Biological networks

Complex biological systems can 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. If you break down the protein further, amino acids can be represented as a network of bound atoms , such as carbon , nitrogen and oxygen . Vertices and edges are the main components of the network. Nodes are units in a network, while edges are interactions between units. Nodes can represent a wide range of biological units, from individual organisms to individual neurons in the brain. Two important properties of the network are the degree and centrality of the graph. A degree (or a connection different from that used in graph theory ) is the number of edges connecting a node, and centrality is a measure of how central a node is in a network ^[4] . Nodes with a high degree of interworking essentially serve as bridges between different parts of the network (that is, interactions must go through this node to reach other parts of the network). In social networks, nodes with a high degree or a high degree of centralization can play an important role in the overall structure of the network. Back in the 1980s, researchers began to consider DNA and genomes as a dynamic repository of the linguistic system with precise, computable final states represented as [Finite automaton ^[5] . Recent studies of complex systems have also shown some far-reaching commonality in organizing information on problems from biology, computer science, and physics , such as the Bose – Einstein condensate (a special state of matter) ^[6] .

Bioinformatics is increasingly shifting the emphasis from individual genes , proteins, and search algorithms to large-scale networks, often referred to as ohms such as biomes , interactomes , genomes, and proteomes . Such theoretical studies have shown that biological networks have many features in common with other networks, such as the Internet or social networks , for example, their network topology .

Protein-Protein Interaction Networks

Many protein-protein interactions (PPIs) in a cell form protein interaction networks (PINs), where proteins are nodes and their interactions are edges ^[7] .PINs are the most thoroughly analyzed networks in biology. There are dozens of IPP detection methods to detect such interactions. Two-hybrid analysis is a widely used experimental technique for studying binary interactions ^[8] .

Recent studies have shown that molecular networks persist over a long evolution ^[9] . Moreover, proteins with a high degree of connectivity have been found to be more important for survival than proteins with a lower degree ^[10] . This suggests that the overall composition of the network (and not just the interaction between protein pairs) is important for the overall functioning of the body.

Gene regulatory networks (DNA-protein interaction networks)

Gene activity is regulated by transcription factors - proteins that are usually associated with DNA . Most transcription factors bind to several binding sites in the genome . As a result, all cells have complex gene regulatory networks. For example, the human genome encodes about 1400 DNA-binding transcription factors that regulate the expression of more than 20,000 human genes ^[11] . Technologies for the study of gene regulatory networks include ChIP-chip , ChIP-seq , CliP-seq and others.

Gene Co-Expression Networks (Transcription-Transcription Networks)

Gene coexpression networks can be interpreted as networks of associations between variables that measure the content of transcripts. These networks were used to provide systematic biological analysis of DNA microarray data, RNA-seq data, miRNA data, etc. Analysis of weighted gene coexpression networks is widely used to identify coexpression modules and intramodular concentrator genes. Coexpression modules may correspond to cell types or pathways. Highly coupled intramodular hubs can be interpreted as representatives of their respective modules.

Metabolic Networks

The chemical compounds of a living cell are linked by biochemical reactions that turn one compound into another. Reactions are catalyzed by enzymes . Thus, all compounds in the cell are part of a complex biochemical network of reactions called the metabolic network . Network analysis can be used to determine how selection affects metabolic pathways ^[4] .

Signal Networks

Signals are transmitted within or between cells and thus form complex signal networks. For example, in the ERK signaling pathway, the path from the cell surface to the cell nucleus is transmitted by a series of protein-protein interactions, phosphorylation reactions, and other events. Signal networks usually integrate interactomes , gene regulatory networks, and metabolic networks.

Neural Networks

The complex interactions in the brain make it an ideal candidate for applying network theory. Neurons in the brain are closely related to each other, and this leads to the fact that complex structures are present in the structural and functional aspects of the brain ^[12] . For example, the properties of a small world have been demonstrated in the connections between the cortical regions of the brain of primates ^[13] or during swallowing in humans ^[14] . This suggests that the cortical regions of the brain do not interact directly with each other, but most areas can be reached from all others through just a few interactions.

Food Networks

All organisms are linked to each other through food interactions. That is, if a species eats or is eaten by another species, they are connected into a complex food web of interactions between predators and victims. The stability of these interactions has been a long-standing issue in ecology ^[15] . That is, if some members of the network are deleted, what happens to the network (that is, it falls apart or adapts)? Network analysis can be used to study the stability of the food network and determine whether certain properties of the network lead to more stable networks. In addition, network analysis can be used to determine how selective removal of species will affect the food network as a whole ^[16] . This is especially important given the potential loss of species due to global climate change.

Interspecific Interaction Networks

Main article: social relations In biology, pairwise interactions have historically been at the center of intense research. Thanks to the latest advances in network science, it has become possible to expand pair interactions to include individuals of many species participating in many interactions in order to understand the structure and function of more fundamental network sciences ^[17] . Using the analysis of social networks can both detect and understand how these complex interactions are interconnected in the network of the system, a previously poorly understood connection. This powerful tool allows you to study different types of interactions (from competition to cooperation ) using the same general structure ^[18] . For example, the interaction of plants and pollinating insects is mutually beneficial and often includes many different types of pollinators, as well as many different types of plants. These interactions are crucial for plant reproduction and, therefore, for the accumulation of resources at the core of the food chain for primary consumers, however, these interaction networks are threatened by anthropogenic factors . Using network analysis can shed light on the operation of pollination networks and, in turn, can serve as the basis for conservation efforts ^[19]. Inside pollination networks, nesting (that is, specialists interact with a subset of the species that universals communicate with), redundancy (that is most plants are pollinated by many pollinators) and modularity play a large role in network stability ^{[19] [20]} . These network properties can be used to slow the spread of perturbation effects through the system and potentially partially protect the buffer network from anthropogenic changes ^[20] . More generally, the structure of the interaction of species in an ecological network can tell us about the diversity, richness and reliability of the network ^[21] . Researchers can even compare current designs of species interaction networks with historical reconstructions of ancient networks to determine how networks have changed over time ^[22] . Recent studies in these networks of complex interactions are associated with an understanding of what factors (eg, diversity) lead to network stability ^[23] .

Intraspecific Interaction Networks

Network analysis makes it possible to quantitatively determine the relationships between individuals, which allows us to derive details about the network as a whole at the level of species and / or populations.24 Researchers interested in the behavior of animals in a variety of taxa, from insects to primates, begin to include network analysis in their studies . Researchers interested in social insects (such as ants and bees) used network analysis to better understand the division of labor, task distribution, and optimization of food searches in colonies ^{[25] [26] [27]} ; Other researchers are interested in how certain properties networks at the group and / or population level can explain behavior at the individual level. For example, a study of wire-tailed manakins (a small bird of the passerine family) showed that the importance of the male in the network significantly increases the male’s ability to ascend in the social hierarchy (that is, ultimately get the territory and the female) ^[28] . In bottlenose dolphin groups , the individual importance of the centrality of the graph and interpersonal relationships can predict whether this individual will exhibit certain behaviors, for example, using lateral spanking and turning up to guide group trips; individuals with high values ​​of interpersonal relationships are more connected and can get more information, and therefore are better suited for group travel and, therefore, tend to exhibit this signaling behavior more than other members of the group ^[29] .

Network analysis can also be used to describe the social organization of the species as a whole, which often reveals important immediate mechanisms that facilitate the use of certain behavioral strategies. These descriptions are often associated with environmental properties (e.g., resource allocation). For example, network analysis revealed subtle differences in the group dynamics of two related species in a merging-dividing community - Grevy's zebras and kulans ; Grevy’s zebras show clear preferences in choosing associations when they are divided into smaller groups, while kulans do not ^[30] . Similarly, researchers interested in primates also used network analysis to compare social organizations across different orders of primates, suggesting that using network metrics (such as centralization, a tendency to group ) could be useful in explaining the types of social behavior. we see inside certain groups, and not others ^[31] .

Finally, an analysis of social networks can also reveal important fluctuations in the behavior of animals in a changing environment. For example, a network analysis of female bear baboons ( Papio hamadryas ursinus ) revealed important dynamic changes in different seasons that were previously unknown; Instead of creating stable, long-term social ties with friends, it was found that baboons have more volatile relationships that depend on short-term unforeseen circumstances associated with group-level dynamics and environmental variability ^[32] . Changes in the environment of a person’s social network can also affect characteristics such as “personality”: for example, sociable spiders who push with bolder neighbors tend to increase and courage ^[33] . This is a very small set of common examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly. Social network analysis is a valuable tool for studying animal behavior across all animal species and may reveal new information about animal behavior and social ecology that has previously been poorly studied.

• Biological network output
• Statistics
• Biological statistics
• Computational Biology
• Systems biology
• Analysis of Weighted Gene Coexpression Networks
• Interact
• Network medicine

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

• Networkbio.org , Сайт для обсуждения INB. С 2012 ещё и www.networkbio.org
• Networkbiology.org ,Сайт посвященный сетевой биологии.
• LindingLab.org , Danish Technical University (DTU), is studying network biology and cellular information processing, and is organizing a branch of the annual Integrated Network Biology and Cancer Symposium series in Denmark.