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 polymath who was active as a mathematician, physicist, astronomer, logician, geographer, and engineer. He founded the studies of graph theory and topology and made influential ...
analyzed a real-world issue known as the
Seven Bridges of Königsberg
The Seven Bridges of Königsberg is a historically notable problem in mathematics. Its negative resolution by Leonhard Euler, in 1736, laid the foundations of graph theory and prefigured the idea of topology.
The city of Königsberg in Prussia ...
, which established the foundation of
graph theory
In mathematics and computer science, graph theory is the study of ''graph (discrete mathematics), graphs'', which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of ''Vertex (graph ...
. From the 1930s-1950s 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 l ...
s were developed. During the mid 1990s, 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 medicine. In 2014, graph theoretical methods were used by Frank Emmert-Streib to analyze biological networks.
In the 1980s, researchers started viewing
DNA
Deoxyribonucleic acid (; DNA) is a polymer composed of two polynucleotide chains that coil around each other to form a double helix. The polymer carries genetic instructions for the development, functioning, growth and reproduction of al ...
or
genome
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 genes, other functional regions of the genome such as ...
s as the dynamic storage of a language system with precise computable finite
states
State most commonly refers to:
* State (polity), a centralized political organization that regulates law and society within a territory
**Sovereign state, a sovereign polity in international law, commonly referred to as a country
**Nation state, a ...
represented as a
finite-state machine
A finite-state machine (FSM) or finite-state automaton (FSA, plural: ''automata''), finite automaton, or simply a state machine, is a mathematical model of computation. It is an abstract machine that can be in exactly one of a finite number o ...
. Recent
complex system
A complex system is a system composed of many components that may interact with one another. 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, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...
, and
physics
Physics is the scientific study of matter, its Elementary particle, 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 whi ...
.
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 t ...
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, the Molecular Interaction Database (MINT),
IntAct,
and
BioGRID.
At the same time, multiple computational approaches have been proposed to predict interactions.
FunCoup and
STRING
String or strings may refer to:
*String (structure), a long flexible structure made from threads twisted together, which is used to tie, bind, or hang other objects
Arts, entertainment, and media Films
* ''Strings'' (1991 film), a Canadian anim ...
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
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 genes, other functional regions of the genome such as ...
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 Protein biosynthesis, synthesizing a protein.
mRNA is ...
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
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis ...
. High-throughput measurement technologies, such as
microarray
A microarray is a multiplex (assay), multiplex lab-on-a-chip. Its purpose is to simultaneously detect the expression of thousands of biological interactions. It is a two-dimensional array on a Substrate (materials science), solid substrate—usu ...
,
RNA-Seq
RNA-Seq (named as an abbreviation of RNA sequencing) is a technique that uses next-generation sequencing to reveal the presence and quantity of RNA molecules in a biological sample, providing a snapshot of gene expression in the sample, also k ...
,
ChIP-chip
ChIP-on-chip (also known as ChIP-chip) is a technology that combines chromatin immunoprecipitation ('ChIP') with DNA microarray (''"chip"''). Like regular Ch-IP, ChIP, ChIP-on-chip is used to investigate interactions between proteins and DNA ''in ...
, and
ChIP-seq
ChIP-sequencing, also known as ChIP-seq, is a method used to analyze protein interactions with DNA. ChIP-seq combines chromatin immunoprecipitation (ChIP) with Massively parallel signature sequencing, massively parallel DNA sequencing to identify t ...
, 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
An enzyme () is a protein that acts as a biological catalyst 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 pro ...
. The complete set of all these biochemical reactions in all the pathways represents the
metabolic network
A metabolic network is the complete set of metabolic and physical processes that determine the physiological and biochemical properties of a cell. As such, these networks comprise the chemical reactions of metabolism, the metabolic pathways, as ...
. 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 which plays a key role in the tissue structure. 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 (biology), cell that communicates a signal from a Receptor (biochemistry), receptor on the surface of the cell to the DNA in the nucleus of the cel ...
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 network
A metabolic network is the complete set of metabolic and physical processes that determine the physiological and biochemical properties of a cell. As such, these networks comprise the chemical reactions of metabolism, the metabolic pathways, as ...
s.
Single cell sequencing technologies allows the extraction of inter-cellular signaling, an example is NicheNet, which allows to modeling intercellular communication by linking ligands to target genes.
Neuronal networks
The complex interactions in the
brain
The brain is an organ (biology), organ that serves as the center of the nervous system in all vertebrate and most invertebrate animals. It consists of nervous tissue and is typically located in the head (cephalization), usually near organs for ...
make it a perfect candidate to apply network theory.
Neuron
A neuron (American English), neurone (British English), or nerve cell, is an membrane potential#Cell excitability, excitable cell (biology), cell that fires electric signals called action potentials across a neural network (biology), neural net ...
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 graph characterized by a high clustering coefficient and low distances. In an example of the social network, high clustering implies the high probability that two friends of one person are friends themselves. The l ...
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. Position in the food web, or trophic level, is used in ecology to broadly classify organisms as autotrophs or he ...
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 network, cognitive and semantic networks, and social networks, considering distinct eleme ...
, 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. 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
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, indi ...
to
cooperative
A cooperative (also known as co-operative, coöperative, co-op, or coop) is "an autonomy, autonomous association of persons united voluntarily to meet their common economic, social and cultural needs and aspirations through a jointly owned a ...
) 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 carpel, 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 ...
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, often starting with an autotroph (such as grass or algae), also called a producer, and typically ending at an apex predator (such as grizzly bears or killer whales), detritivore (such as ...
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
Anthropogenic may also refer to things that have been generated by humans, as follows:
* Human impact on the enviro ...
change. The use of network analysis can illuminate how
pollination networks 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
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 system into varying ...
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 a branch of zoology that studies the behavior, behaviour of non-human animals. It has its scientific roots in the work of Charles Darwin and of American and German ornithology, ornithologists of the late 19th and early 20th cen ...
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 (biology), fitness of a phenotype or genotype depends on the phenotype or genotype composition of a given population.
* In positive frequency-dependent selection, the fit ...
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
The bottlenose dolphin is a toothed whale in the genus ''Tursiops''. They are common, cosmopolitan members of the family Delphinidae, the family of oceanic dolphins. Molecular studies show the genus contains three species: the common bot ...
groups, an individual's degree and
betweenness centrality
In graph theory, betweenness 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, that is, there exists at leas ...
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''), also known as hemione or Asiatic wild ass, is a species of the family Equidae native to Asia. A member of the subgenus ''Asinus'', the onager was Scientific description, described and given its binomial name ...
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 is an order (biology), order of mammals, which is further divided into the Strepsirrhini, strepsirrhines, which include lemurs, galagos, and Lorisidae, lorisids; and the Haplorhini, haplorhines, which include Tarsiiformes, tarsiers a ...
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, ke ...
,
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 ...
,
modularity
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 system into varying ...
, 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 tasks include methods for image sensor, acquiring, Image processing, processing, Image analysis, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical ...
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
Deoxyribonucleic acid (; DNA) is a polymer composed of two polynucleotide chains that coil around each other to form a double helix. The polymer carries genetic instructions for the development, functioning, growth and reproduction of al ...
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 r ...
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 and computer science, graph theory is the study of ''graph (discrete mathematics), graphs'', which are mathematical structures used to model pairwise relations between objects. A graph in this context is made up of ''Vertex (graph ...
,
computer science
Computer science is the study of computation, information, and automation. Computer science spans Theoretical computer science, theoretical disciplines (such as algorithms, theory of computation, and information theory) to Applied science, ...
, and
bioinformatics
Bioinformatics () is an interdisciplinary field of science that develops methods and Bioinformatics software, software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, ...
.
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 statistics ...
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, often abbreviated to LD, is a term in population genetics referring to the association of genes, usually linked genes, in a population. It has become an important tool in medical genetics and other fields
In defining LD, it ...
. 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, ke ...
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 medical school of Harvard University and is located in the Longwood Medical and Academic Area, Longwood Medical Area in Boston, Massachusetts. Founded in 1782, HMS is the third oldest medical school in the Un ...
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
In the study of complex networks, a network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally. In the par ...
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 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 subsectioning networks and thus a plethora of different
algorithm
In mathematics and computer science, an algorithm () is a finite sequence of Rigour#Mathematics, mathematically rigorous instructions, typically used to solve a class of specific Computational problem, problems or to perform a computation. Algo ...
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 theoretical 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 ...
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 (United States), Mid-Atlantic region and is primarily separated from the Atlantic Ocean by the Delmarva Peninsula, including parts of the Ea ...
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 is a
greedy algorithm
A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage. In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally ...
that attempts to maximize
modularity
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 system into varying ...
, 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.
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 con ...
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.
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 easy to understand compared 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 the Louvain Method solely focuses on maximizing the modularity that was chosen. The Leiden algorithm, while more complex than the Louvain Method, performs faster with better community detection and can be a valuable tool for identifying groups.
Network Motifs
Network motifs, or statistically significant recurring interaction patterns within a network, are a commonly used tool to understand biological networks. A major use case of network motifs is in
Neurophysiology
Neurophysiology is a branch of physiology and neuroscience concerned with the functions of the nervous system and their mechanisms. The term ''neurophysiology'' originates from the Greek word ''νεῦρον'' ("nerve") and ''physiology'' (whic ...
where motif analysis is commonly used to understand interconnected neuronal functions at varying scales.
As an example, in 2017, researchers at
Beijing Normal University
Beijing Normal University (BNU) () is a public university in Haidian, Beijing, Haidian, Beijing, China. It is affiliated with the Ministry of Education (China), Ministry of Education of China, and co-funded by the Ministry of Education and the B ...
analyzed highly represented 2 and 3 node network motifs in directed functional brain networks constructed by
Resting state fMRI
Resting state fMRI (rs-fMRI or R-fMRI), also referred to as task-independent fMRI or task-free fMRI, is a method of functional magnetic resonance imaging (fMRI) that is used in brain mapping to evaluate regional interactions that occur in a rest ...
data to study the basic mechanisms in brain information flow.
See also
*
List of omics topics in biology
Inspired by the terms genome and genomics, other words to describe complete Biology, biological datasets, mostly sets of biomolecules originating from one organism, have been coined with the suffix ''-ome'' and ''-omics''. Some of these terms are ...
*
Biological network inference
Biological network inference is the process of making inferences and predictions about biological networks. By using these networks to analyze patterns in biological systems, such as food-webs, we can visualize the nature and strength of these int ...
*
Biostatistics
Biostatistics (also known as biometry) is a branch of statistics that applies 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 techniques in computer science, data analysis, mathematical modeling and Computer simulation, computational simulations to understand biological systems and relationships. An intersection of computer sci ...
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Systems biology
Systems biology is the computational modeling, 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 system ...
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Weighted correlation network analysis
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Interactome
In molecular biology, an interactome is the whole set of molecular interactions in a particular cell. The term specifically refers to physical interactions among molecules (such as those among proteins, also known as protein–protein interactions ...
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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 ...
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Ecological network
References
Books
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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 RepositoryThe 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