could interact.
The most elementary characteristic of a node is its degree (or connectivity), that is, the number of partners to which the node is connected.
Interconnectivity is the concept that all parts of a system interact with and rely on one another simply by the fact that they occupy the same system. Of note, a system is difficult or sometimes impossible to analyze through its individual parts considered alone. In systems biology, interconnectivity can be defined as the percent of nodes from a given set that form a continuous network.
A node with a very large number of connections is usually referred as a hub. For example, in the human interactome about 1.5% of all nodes are hubs, their connectivity being higher than 40 and reaching over 200 interacting partners.
A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. That is, the probability that a node has k connections goes for large values of k as P(k) ~ k−γ where γ is a constant, called degree exponent.
Scale-free networks are noteworthy because many empirically observed networks appear to be scale-free, including the world wide web, protein networks, citation networks, and some social networks.
The degree exponent is a constant, γ, which determines the degree distribution in a scale-free network. Scale-free network with degree exponents in the range 2<γ<3, are the most observed networks in biological and non-biological systems, which is much smaller than for random networks.
Network robustness refers to the system’s ability to maintain its integrity and functionality upon external and internal insults. Disabling a substantial number of nodes or interactions will result in the decrease of network functionality and eventually the disintegration of the network. Because of their topological organization, scale-free networks are very robust, that is, they can withstand random node failures.
The longevity-associated genes (LAGs) represent a diverse group of genes, among which are those that predispose to increased lifespan, whereas others cause premature aging. Thus, genetic or other manipulations with LAGs result in life span extension or reduction.
Age-related diseases (ARDs) represent a group of degenerative pathologies strongly associated with advanced age. In humans, the major ARDs (atherosclerosis, cancer, type 2 diabetes, and Alzheimer’s disease) are the main life span limiting factor.
The longevity networks for model organisms are constructed as protein-protein interaction (PPI) networks which include longevity genes with their first-order partners. Except for the yeast longevity network, all other longevity networks for model organisms, as well as for humans, include miRNAs which have validated targets in the PPI network.
A common gene signature (CGS) for two or more networks includes all overlapping genes/proteins. In most cases, a CGS also forms a continuous network.