Biological network

A biological network is any network that applies to biological systems. A network is any system with sub-units linked into a whole, such as species units linked into a whole food web. Biological networks provide a mathematical representation of connections found in ecological, evolutionary, and physiological studies, such as neural networks.[1] The analysis of biological networks in human health and disease has led to an emerging field of research called network medicine.[2][3]

Network biology and bioinformaticsEdit

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.[4] Furthermore, amino acids can be represented as a network of connected atoms, such as carbon, nitrogen, and oxygen. Nodes and edges are the essential components of a network. Nodes represent units in the network, while edges represent the connection between the units. Nodes can represent a wide array of biological units, from individual organisms to individual neurons in the brain. Two of the important properties of a network are degree and betweenness centrality. Degree denotes the number of edges connected to a node, while betweenness is a measure of how central a node is in the network.[5] 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 a high degree or high betweenness may play important roles in their overall composition.[6]

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.[7] 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).[8]

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., network topology.[9]

Networks in biologyEdit

Protein–protein interaction networksEdit

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.[10] 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.[11] Recently, high-throughput studies using mass spectrometry have identified large sets of protein interactions.[12]

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),[13] IntAct,[14] and BioGRID.[15] At the same time, multiple computational approaches were proposed to predict interactions.[16] STRING is one such database, where both computationally predicted and experimentally validated protein-protein interactions are gathered for public usage.

Recent studies have indicated the conservation of molecular networks through deep evolutionary time.[17] 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.[18] 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)Edit

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. [19] 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.[20][21]

Gene co-expression networks (transcript–transcript association networks)Edit

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.[22] Co-expression modules may correspond to cell types or pathways, while highly connected intramodular hubs can be interpreted as representatives of their respective modules.

Metabolic networksEdit

Cells break down the food and nutrients into small molecules necessary for cellular processing through a series of biochemical reactions. These biochemical reactions are catalyzed by enzymes. The complete set of all these biochemical reactions in all the pathways represents the metabolic network. Within the metabolic network, the small molecules take the roles of nodes, and they could be either carbohydrates, lipids, or amino acids. The reactions which convert these small molecules from one form to another are represented as edges. It is possible to use network analyses to infer how selection acts on metabolic pathways.[5]

Signaling networksEdit

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, 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.[23] Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks.[24][25]

Neuronal networksEdit

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.[26] For instance, small-world network properties have been demonstrated in connections between cortical regions of the primate brain[27] or during swallowing in humans.[28] 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 websEdit

All organisms are connected through feeding interactions. 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.[29] 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.[30] This is especially important considering the potential species loss due to global climate change.

Between-species interaction networksEdit

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks.[31] The use of network analysis can allow for both the discovery and understanding of how these complex interactions link together within the system’s network, a property that has previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework.[32] For example, plant-pollinator interactions are mutually beneficial and often involve many different species of pollinators as well as many different species of plants. These interactions are critical to plant reproduction and thus the accumulation of resources at the base of the food chain for primary consumers, yet these interaction networks are threatened by anthropogenic change. The use of network analysis can illuminate how pollination networks work and may, in turn, inform conservation efforts.[33] Within pollination networks, nestedness (i.e., specialists interact with a subset of species that generalists interact with), redundancy (i.e., most plants are pollinated by many pollinators), and modularity play a large role in network stability.[33][34] These network properties may actually work to slow the spread of disturbance effects through the system and potentially buffer the pollination network from anthropogenic changes somewhat.[34] More generally, the structure of species interactions within an ecological network can tell us something about the diversity, richness, and robustness of the network.[35] Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time.[36] Recent research into these complex species interactions networks is highly concerned with understanding what factors (e.g., diversity) lead to network stability.[37]

Within-species interaction networksEdit

Network analysis provides the ability to quantify associations between individuals, which makes it possible to infer details about the network as a whole at the species and/or population level.[38] One of the most attractive features of the network paradigm would be that it provides a single conceptual framework in which the social organization of animals at all levels (individual, dyad, group, population) and for all types of interaction (aggressive, cooperative, sexual, etc.) can be studied.[39]

Researchers interested in ethology across many taxa, from insects to primates, are starting to incorporate network analysis into their research. Researchers interested in social insects (e.g., ants and bees) have used network analyses better to understand the division of labor, task allocation, and foraging optimization within colonies;[40][41][42] Other researchers are interested in how specific network properties at the group and/or population level can explain individual-level behaviors. Studies have demonstrated how animal social network structure can be influenced by factors ranging from characteristics of the environment to characteristics of the individual, such as developmental experience and personality. At the level of the individual, the patterning of social connections can be an important determinant of fitness, predicting both survival and reproductive success. At the population level, network structure can influence the patterning of ecological and evolutionary processes, such as frequency-dependent selection and disease and information transmission.[43] For instance, a study on wire-tailed manakins (a small passerine bird) 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).[44] In bottlenose dolphin groups, an individual’s degree and betweenness centrality values may predict whether or not that individual will exhibit certain behaviors, like the use of side flopping and upside-down lobtailing to lead group traveling efforts; individuals with high betweenness values are more connected and can obtain more information, and thus are better suited to lead group travel and therefore tend to exhibit these signaling behaviors more than other group members.[45]

Social network analysis can also be used to describe the social organization within a species more generally, which frequently reveals important proximate mechanisms promoting the use of certain behavioral strategies. These descriptions are frequently linked to ecological properties (e.g., resource distribution). For example, network analyses revealed subtle differences in the group dynamics of two related equid fission-fusion species, Grevy’s zebra and onagers, living in variable environments; Grevy’s zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not.[46] Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse primate order, suggesting that using network measures (such as centrality, assortativity, modularity, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others.[47]

Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female chacma baboons (Papio hamadryas ursinus) revealed important dynamic changes across seasons that were previously unknown; instead of creating stable, long-lasting social bonds with friends, baboons were found to exhibit more variable relationships which were dependent on short-term contingencies related to group-level dynamics as well as environmental variability.[48] Changes in an individual's social network environment can also influence characteristics such as 'personality': for example, social spiders that huddle with bolder neighbors tend to increase also in boldness.[49] This is a very small set of broad examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly, especially since the broader development of animal-borne tags and computer vision can be used to automate the collection of social associations.[50] Social network analysis is a valuable tool for studying animal behavior across all animal species and has the potential to uncover new information about animal behavior and social ecology that was previously poorly understood.

See alsoEdit


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External linksEdit


  • E. Estrada, "The Structure of Complex Networks: Theory and Applications", Oxford University Press, 2011, ISBN 978-0-199-59175-6
  • J. Krause, R. James, D. Franks, D. Croft, "Animal Social Networks", Oxford University Press, 2015, ISBN 978-0199679041

External linksEdit

  •, The site of the series of Integrative Network Biology (INB) meetings. For the 2012 event also see
  •, NetworkBiology wiki site.
  •, 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.
  •, The National Resource for Network Biology. A US National Institute of Health (NIH) Biomedical Technology Research Center dedicated to the study of biological networks.
  • Network Repository The first interactive data and network data repository with real-time visual analytics.
  • Animal Social Network Repository(ASNR) The first multi-taxonomic repository that collates 790 social networks from more than 45 species, including those of mammals, reptiles, fish, birds, and insects
  • KONECT – Koblenz network collection.