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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 typical graphing representation consists of a set of nodes connected by edges.


History of networks

As early as 1736
Leonhard Euler Leonhard Euler ( , ; 15 April 170718 September 1783) was a Swiss mathematician, physicist, astronomer, geographer, logician and engineer who founded the studies of graph theory and topology and made pioneering and influential discoveries ...
analyzed a real-world issue known as the Seven Bridges of Königsberg, which established the foundation of
graph theory In mathematics, graph theory is the study of '' graphs'', which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of '' vertices'' (also called ''nodes'' or ''points'') which are conn ...
. From the 1930's-1950's the study of
random graph In mathematics, random graph is the general term to refer to probability distributions over graphs. Random graphs may be described simply by a probability distribution, or by a random process which generates them. The theory of random graphs ...
s were developed. During the mid 1990's, it was discovered that many different types of "real" networks have structural properties quite different from random networks. In the late 2000's, scale-free and small-world networks began shaping the emergence of systems biology, network biology, and network medicin

In 2014, graph theoretical methods were used b
Frank Emmert-Streib
to analyze biological networks. In the 1980s, researchers started viewing DNA or
genome In the fields of molecular biology and genetics, a genome is all the genetic information of an organism. It consists of nucleotide sequences of DNA (or RNA in RNA viruses). The nuclear genome includes protein-coding genes and non-coding ...
s as the dynamic storage of a language system with precise computable finite states represented as a finite state machine. Recent
complex system A complex system is a system composed of many components which may interact with each other. Examples of complex systems are Earth's global climate, organisms, the human brain, infrastructure such as power grid, transportation or communication sy ...
s research has also suggested some far-reaching commonality in the organization of information in problems from biology,
computer science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...
, and
physics Physics is the natural science that studies matter, its fundamental constituents, its motion and behavior through space and time, and the related entities of energy and force. "Physical science is that department of knowledge which ...
.


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 interaction Protein–protein interactions (PPIs) are physical contacts of high specificity established between two or more protein molecules as a result of biochemical events steered by interactions that include electrostatic forces, hydrogen bonding and th ...
s (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 In computing, a database is an organized collection of data stored and accessed electronically. Small databases can be stored on a file system, while large databases are hosted on computer clusters or cloud storage. The design of databases spa ...
, 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 In the fields of molecular biology and genetics, a genome is all the genetic information of an organism. It consists of nucleotide sequences of DNA (or RNA in RNA viruses). The nuclear genome includes protein-coding genes and non-coding ...
encodes thousands of genes whose products (
mRNA In molecular biology, messenger ribonucleic acid (mRNA) is a single-stranded molecule of RNA that corresponds to the genetic sequence of a gene, and is read by a ribosome in the process of synthesizing a protein. mRNA is created during the ...
s, 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 In molecular biology, a transcription factor (TF) (or sequence-specific DNA-binding factor) is a protein that controls the rate of transcription of genetic information from DNA to messenger RNA, by binding to a specific DNA sequence. The fun ...
. 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 A microarray is a multiplex lab-on-a-chip. Its purpose is to simultaneously detect the expression of thousands of genes from a sample (e.g. from a tissue). It is a two-dimensional array on a solid substrate—usually a glass slide or silicon ...
,
RNA-Seq RNA-Seq (named as an abbreviation of RNA sequencing) is a sequencing technique which uses next-generation sequencing (NGS) to reveal the presence and quantity of RNA in a biological sample at a given moment, analyzing the continuously changing ...
, 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.


Metabolic networks

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 Enzymes () are proteins that act as biological catalysts by accelerating chemical reactions. The molecules upon which enzymes may act are called substrates, and the enzyme converts the substrates into different molecules known as products. ...
. 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.


Signaling networks

Signals are transduced within cells or in between cells and thus form complex signaling networks. For instance, the
MAPK/ERK pathway The MAPK/ERK pathway (also known as the Ras-Raf-MEK-ERK pathway) is a chain of proteins in the cell that communicates a signal from a receptor on the surface of the cell to the DNA in the nucleus of the cell. The signal starts when a signaling ...
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

The complex interactions in the
brain A brain is an organ (biology), organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. It is located in the head, usually close to the sensory organs for senses such as Visual perception, vision. I ...
make it a perfect candidate to apply network theory.
Neuron A neuron, neurone, or nerve cell is an electrically excitable cell that communicates with other cells via specialized connections called synapses. The neuron is the main component of nervous tissue in all animals except sponges and placozoa ...
s 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. For instance,
small-world network A small-world network is a type of mathematical graph in which most nodes are not neighbors of one another, but the neighbors of any given node are likely to be neighbors of each other and most nodes can be reached from every other node by a sm ...
properties have been demonstrated in connections between cortical regions of the primate brain or during swallowing in humans. 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

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 A food web is the natural interconnection of food chains and a graphical representation of what-eats-what in an ecological community. Another name for food web is consumer-resource system. Ecologists can broadly lump all life forms into one o ...
of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology. 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. This is especially important considering the potential species loss due to global climate change.


Between-species interaction networks

In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in
network science Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors rep ...
, 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 network An ecological network is a representation of the biotic interactions in an ecosystem, in which species (nodes) are connected by pairwise interactions (links). These interactions can be trophic or symbiotic. Ecological networks are used to describ ...
s. The use of
network analysis Network analysis can refer to: * Network theory, the analysis of relations through mathematical graphs ** Social network analysis, network theory applied to social relations * Network analysis (electrical circuits) See also *Network planning and d ...
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 Competition is a rivalry where two or more parties strive for a common goal which cannot be shared: where one's gain is the other's loss (an example of which is a zero-sum game). Competition can arise between entities such as organisms, indivi ...
to
cooperative A cooperative (also known as co-operative, co-op, or coop) is "an autonomous association of persons united voluntarily to meet their common economic, social and cultural needs and aspirations through a jointly owned and democratically-contro ...
) using the same general framework. For example, plant-
pollinator A pollinator is an animal that moves pollen from the male anther of a flower to the female stigma of a flower. This helps to bring about fertilization of the ovules in the flower by the male gametes from the pollen grains. Insects are the m ...
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 A food chain is a linear network of links in a food web starting from producer organisms (such as grass or algae which produce their own food via photosynthesis) and ending at an apex predator species (like grizzly bears or killer whales), de ...
for primary consumers, yet these interaction networks are threatened by
anthropogenic Anthropogenic ("human" + "generating") is an adjective that may refer to: * Anthropogeny, the study of the origins of humanity Counterintuitively, anthropogenic may also refer to things that have been generated by humans, as follows: * Human i ...
change. The use of network analysis can illuminate how
pollination network A pollination network is a bipartite mutualistic network in which plants and pollinators are the nodes, and the pollination interactions form the links between these nodes. The pollination network is bipartite as interactions only exist betwe ...
s work and may, in turn, inform conservation efforts. 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 Broadly speaking, modularity is the degree to which a system's components may be separated and recombined, often with the benefit of flexibility and variety in use. The concept of modularity is used primarily to reduce complexity by breaking a sy ...
play a large role in network stability. 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. More generally, the structure of species interactions within an ecological network can tell us something about the diversity, richness, and robustness of the network. Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time. Much research into these complex species interactions networks is highly concerned with understanding what factors (e.g., species richness, connectance, nature of the physical environment) lead to network stability.


Within-species interaction networks

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. 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. Researchers interested in
ethology Ethology is the scientific study of animal behaviour, usually with a focus on behaviour under natural conditions, and viewing behaviour as an evolutionarily adaptive trait. Behaviourism as a term also describes the scientific and objecti ...
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. 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 Frequency-dependent selection is an evolutionary process by which the fitness of a phenotype or genotype depends on the phenotype or genotype composition of a given population. * In positive frequency-dependent selection, the fitness of a phenotyp ...
and disease and information transmission. 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). In
bottlenose dolphin Bottlenose dolphins are aquatic mammals in the genus ''Tursiops.'' They are common, cosmopolitan members of the family Delphinidae, the family of oceanic dolphins. Molecular studies show the genus definitively contains two species: the comm ...
groups, an individual's degree and
betweenness centrality In graph theory, betweenness centrality (or "betweeness centrality") is a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices suc ...
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.
Social network analysis Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of ''nodes'' (individual actors, people, or things within the network) ...
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
onager The onager (; ''Equus hemionus'' ), A new species called the kiang (''E. kiang''), a Tibetan relative, was previously considered to be a subspecies of the onager as ''E. hemionus kiang'', but recent molecular studies indicate it to be a distinct ...
s, living in variable environments; Grevy's zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not. Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse
primate Primates are a diverse order of mammals. They are divided into the strepsirrhines, which include the lemurs, galagos, and lorisids, and the haplorhines, which include the tarsiers and the simians ( monkeys and apes, the latter includin ...
order, suggesting that using network measures (such as
centrality In graph theory and network analysis, indicators of centrality assign numbers or rankings to nodes within a graph corresponding to their network position. Applications include identifying the most influential person(s) in a social network, key ...
,
assortativity Assortativity, or assortative mixing is a preference for a network's nodes to attach to others that are similar in some way. Though the specific measure of similarity may vary, network theorists often examine assortativity in terms of a node's de ...
,
modularity Broadly speaking, modularity is the degree to which a system's components may be separated and recombined, often with the benefit of flexibility and variety in use. The concept of modularity is used primarily to reduce complexity by breaking a sy ...
, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others. Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female
chacma baboon The chacma baboon (''Papio ursinus''), also known as the Cape baboon, is, like all other baboons, from the Old World monkey family. It is one of the largest of all monkeys. Located primarily in southern Africa, the chacma baboon has a wide vari ...
s (''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. 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. 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 Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human ...
can be used to automate the collection of social associations. 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.


DNA-DNA chromatin networks

Within a nucleus, DNA is constantly in motion. Perpetual actions such as genome folding and Cohesin extrusion morph the shape of a genome in real time. The spatial location of strands of
chromatin Chromatin is a complex of DNA and protein found in eukaryote, eukaryotic cells. The primary function is to package long DNA molecules into more compact, denser structures. This prevents the strands from becoming tangled and also plays important ...
relative to each other plays an important role in the activation or suppression of certain genes. DNA-DNA Chromatin Networks help biologists to understand these interactions by analyzing commonalities amongst different loci. The size of a network can vary significantly, from a few genes to several thousand and thus network analysis can provide vital support in understanding relationships among different areas of the genome. As an example, analysis of spatially similar loci within the organization in a nucleus with Genome Architecture Mapping (GAM) can be used to construct a network of loci with edges representing highly linked genomic regions. The first graphic showcases the Hist1 region of the mm9 mouse genome with each node representing genomic loci. Two nodes are connected by an edge if their linkage disequilibrium is greater than the average across all 81 genomic windows. The locations of the nodes within the graphic are randomly selected and the methodology of choosing edges yields a, simple to show, but rudimentary graphical representation of the relationships in the dataset. The second visual exemplifies the same information as the previous; However, the network starts with every loci placed sequentially in a ring configuration. It then pulls nodes together using linear interpolation by their linkage as a percentage. The figure illustrates strong connections between the center genomic windows as well as the edge loci at the beginning and end of the Hist1 region.


Modelling biological networks


Introduction

To draw useful information from a biological network, an understanding of the statistical and mathematical techniques of identifying relationships within a network is vital. Procedures to identify association, communities, and centrality within nodes in a biological network can provide insight into the relationships of whatever the nodes represent whether they are genes, species, etc. Formulation of these methods transcends disciplines and relies heavily on
Graph Theory In mathematics, graph theory is the study of '' graphs'', which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of '' vertices'' (also called ''nodes'' or ''points'') which are conn ...
,
Computer Science Computer science is the study of computation, automation, and information. Computer science spans theoretical disciplines (such as algorithms, theory of computation, information theory, and automation) to Applied science, practical discipli ...
, and
Bioinformatics Bioinformatics () is an interdisciplinary field that develops methods and software tools for understanding biological data, in particular when the data sets are large and complex. As an interdisciplinary field of science, bioinformatics combi ...
.


Association

There are many different ways to measure the relationships of nodes when analyzing a network. In many cases, the measure used to find nodes that share similarity within a network is specific to the application it is being used. One of the types of measures that biologists utilize is
Correlation In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistic ...
which specifically centers around the linear relationship between two variables. As an example, weighted gene co-expression network analysis uses Pearson correlation to analyze linked gene expression and understand genetics at a systems level. Another measure of correlation is linkage disequilibrium. Linkage disequilibrium describes the non-random association of genetic sequences among loci in a given chromosome. An example of its use is in detecting relationships in GAM data across genomic intervals based upon detection frequencies of certain loci.


Centrality

The concept of
Centrality In graph theory and network analysis, indicators of centrality assign numbers or rankings to nodes within a graph corresponding to their network position. Applications include identifying the most influential person(s) in a social network, key ...
can be extremely useful when analyzing biological network structures. There are many different methods to measure centrality such as betweenness, degree, Eigenvector, and Katz centrality. Every type of centrality technique can provide different insights on nodes in a particular network; However, they all share the commonality that they are to measure the prominence of a node in a network. In 2005, Researchers at
Harvard Medical School Harvard Medical School (HMS) is the graduate medical school of Harvard University and is located in the Longwood Medical Area of Boston, Massachusetts. Founded in 1782, HMS is one of the oldest medical schools in the United States and is cons ...
utilized centrality measures with the yeast protein interaction network. They found that proteins that exhibited high Betweenness centrality were more essential and translated closely to a given protein's evolutionary age.


Communities

Studying the Community structure of a network by subdividing groups of nodes into like-regions can be an integral tool for bioinformatics when exploring data as a network. A food web of The
Secaucus High School Secaucus High School is a four-year comprehensive public high school that serves students in ninth through twelfth grade from Secaucus, in Hudson County, New Jersey, United States, operating as the lone secondary school of the Secaucus Board o ...
Marsh exemplifies the benefits of grouping as the relationships between nodes are far easier to analyze with well-made communities. While the first graphic is hard to visualize, the second provides a better view of the pockets of highly connected feeding relationships that would be expected in a food web. The problem of community detection is still an active problem. Scientists and graph theorists continuously discover new ways of sub sectioning networks and thus a plethora of different
algorithm In mathematics and computer science, an algorithm () is a finite sequence of rigorous instructions, typically used to solve a class of specific problems or to perform a computation. Algorithms are used as specifications for performing ...
s exist for creating these relationships. Like many other tools that biologists utilize to understand data with network models, every algorithm can provide its own unique insight and may vary widely on aspects such as accuracy or
Time Complexity In computer science, the time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by t ...
of calculation. In 2002, a food web of marine mammals in the
Chesapeake Bay The Chesapeake Bay ( ) is the largest estuary in the United States. The Bay is located in the Mid-Atlantic region and is primarily separated from the Atlantic Ocean by the Delmarva Peninsula (including the parts: the Eastern Shore of Maryland / ...
was divided into communities by biologists using a community detection algorithm based on neighbors of nodes with high degree centrality. The resulting communities displayed a sizable split in pelagic and benthic organisms. Two very common community detection algorithms for biological networks are the Louvain Method and Leiden Algorithm. The
Louvain method The Louvain method for community detection is a method to extract non-overlapping communities from large networks created by Blondel ''et al''. from the University of Louvain (the source of this method's name). The method is a greedy optimizatio ...
is a greedy algorithm that attempts to maximize
modularity Broadly speaking, modularity is the degree to which a system's components may be separated and recombined, often with the benefit of flexibility and variety in use. The concept of modularity is used primarily to reduce complexity by breaking a sy ...
, which favors heavy edges within communities and sparse edges between, within a set of nodes. The algorithm starts by each node being in its own community and iteratively being added to the particular node's community that favors a higher modularity.Traag, V.A., Waltman, L. & van Eck, N.J. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9, 5233 (2019). https://doi.org/10.1038/s41598-019-41695-z Once no modularity increase can occur by joining nodes to a community, a new
weighted network A weighted network is a network where the ties among nodes have weights assigned to them. A network is a system whose elements are somehow connected. The elements of a system are represented as nodes (also known as actors or vertices) and the connec ...
is constructed of communities as nodes with edges representing between-community edges and loops representing edges within a community. The process continues until no increase in modularity occurs.Ozaki, Naoto. Tezuka Hiroshi. Inaba, Mary. “A Simple Acceleration Method for the Louvain Algorithm” International Journal of Computer and Electrical Engineering, vol. 8, no. 3, 3 June 2016, page numbers pp 207-218. http://www.ijcee.org/vol8/927-A023.pdf While the Louvain Method provides good community detection, there are a few ways that it is limited. By mainly focusing on maximizing a given measure of modularity, it may be led to craft badly connected communities by degrading a model for the sake of maximizing a modularity metric; However, the Louvain Method performs fairly and is can be easy to understand comparatively to many other community detection algorithms. The Leiden Algorithm expands on the Louvain Method by providing a number of improvements. When joining nodes to a community, only neighborhoods that have been recently changed are considered. This greatly improves the speed of merging nodes. Another optimization is in the refinement phase in-which the algorithm randomly chooses for a node from a set of communities to merge with. This allows for greater depth in choosing communities as Louvain solely focuses on maximizing the modularity that was chosen. The Leiden algorithm, while more complex than Louvain, performs faster with higher quality nodes and can be a valuable tool for identifying groups.


See also

*
List of omics topics in biology Inspired by the terms genome and genomics, other words to describe complete biological datasets, mostly sets of biomolecules originating from one organism, have been coined with the suffix '' -ome'' and ''-omics''. Some of these terms are related ...
* Biological network inference *
Biostatistics Biostatistics (also known as biometry) are the development and application of statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experimen ...
*
Computational biology Computational biology refers to the use of data analysis, mathematical modeling and Computer simulation, computational simulations to understand biological systems and relationships. An intersection of computer science, biology, and big data, the ...
*
Systems biology Systems biology is the computational and mathematical analysis and modeling of complex biological systems. It is a biology-based interdisciplinary field of study that focuses on complex interactions within biological systems, using a holistic ...
*
Weighted correlation network analysis Weighted correlation network analysis, also known as weighted gene co-expression network analysis (WGCNA), is a widely used data mining method especially for studying biological networks based on pairwise correlations between variables. While it ...
* Interactome *
Network medicine Network medicine is the application of network science towards identifying, preventing, and treating diseases. This field focuses on using network topology and network dynamics towards identifying diseases and developing medical drugs. Biological ...


References


Books

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


External links


Networkbio.org
The site of the series of Integrative Network Biology (INB) meetings. For the 2012 event also see www.networkbio.org
Network Tools and Applications in Biology
(NETTAB) workshops.
Networkbiology.org
NetworkBiology wiki site.
Linding Lab
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.
NRNB.org
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 Biological techniques and tools Bioinformatics Systems biology Networks