Biological network is any network that applies to biological systems. A network is any system with sub-units that are linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical analysis of connections found in ecological, evolutionary, and physiological studies, such as neural networks.[1]
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Researchers can best represent and analyze the essence of biology’s layers as computable networks. For example, a protein can be modelled as a network of amino acids with nodes and edges. Amino acids can be represented as a network of atoms such as carbon, nitrogen and oxygen.
As early as 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 (Searls, 1993). Recent complex systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter, Bianconi and A.L. Barabási, 2001).
Bioinformatics truly shifted its focus from individual genes, proteins, structures and search algorithms to large-scale networks often denoted as -omes such as biome, interactome, genome and proteome. Now biologists are finding the links between the Internet and metabolic pathways, structural interactions of proteins via a network topology or scale-free network.
Protein-protein interactions (PPIs) are the most intensely analyzed networks in biology. There are over 130 PPI detection methods. Y2H is a commonly used experimental technique for binary interactions. For more complex interactions mass spectrometric techniques and extended graph theory are used.[2]
DNA-protein interactions, as in transcription binding, is a research topic to study the binding of proteins to DNA. Recent technology includes Chip-chip, Chip-seq and Clip-seq.