Biological network

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A 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]

1 Network biology and bioinformatics
2 Networks in biology
3 See also
4 References
5 Books
6 External links

Network biology and bioinformatics [edit]

Complex biological systems may be represented and analyzed as computable networks. For example, ecosystems can be modeled as networks of interacting species or a protein can be modeled as a network of amino acids. Breaking a protein down farther, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the basic components of a network. Nodes represent units in the network, while edges represent the interactions between the units. Nodes can represent a wide-array of biological units, from individual organisms to individual neurons in the brain. Two important properties of a network are degree and betweenness. Degree (or connectivity) is the number of edges that connect a node, while betweenness is a measure of how central a node is in a network. ^[2] Nodes with high betweenness essentially serve as bridges between different portions of the network (i.e. interactions must pass through this node to reach other portions of the network). In social networks, nodes with high degree or high betweenness may play important roles in the overall composition of a network.

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. ^[3] 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. ^[4]

Bioinformatics has increasingly shifted its focus from individual genes, proteins, and search algorithms to large-scale networks often denoted as -omes such as biome, interactome, genome and proteome. Such theoretical studies have revealed that biological networks share many features with other networks such as the Internet or social networks, e.g. their network topology.

Networks in biology [edit]

Protein-protein interaction networks [edit]

Main article: interactome

Many protein-protein interactions (PPIs) in a cell form protein interaction networks (PINs) where proteins are nodes and their interactions are edges. PINs are the most intensely analyzed networks in biology. There are dozens of PPI detection methods to identify such interactions. The yeast two-hybrid system is a commonly used experimental technique for the study of binary interactions.^[5]

Recent studies have indicated conservation of molecular networks through deep evolutionary time. ^[6] 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. ^[7] This suggests that the overall composition of the network (not simply interactions between protein pairs) is important for the overall functioning of an organism.

Gene regulatory networks (DNA-protein interaction networks) [edit]

Main article: Gene regulatory network

The activity of genes is regulated by transcription factors, proteins that typically bind to DNA. Most transcription factors bind to multiple binding sites in a genome. As a result, all cells have complex gene regulatory networks. For instance, the human genome encodes on the order of 1,400 DNA-binding transcription factors that regulate the expression of more than 20,000 human genes.^[8] Technologies to study gene regulatory networks include ChIP-chip, ChIP-seq, CliP-seq, and others.

Metabolic networks [edit]

Main article: metabolic network

The chemical compounds of a living cell are connected by biochemical reactions which convert one compound into another. The reactions are catalyzed by enzymes. Thus, all compounds in a cell are parts of an intricate biochemical network of reactions which is called metabolic network. It is possible to use network analyses to infer how selection acts on metabolic pathways. ^[2]

Signaling networks [edit]

Main article: Cell signaling

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, in the MAPK/ERK pathway is transduced from the cell surface to the cell nucleus by a series of protein-protein interactions, phosphorylation reactions, and other events. Signaling networks typically integrate protein-protein interaction networks, gene regulatory networks, and metabolic networks.

Neuronal networks [edit]

Main article: Biological neural network

The complex interactions in the brain make it a perfect candidate to apply network theory. Neurons in the brain are deeply connected with one another and this results in complex networks being present in the structural and functional aspects of the brain. ^[9] For instance, small-world network properties have been demonstrated in connections between cortical areas of the primate brain. ^[10] This suggests that cortical areas of the brain are not directly interacting with each other, but most areas can be reached from all others through only a few interactions.

Food webs [edit]

Main article: Food web

All organisms are connected to each other through feeding interactions. That is, if a species eats or is eaten by another species, they are connected in an intricate food web of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology. ^[11] That is to say, if certain individuals are removed, what happens to the network (i.e. does it collapse or adapt)? Network analysis can be used to explore food web stability and determine if certain network properties result in more stable networks. Moreover, network analysis can be used to determine how selective removals of species will influence the food web as a whole. ^[12] This is especially important considering the potential species loss due to global climate change.

Species interaction networks [edit]

Main article: social relation

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to look at complex interactions between many individuals (generally individuals of one or more species). Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole. ^[13] For instance, a study on wire-tailed manakins found that a male’s degree in the network largely predicted the ability of the male to rise in the social hierarchy (i.e. eventually obtain a territory and matings). ^[14] The use of network analysis can allow for both the discovery and understanding of these complex interactions that may have previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework. ^[15]

References [edit]

^ Proulx, S. R.; Promislow, D. E. L.; Phillips, P. C. (2005). "Network thinking in ecology and evolution". Trends in Ecology and Evolution 20 (6): 345–353. doi:10.1016/j.tree.2005.04.004. PMID 16701391.
^ ^a ^b Proulx, S.R. et al. (2005). "Network thinking in ecology and evolution". Trends in Ecology and Evolution 20 (6): 345–353.
^ Searls, D. (1993). Artificial intelligence and molecular biology. Cambridge, MA: MIT Press.
^ Bianconi, G. and Barabasi A. (2001). "Bose-Einstein condensation in complex networks". Phys. Rev. Lett. 86 (24): 5632–5635.
^ Mashaghi, A. et al. (2004). "Investigation of a protein complex network". European Physical Journal 41 (1): 113–121.
^ Sharan, R. et al. (2005). "Conserved patterns of protein interaction in multiple species". Proc. Natl. Acad. Sci. 102 (6): 1974–1979.
^ Jeong, H. et al. (2001). "Lethality and centrality in protein networks". Nature 411 (6833): 41–42.
^ Vaquerizas, J.-M. et al. (2009). "A census of human transcription factors: function, expression and evolution". Nat Rev Genet. 10 (4): 252–263. PMID 19274049.
^ Bullmore, E. and O. Sporns (2009). "Complex brain networks: graph theoretical analysis of structural and functional systems". Nature Reviews Neuroscience 10 (3): 186–198.
^ Stephan, K.E. et al. (2000). "Computational analysis of functional connectivity between areas of primate cerebral cortex". Philo. Trans. R. Soc. Lond. B Biol. Sci. 355 (1393): 111–126.
^ MacArthur, R.H. (1955). "Fluctuations in animal populations and a measure of community stability". Ecology 36 (3): 533–536.
^ Dunne, J.A. et al. (2002). "Network structure and biodiversity loss in food webs: robustness increases with connectance". Ecology Letters 5 (4): 558–567.
^ Croft, D.P et al. (2004). "Social networks in the guppy (Poecilia reticulate)". Proc. R. Soc. Lond. B 271 (Suppl.): S516–S519.
^ Ryder, T.B. et al. (2008). "Social networks in the lek-mating wire-tailed manakin (Pipra filicauda)". Proc. R. Soc. Lond. B 275 (1641): 1367–1374.
^ Krause, J. et al. (2009). "Animal social networks: an introduction". Behav. Ecol. and Sociobiol. 63 (7): 967–973.

Books [edit]

E. Estrada, "The Structure of Complex Networks: Theory and Applications", Oxford University Press, 2011, ISBN 978-0-199-59175-6

External links [edit]

Networkbio.org, The site of the series of Integrative Network Biology (INB) meetings. For the 2012 event also see www.networkbio.org
Networkbiology.org, NetworkBiology wiki site.
LindingLab.org, Technical University of Denmark (DTU) studies Network Biology and Cellular Information Processing, and is also organizing the Denmark branch of the annual "Integrative Network Biology and Cancer" symposium series.