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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Biological network | 1/5 | https://en.wikipedia.org/wiki/Biological_network | reference | science, encyclopedia | 2026-05-05T14:01:40.853976+00:00 | kb-cron |
A biological network is a method of representing systems as complex sets of binary interactions or relations between various biological entities. In general, networks or graphs are used to capture relationships between entities or objects. A network can be represented as an N×N matrix where N is the number of nodes, and who's entries tell you if two nodes share an edge. Typically, this matrix will have entries of 0 or 1 with 1 denoting an edge. We can optionally use a weighted graph by assigning a weight to each edge to show how relevant the connection between two nodes is.
== History of networks ==
As early as 1736 Leonhard Euler analyzed a real-world issue known as the Seven Bridges of Königsberg, which established the foundation of graph theory. From the 1930s-1950s the study of random graphs were developed. During the mid 1990s, it was discovered that many different types of "real" networks have structural properties quite different from random networks. In the late 2000s, scale-free and small-world networks began shaping the emergence of systems biology, network biology, and network medicine. In 2014, graph theoretical methods were used by Frank Emmert-Streib to analyze biological networks. In the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states represented as a finite-state machine. Recent complex systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics.
== Networks in biology ==
=== Protein–protein interaction networks ===
Protein-protein interaction networks (PINs) represent the physical relationship among proteins present in a cell, where proteins are nodes, and their interactions are undirected edges. Due to their undirected nature, it is difficult to identify all the proteins involved in an interaction. Protein–protein interactions (PPIs) are essential to the cellular processes and also the most intensely analyzed networks in biology. PPIs could be discovered by various experimental techniques, among which the yeast two-hybrid system is a commonly used technique for the study of binary interactions. Recently, high-throughput studies using mass spectrometry have identified large sets of protein interactions. Many international efforts have resulted in databases that catalog experimentally determined protein-protein interactions. Some of them are the Human Protein Reference Database, Database of Interacting Proteins, the Molecular Interaction Database (MINT), IntAct, and BioGRID. At the same time, multiple computational approaches have been proposed to predict interactions. FunCoup and STRING are examples of such databases, where protein-protein interactions inferred from multiple evidences are gathered and made available for public usage. Recent studies have indicated the conservation of molecular networks through deep evolutionary time. Moreover, it has been discovered that proteins with high degrees of connectedness are more likely to be essential for survival than proteins with lesser degrees. This observation suggests that the overall composition of the network (not simply interactions between protein pairs) is vital for an organism's overall functioning.
=== Gene regulatory networks (DNA–protein interaction networks) ===
The genome encodes thousands of genes whose products (mRNAs, proteins) are crucial to the various processes of life, such as cell differentiation, cell survival, and metabolism. Genes produce such products through a process called transcription, which is regulated by a class of proteins called transcription factors. For instance, the human genome encodes almost 1,500 DNA-binding transcription factors that regulate the expression of more than 20,000 human genes. The complete set of gene products and the interactions among them constitutes gene regulatory networks (GRN). GRNs regulate the levels of gene products within the cell and in-turn the cellular processes. GRNs are represented with genes and transcriptional factors as nodes and the relationship between them as edges. These edges are directional, representing the regulatory relationship between the two ends of the edge. For example, the directed edge from gene A to gene B indicates that A regulates the expression of B. Thus, these directional edges can not only represent the promotion of gene regulation but also its inhibition. GRNs are usually constructed by utilizing the gene regulation knowledge available from databases such as., Reactome and KEGG. High-throughput measurement technologies, such as microarray, RNA-Seq, ChIP-chip, and ChIP-seq, enabled the accumulation of large-scale transcriptomics data, which could help in understanding the complex gene regulation patterns.
=== Gene co-expression networks (transcript–transcript association networks) ===
Gene co-expression networks can be perceived as association networks between variables that measure transcript abundances. These networks have been used to provide a system biologic analysis of DNA microarray data, RNA-seq data, miRNA data, etc. weighted gene co-expression network analysis is extensively used to identify co-expression modules and intramodular hub genes. Co-expression modules may correspond to cell types or pathways, while highly connected intramodular hubs can be interpreted as representatives of their respective modules.
=== DNA-DNA chromatin networks ===