Biological network

A '''biological network''' is a method of representing systems as complex sets of binary interactions or relations between various biological entities.<ref name=":0">{{Cite journal |last1=Koutrouli |first1=Mikaela |last2=Karatzas |first2=Evangelos |last3=Paez-Espino |first3=David |last4=Pavlopoulos |first4=Georgios A. |date=2020 |title=A Guide to Conquer the Biological Network Era Using Graph Theory |journal=Frontiers in Bioengineering and Biotechnology |volume=8 |article-number=34 |doi=10.3389/fbioe.2020.00034 |pmid=32083072 |pmc=7004966 |doi-access=free }}</ref> In general, networks or graphs are used to capture relationships between entities or objects.<ref name=":0" /> A network can be represented as an N×N matrix where N is the number of nodes, and who's entries tell you if two nodes share an edge. Typically, this matrix will have entries of 0 or 1 with 1 denoting an edge. We can optionally use a weighted graph by assigning a weight to each edge to show how relevant the connection between two nodes is.<ref name=":0" /> none|thumb|Example of a weighted network (weights can also be visualized by giving edges different widths)

== History of networks == {{main article|Graph theory#History}} [[File:Konigsberg bridges.png|left|thumb|214x214px|Seven Bridges of Königsberg. Euler's objective was to design a path that crossed each bridge only once. ]] As early as 1736 Leonhard Euler analyzed a real-world issue known as the Seven Bridges of Königsberg, which established the foundation of graph theory. From the 1930s-1950s the study of random graphs were developed. During the mid 1990s, it was discovered that many different types of "real" networks have structural properties quite different from random networks.<ref>{{Cite journal |last1=Emmert-Streib |first1=Frank |last2=Dehmer |first2=Matthias |date=2015 |title=Biological networks: the microscope of the twenty-first century? |journal=Frontiers in Genetics |volume=6 |page=307 |doi=10.3389/fgene.2015.00307 |pmid=26528327 |pmc=4602153 |doi-access=free }}</ref> In the late 2000s, scale-free and small-world networks began shaping the emergence of systems biology, network biology, and network medicine.<ref>{{cite journal |last1=Emmert-Streib |first1=Frank |last2=Dehmer |first2=Matthias |title=Biological networks: the microscope of the twenty-first century? |journal=Frontiers in Genetics |date=13 October 2015 |volume=6 |page=307 |doi=10.3389/fgene.2015.00307 |doi-access=free |pmid=26528327 |pmc=4602153 }}</ref> In 2014, graph theoretical methods were used by Frank Emmert-Streib to analyze biological networks.<ref>{{cite journal |last1=Emmert-Streib |first1=Frank |last2=Dehmer |first2=Matthias |last3=Haibe-Kains |first3=Benjamin |title=Gene regulatory networks and their applications: understanding biological and medical problems in terms of networks |journal=Frontiers in Cell and Developmental Biology |date=19 August 2014 |volume=2 |page=38 |doi=10.3389/fcell.2014.00038 |doi-access=free |pmid=25364745 |pmc=4207011 }}</ref>

In the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states represented as a finite-state machine.<ref>{{cite book |last= Searls |first=D.B. |chapter=The computational linguistics of biological sequences |isbn=978-0-262-58115-8 |oclc=77932373 |title= Artificial intelligence and molecular biology |publisher= MIT Press |year= 1993 |location= Cambridge, MA}}</ref> Recent complex systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics.

==Networks in biology==

===Protein–protein interaction networks=== {{main article|interactome}} thumb|Shows protein interaction affecting HUD ''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''.<ref>{{Cite journal|last1=Habibi|first1=Iman|last2=Emamian|first2=Effat S.|last3=Abdi|first3=Ali|date=2014-01-01|title=Quantitative analysis of intracellular communication and signaling errors in signaling networks|journal=BMC Systems Biology|volume=8|article-number=89|doi=10.1186/s12918-014-0089-z |pmc=4255782|pmid=25115405 |doi-access=free }}</ref> Due to their undirected nature, it is difficult to identify all the proteins involved in an interaction. Protein–protein interactions (PPIs) are essential to the cellular processes and also the most intensely analyzed networks in biology. PPIs could be discovered by various experimental techniques, among which the yeast two-hybrid system is a commonly used technique for the study of binary interactions.<ref>{{cite journal |last1=Mashaghi |first1=A. R. |last2=Ramezanpour |first2=A. |last3=Karimipour |first3=V. |title=Investigation of a protein complex network |journal=The European Physical Journal B |date=September 2004 |volume=41 |issue=1 |pages=113–121 |doi=10.1140/epjb/e2004-00301-0 |arxiv=cond-mat/0304207 |bibcode=2004EPJB...41..113M }}</ref> Recently, high-throughput studies using mass spectrometry have identified large sets of protein interactions.<ref name="SmitsVermeulen2016">{{cite journal |last1=Smits |first1=Arne H. |last2=Vermeulen |first2=Michiel |title=Characterizing Protein–Protein Interactions Using Mass Spectrometry: Challenges and Opportunities |journal=Trends in Biotechnology |date=October 2016 |volume=34 |issue=10 |pages=825–834 |doi=10.1016/j.tibtech.2016.02.014 |pmid=26996615 |hdl=2066/161800 |hdl-access=free }}</ref>

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),<ref name=mint>{{cite journal|last1=Zanzoni|first1=A|last2=Montecchi-Palazzi|first2=L|last3=Quondam|first3=M|last4=Ausiello|first4=G|last5=Helmer-Citterich|first5=M|last6=Cesareni|first6=G|title=MINT: a Molecular INTeraction database.|journal=FEBS Letters|date=Feb 20, 2002|volume=513|issue=1|pages=135–40|pmid=11911893|doi=10.1016/s0014-5793(01)03293-8|pmc=1751541|bibcode=2002FEBSL.513..135Z}}</ref> IntAct,<ref name=intact>{{cite journal|last1=Kerrien|first1=S.|last2=Aranda|first2=B.|last3=Breuza|first3=L.|last4=Bridge|first4=A.|last5=Broackes-Carter|first5=F.|last6=Chen|first6=C.|last7=Duesbury|first7=M.|last8=Dumousseau|first8=M.|last9=Feuermann|first9=M.|last10=Hinz|first10=U.|last11=Jandrasits|first11=C.|last12=Jimenez|first12=R. C.|last13=Khadake|first13=J.|last14=Mahadevan|first14=U.|last15=Masson|first15=P.|last16=Pedruzzi|first16=I.|last17=Pfeiffenberger|first17=E.|last18=Porras|first18=P.|last19=Raghunath|first19=A.|last20=Roechert|first20=B.|last21=Orchard|first21=S.|last22=Hermjakob|first22=H.|title=The IntAct molecular interaction database in 2012|journal=Nucleic Acids Research|date=24 November 2011|volume=40|issue=D1|pages=D841–D846|doi=10.1093/nar/gkr1088|pmid=22121220|pmc=3245075}}</ref> and BioGRID.<ref name="OughtredRust2020">{{cite journal|last1=Oughtred|first1=Rose|last2=Rust|first2=Jennifer|last3=Chang|first3=Christie|last4=Breitkreutz|first4=Bobby-Joe|last5=Stark|first5=Chris|last6=Willems|first6=Andrew|last7=Boucher|first7=Lorrie|last8=Leung|first8=Genie|last9=Kolas|first9=Nadine|last10=Zhang|first10=Frederick|last11=Dolma|first11=Sonam|last12=Coulombe-Huntington|first12=Jasmin|last13=Chatr-aryamontri|first13=Andrew|last14=Dolinski|first14=Kara|last15=Tyers|first15=Mike|title=TheBioGRIDdatabase: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions|journal=Protein Science|volume=30|issue=1|year=2020|pages=187–200 |doi=10.1002/pro.3978|pmid=33070389|pmc=7737760|doi-access=free}}</ref> At the same time, multiple computational approaches have been proposed to predict interactions.<ref name="Jansen2003">{{cite journal |last1=Jansen |first1=Ronald |last2=Yu |first2=Haiyuan |last3=Greenbaum |first3=Dov |last4=Kluger |first4=Yuval |last5=Krogan |first5=Nevan J. |last6=Chung |first6=Sambath |last7=Emili |first7=Andrew |last8=Snyder |first8=Michael |last9=Greenblatt |first9=Jack F. |last10=Gerstein |first10=Mark |title=A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data |journal=Science |date=17 October 2003 |volume=302 |issue=5644 |pages=449–453 |doi=10.1126/science.1087361 |pmid=14564010 |bibcode=2003Sci...302..449J }}</ref> FunCoup and STRING are examples of such databases, where protein-protein interactions inferred from multiple evidences are gathered and made available for public usage.{{fact|date=December 2024}} Recent studies have indicated the conservation of molecular networks through deep evolutionary time.<ref>{{cite journal |last1=Sharan |first1=Roded |last2=Suthram |first2=Silpa |last3=Kelley |first3=Ryan M. |last4=Kuhn |first4=Tanja |last5=McCuine |first5=Scott |last6=Uetz |first6=Peter |last7=Sittler |first7=Taylor |last8=Karp |first8=Richard M. |last9=Ideker |first9=Trey |title=Conserved patterns of protein interaction in multiple species |journal=Proceedings of the National Academy of Sciences |date=8 February 2005 |volume=102 |issue=6 |pages=1974–1979 |doi=10.1073/pnas.0409522102 |doi-access=free |pmid=15687504 |pmc=548573 }}</ref> 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.<ref>{{cite journal |last1=Jeong |first1=H. |last2=Mason |first2=S. P. |last3=Barabási |first3=A.-L. |last4=Oltvai |first4=Z. N. |title=Lethality and centrality in protein networks |journal=Nature |date=May 2001 |volume=411 |issue=6833 |pages=41–42 |doi=10.1038/35075138 |pmid=11333967 |arxiv=cond-mat/0105306 |bibcode=2001Natur.411...41J }}</ref> 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)=== {{main article| Gene regulatory network}} thumb|Shows interaction between ADRB2 and cancer-specific genes The genome encodes thousands of genes whose products (mRNAs, proteins) are crucial to the various processes of life, such as cell differentiation, cell survival, and metabolism. Genes produce such products through a process called transcription, which is regulated by a class of proteins called transcription factors. For instance, the human genome encodes almost 1,500 DNA-binding transcription factors that regulate the expression of more than 20,000 human genes.<ref>{{cite journal |last1=Vaquerizas |first1=Juan M. |last2=Kummerfeld |first2=Sarah K. |last3=Teichmann |first3=Sarah A. |last4=Luscombe |first4=Nicholas M. |title=A census of human transcription factors: function, expression and evolution |journal=Nature Reviews Genetics |date=April 2009 |volume=10 |issue=4 |pages=252–263 |doi=10.1038/nrg2538 |pmid=19274049 }}</ref> The complete set of gene products and the interactions among them constitutes gene regulatory networks (GRN). GRNs regulate the levels of gene products within the cell and in-turn the cellular processes.

GRNs are represented with genes and transcriptional factors as nodes and the relationship between them as edges. These edges are directional, representing the regulatory relationship between the two ends of the edge. For example, the directed edge from gene A to gene B indicates that A regulates the expression of B. Thus, these directional edges can not only represent the promotion of gene regulation but also its inhibition.

GRNs are usually constructed by utilizing the gene regulation knowledge available from databases such as., Reactome and KEGG. High-throughput measurement technologies, such as microarray, RNA-Seq, ChIP-chip, and ChIP-seq, enabled the accumulation of large-scale transcriptomics data, which could help in understanding the complex gene regulation patterns.<ref name="JiaXu2017">{{cite journal|last1=Jia|first1=Bochao|last2=Xu|first2=Suwa|last3=Xiao|first3=Guanghua|last4=Lamba|first4=Vishal|last5=Liang|first5=Faming|title=Learning gene regulatory networks from next generation sequencing data|journal=Biometrics|volume=73|issue=4|year=2017|pages=1221–30 |doi=10.1111/biom.12682|pmid=28294287|pmc=6258556}}</ref><ref name="AngeliniCosta2014">{{cite journal|last1=Angelini|first1=Claudia|last2=Costa|first2=Valerio|title=Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA-seq data: statistical solutions to biological problems|journal=Frontiers in Cell and Developmental Biology|volume=2|year=2014|page=51 |doi=10.3389/fcell.2014.00051|pmid=25364758|pmc=4207007|doi-access=free}}</ref>

===Gene co-expression networks (transcript–transcript association networks)=== {{main article| Gene co-expression 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.<ref name="ZhengChen2021">{{cite journal|last1=Zheng|first1=Peng-Fei|last2=Chen|first2=Lu-Zhu|last3=Guan|first3=Yao-Zong|last4=Liu|first4=Peng|title=Weighted gene co-expression network analysis identifies specific modules and hub genes related to coronary artery disease|journal=Scientific Reports|volume=11|issue=1|year=2021|page=6711 |doi=10.1038/s41598-021-86207-0|pmid=33758323|pmc=7988178|bibcode=2021NatSR..11.6711Z|doi-access=free}}</ref> Co-expression modules may correspond to cell types or pathways, while highly connected intramodular hubs can be interpreted as representatives of their respective modules.

===DNA-DNA chromatin networks=== thumb|DNA-DNA chromatin network of the mouse Hist1 region linked based on high normalized linkage disequilibrium thumb|right|This is the network hub representation of the Hist1 region of the mm9 mouse genome. The green nodes are the top 5 hubs based on centrality values while all the other nodes are linked to the node with the greatest linkage value in the corresponding adjacency matrix. The size of the nodes are based on its respective centrality values. thumb|right| This is the heat map representation of the Hist1 region of the mm9 mouse genome hubs. The range of values are calculated based on the Linkage Distribution values of the dataset in this region.

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 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.

In such networks, edge weights often correspond to the frequency or strength of interaction between loci, while network construction may involve filtering or thresholding to retain only strong interactions. Some examples of this may include filtering out certain gene locations, filtering based on quartile of closeness, or by expression as this can serve to reduce noise and highlight biologically meaningful relationships for interpretation.

The first graphic portrays the layout of the Hist1 region of the mm9 mouse genome, a large cluster of genes that encode for replication-dependant histones. The organization of the histone genes in this cluster have been found to be practically identical to that of the human Hist1 region.<ref>{{cite journal |last1=Marzluff |first1=William F. |last2=Gongidi |first2=Preetam |last3=Woods |first3=Keith R. |last4=Jin |first4=Jianping |last5=Maltais |first5=Lois J. |title=The human and mouse replication-dependent histone genes |journal=Genomics |date=November 2002 |volume=80 |issue=5 |pages=487–498 |url=https://pubmed.ncbi.nlm.nih.gov/12408966/ |issn=0888-7543}}</ref> The data used to develop this network graph was discovered through GAM.<ref name="Complex multi-enhancer contacts captured by GAM">{{cite journal |vauthors=Beagrie RA, Scialdone A, Schueler M, Kraemer DC, Chotalia M, Xie SQ, Barbieri M, de Santiago I, Lavitas LM, Branco MR, Fraser J, Dostie J, Game L, Dillon N, Edwards PA, Nicodemi M, Pombo A |title=Complex multi-enhancer contacts captured by genome architecture mapping |journal=Nature |volume=543 |issue=7646 |pages=519–524 |date=March 2017 |pmid=28273065 |pmc=5366070 |doi=10.1038/nature21411 |bibcode=2017Natur.543..519B }}</ref> Each node on the graph represents a genomic loci within the mouse genome. The edges between the nodes represent a linkage disequilibrium between the connected nodes greater than the average across all 81 genomic windows. The initial locations of the nodes within the graphic were randomly selected but the methodology of choosing edges shaped the graph into a rudimentary graphical representation of the placement of genomic loci throughout the Hist1 region.

Highly connected nodes in such chromatin interaction networks can be interpreted as hubs, and may be used to define communities of loci that interact more frequently with one another. These community structures reflect the modular organization commonly observed in biological and regulatory networks <ref>name="Topology and evolution of regulatory control networks">{{cite journal |vauthors=Yan KK, Gerstein M |title=Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks |journal=Proceedings of the National Academy of Sciences of the United States of America |volume=107 |issue=20 |pages=9186–9191 |date=May 2010 |pmid=20439753 |pmc=2889124 |doi=10.1073/pnas.0914771107 |bibcode=2010PNAS..107.9186Y }}</ref>. In hub-based approaches, nodes are assigned to the community of the hub with which they share the strongest interaction, often with constraints to ensure that each node belongs to only one community.

Such network representations are closely related to heat map visualizations, where interaction data are displayed as a matrix (adjacency matrix) in which each cell represents the interaction strength between two loci. Patterns observed in heat maps, such as dense blocks of high interaction, often correspond to communities identified in the network representation. These approaches enable combination of graph-based and matrix-based analyses of chromatin organization. This type of comparison can be seen in the graphics below where the heat map and network visualizations can be compared in such a manner.

===Metabolic networks=== {{main article|metabolic network}} thumb|This is a phosphorus-dependent metabolic network. Cells break down the food and nutrients into small molecules necessary for cellular processing through a series of biochemical reactions. These biochemical reactions are catalyzed by enzymes. The complete set of all these biochemical reactions in all the pathways represents the metabolic network. Within the metabolic network, the small molecules take the roles of nodes, and they could be either carbohydrates, lipids, or amino acids. The reactions which convert these small molecules from one form to another are represented as edges. It is possible to use network analyses to infer how selection acts on metabolic pathways.<ref name="Proulx">{{cite journal |last1=Proulx |first1=S |last2=Promislow |first2=D |last3=Phillips |first3=P |title=Network thinking in ecology and evolution |journal=Trends in Ecology & Evolution |date=June 2005 |volume=20 |issue=6 |pages=345–353 |doi=10.1016/j.tree.2005.04.004 |pmid=16701391 }}</ref>

===Signaling networks=== {{main article|Cell signaling}} thumb|Signaling network showing difference between traditional and network biological view 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 is transduced from the cell surface to the cell nucleus by a series of protein-protein interactions, phosphorylation reactions, and other events.<ref name="CargnelloRoux2011">{{cite journal|last1=Cargnello|first1=M.|last2=Roux|first2=P. P.|title=Activation and Function of the MAPKs and Their Substrates, the MAPK-Activated Protein Kinases|journal=Microbiology and Molecular Biology Reviews|volume=75|issue=1|year=2011|pages=50–83 |doi=10.1128/MMBR.00031-10|pmid=21372320|pmc=3063353|doi-access=free}}</ref> Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks.<ref name="SevimogluArga2014">{{cite journal|last1=Sevimoglu|first1=Tuba|last2=Arga|first2=Kazim Yalcin|title=The role of protein interaction networks in systems biomedicine|journal=Computational and Structural Biotechnology Journal|volume=11|issue=18|year=2014|pages=22–27 |doi=10.1016/j.csbj.2014.08.008|pmid=25379140|pmc=4212283|doi-access=free}}</ref><ref name="ArgaÖnsan2007">{{cite journal |last1=Arga |first1=K Yalçın |last2=Önsan |first2=Z İlsen |last3=Kırdar |first3=Betül | author3-link = Betül Kırdar |last4=Ülgen |first4=Kutlu Ö |last5=Nielsen |first5=Jens |title=Understanding signaling in yeast: Insights from network analysis |journal=Biotechnology and Bioengineering |date=August 2007 |volume=97 |issue=5 |pages=1246–1258 |doi=10.1002/bit.21317 |pmid=17252576 |bibcode=2007BiotB..97.1246A }}</ref> 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.<ref>{{cite journal |last1=Browaeys |first1=Robin |last2=Saelens |first2=Wouter |last3=Saeys |first3=Yvan |title=NicheNet: modeling intercellular communication by linking ligands to target genes |journal=Nature Methods |date=February 2020 |volume=17 |issue=2 |pages=159–162 |doi=10.1038/s41592-019-0667-5|pmid=31819264 |bibcode=2020NatCB..17..159B }}</ref>

===Neuronal networks=== {{main article| Biological neural network}} The complex interactions in the brain make it a perfect candidate to apply network theory. Neurons in the brain are deeply connected with one another, and this results in complex networks being present in the structural and functional aspects of the brain.<ref>{{cite journal |last1=Bullmore |first1=Ed |last2=Sporns |first2=Olaf |title=Complex brain networks: graph theoretical analysis of structural and functional systems |journal=Nature Reviews Neuroscience |date=March 2009 |volume=10 |issue=3 |pages=186–198 |doi=10.1038/nrn2575 |pmid=19190637 }}</ref> For instance, small-world network properties have been demonstrated in connections between cortical regions of the primate brain<ref>{{cite journal |last1=Stephan |first1=Klaas E. |last2=Hilgetag |first2=Claus–C. |last3=Burns |first3=Gully A. P. C. |last4=O'Neill |first4=Marc A. |last5=Young |first5=Malcolm P. |last6=Kotter |first6=Rolf |title=Computational analysis of functional connectivity between areas of primate cerebral cortex |journal=Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences |date=29 January 2000 |volume=355 |issue=1393 |pages=111–126 |doi=10.1098/rstb.2000.0552 |pmid=10703047 |pmc=1692715 }}</ref> or during swallowing in humans.<ref>{{cite journal |last1=Jestrović |first1=Iva |last2=Coyle |first2=James L. |last3=Perera |first3=Subashan |last4=Sejdić |first4=Ervin |title=Functional connectivity patterns of normal human swallowing: difference among various viscosity swallows in normal and chin-tuck head positions |journal=Brain Research |date=December 2016 |volume=1652 |pages=158–169 |doi=10.1016/j.brainres.2016.09.041 |pmid=27693396 |pmc=5102805 }}</ref> 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=== {{main article|Food web}} All organisms are connected through feeding interactions. If a species eats or is eaten by another species, they are connected in an intricate food web of predator and prey interactions. The stability of these interactions has been a long-standing question in ecology.<ref>{{cite journal |author= MacArthur, R.H. |author-link= Robert MacArthur|title= Fluctuations in animal populations and a measure of community stability |journal= Ecology |year= 1955 |volume= 36 |issue= 3 |pages= 533–6 |doi=10.2307/1929601|jstor= 1929601|bibcode= 1955Ecol...36..533M}}</ref> 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.<ref>{{cite journal |last1=Dunne |first1=Jennifer A. |last2=Williams |first2=Richard J. |last3=Martinez |first3=Neo D. |title=Network structure and biodiversity loss in food webs: robustness increases with connectance |journal=Ecology Letters |date=July 2002 |volume=5 |issue=4 |pages=558–567 |doi=10.1046/j.1461-0248.2002.00354.x |bibcode=2002EcolL...5..558D }}</ref> This is especially important considering the potential species loss due to global climate change.

=== Network medicine === thumb|Graphs of Functional, Disease, and Topological Modules. Network medicine is an emerging field that applies network principles to understand the molecular basis of human disease. <ref name="Barabasi2011">{{cite journal |last1=Barabási |first1=Albert-László |last2=Gulbahce |first2=Nazli |last3=Loscalzo |first3=Joseph |year=2011 |title=Network medicine: a network-based approach to human disease |journal=Nature Reviews Genetics |volume=12 |issue=1 |pages=56–68 |doi=10.1038/nrg2918}}</ref> Instead of focusing on single genes or proteins, network medicine examines how diseases arise from small changes in complex biological networks, including protein-protein interaction networks, gene regulatory networks, and metabolic pathways. <ref name="Barabasi2011" /> Within this framework, diseases are associated with specific “disease modules,” defined as groups of interconnected components whose collective dysfunction contributes to a pathological state. <ref name="Barabasi2011" /> This network based perspective enables the identification of disease associated genes, the analysis of relationships between different diseases, and the development of therapeutic strategies that target multiple components of a biological system rather than a single molecule. <ref name="Barabasi2011" /> Network medicine also supports approaches such as drug repurposing and the integration of large-scale omics data, providing a systems-level complement to traditional reductionist methods and contributing to advances in precision medicine. <ref name="Barabasi2011" />

===Between-species interaction networks=== In biology, pairwise interactions have historically been the focus of intense study. With the recent advances in network science, it has become possible to scale up pairwise interactions to include individuals of many species involved in many sets of interactions to understand the structure and function of larger ecological networks.<ref>{{cite journal |last1=Bascompte |first1=Jordi |title=Disentangling the Web of Life |journal=Science |date=24 July 2009 |volume=325 |issue=5939 |pages=416–419 |doi=10.1126/science.1170749 |pmid=19628856 |bibcode=2009Sci...325..416B }}</ref> The use of network analysis can allow for both the discovery and understanding of how these complex interactions link together within the system's network, a property that has previously been overlooked. This powerful tool allows for the study of various types of interactions (from competitive to cooperative) using the same general framework.<ref name="auto">{{cite journal |last1=Krause |first1=Jens |last2=Lusseau |first2=David |last3=James |first3=Richard |title=Animal social networks: an introduction |journal=Behavioral Ecology and Sociobiology |date=May 2009 |volume=63 |issue=7 |pages=967–973 |doi=10.1007/s00265-009-0747-0 |bibcode=2009BEcoS..63..967K }}</ref> For example, plant-pollinator interactions are mutually beneficial and often involve many different species of pollinators as well as many different species of plants. These interactions are critical to plant reproduction and thus the accumulation of resources at the base of the food chain for primary consumers, yet these interaction networks are threatened by anthropogenic change. The use of network analysis can illuminate how pollination networks work and may, in turn, inform conservation efforts.<ref name="Memmott, J., et al. 2004 2605-261">{{cite journal |last1=Memmott |first1=Jane |last2=Waser |first2=Nickolas M. |last3=Price |first3=Mary V. |title=Tolerance of pollination networks to species extinctions |journal=Proceedings of the Royal Society of London. Series B: Biological Sciences |date=22 December 2004 |volume=271 |issue=1557 |pages=2605–2611 |doi=10.1098/rspb.2004.2909 |pmid=15615687 |pmc=1691904 }}</ref> Within pollination networks, nestedness (i.e., specialists interact with a subset of species that generalists interact with), redundancy (i.e., most plants are pollinated by many pollinators), and modularity play a large role in network stability.<ref name="Memmott, J., et al. 2004 2605-261"/><ref name="Olesen, J., et al. 2007 19891–19896">{{cite journal |last1=Olesen |first1=Jens M. |last2=Bascompte |first2=Jordi |last3=Dupont |first3=Yoko L. |last4=Jordano |first4=Pedro |title=The modularity of pollination networks |journal=Proceedings of the National Academy of Sciences |date=11 December 2007 |volume=104 |issue=50 |pages=19891–19896 |doi=10.1073/pnas.0706375104 |doi-access=free |pmid=18056808 |pmc=2148393 |bibcode=2007PNAS..10419891O }}</ref> 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.<ref name="Olesen, J., et al. 2007 19891–19896"/> More generally, the structure of species interactions within an ecological network can tell us something about the diversity, richness, and robustness of the network.<ref>{{cite journal |last1=Campbell |first1=Veronik |last2=Murphy |first2=Grace |last3=Romanuk |first3=Tamara N. |title=Experimental design and the outcome and interpretation of diversity–stability relations |journal=Oikos |date=March 2011 |volume=120 |issue=3 |pages=399–408 |doi=10.1111/j.1600-0706.2010.18768.x |bibcode=2011Oikos.120..399C }}</ref> Researchers can even compare current constructions of species interactions networks with historical reconstructions of ancient networks to determine how networks have changed over time.<ref>{{cite journal |last1=Lotze |first1=Heike K. |last2=Coll |first2=Marta |last3=Dunne |first3=Jennifer A. |title=Historical Changes in Marine Resources, Food-web Structure and Ecosystem Functioning in the Adriatic Sea, Mediterranean |journal=Ecosystems |date=March 2011 |volume=14 |issue=2 |pages=198–222 |doi=10.1007/s10021-010-9404-8 |bibcode=2011Ecosy..14..198L }}</ref> 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.<ref>{{cite journal |last1=Romanuk |first1=Tamara N. |last2=Vogt |first2=Richard J. |last3=Young |first3=Angela |last4=Tuck |first4=Constance |last5=Carscallen |first5=Mather W. |title=Maintenance of Positive Diversity-Stability Relations along a Gradient of Environmental Stress |journal=PLOS ONE |date=27 April 2010 |volume=5 |issue=4 |article-number=e10378 |doi=10.1371/journal.pone.0010378 |doi-access=free |pmid=20436913 |pmc=2860506 |bibcode=2010PLoSO...510378R }}</ref>

===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.<ref>{{cite journal |last1=Croft |first1=Darren P. |last2=Krause |first2=Jens |last3=James |first3=Richard |title=Social networks in the guppy ( Poecilia reticulata ) |journal=Proceedings of the Royal Society of London. Series B: Biological Sciences |date=7 December 2004 |volume=271 |issue=suppl_6 |pages=S516-9 |doi=10.1098/rsbl.2004.0206 |pmid=15801620 |pmc=1810091 }}</ref> 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.<ref name="auto"/>

Researchers interested in ethology across many taxa, from insects to primates, are starting to incorporate network analysis into their research. Researchers interested in social insects (e.g., ants and bees) have used network analyses better to understand the division of labor, task allocation, and foraging optimization within colonies.<ref>{{cite journal |last1=Dornhaus |first1=Anna |last2=Klügl |first2=Franziska |last3=Oechslein |first3=Christoph |last4=Puppe |first4=Frank |last5=Chittka |first5=Lars |title=Benefits of recruitment in honey bees: effects of ecology and colony size in an individual-based model |journal=Behavioral Ecology |date=1 May 2006 |volume=17 |issue=3 |pages=336–344 |doi=10.1093/beheco/arj036 }}</ref><ref>{{cite journal |last1=Linksvayer |first1=Timothy A. |last2=Fewell |first2=Jennifer H. |last3=Gadau |first3=JüRgen |last4=Laubichler |first4=Manfred D. |title=Developmental Evolution in Social Insects: Regulatory Networks from Genes to Societies |journal=Journal of Experimental Zoology Part B: Molecular and Developmental Evolution |date=May 2012 |volume=318 |issue=3 |pages=159–169 |doi=10.1002/jez.b.22001 |pmid=22544713 |bibcode=2012JEZB..318..159L }}</ref><ref>{{cite journal |last1=Mullen |first1=R.J. |last2=Monekosso |first2=D. |last3=Barman |first3=S. |last4=Remagnino |first4=P. |title=A review of ant algorithms |journal=Expert Systems with Applications |date=August 2009 |volume=36 |issue=6 |pages=9608��9617 |doi=10.1016/j.eswa.2009.01.020 |url=https://durham-repository.worktribe.com/output/1222953 }}</ref> Other researchers are interested in how specific network properties at the group and/or population level can explain individual-level behaviors. Studies have demonstrated how animal social network structure can be influenced by factors ranging from characteristics of the environment to characteristics of the individual, such as developmental experience and personality. At the level of the individual, the patterning of social connections can be an important determinant of fitness, predicting both survival and reproductive success. At the population level, network structure can influence the patterning of ecological and evolutionary processes, such as frequency-dependent selection and disease and information transmission.<ref>{{cite journal |last1=Croft |first1=Darren P |last2=Darden |first2=Safi K |last3=Wey |first3=Tina W |title=Current directions in animal social networks |journal=Current Opinion in Behavioral Sciences |date=December 2016 |volume=12 |pages=52–58 |doi=10.1016/j.cobeha.2016.09.001 |hdl=10871/23348 |hdl-access=free }}</ref> 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).<ref>{{cite journal |last1=Ryder |first1=Thomas B |last2=McDonald |first2=David B |last3=Blake |first3=John G |last4=Parker |first4=Patricia G |last5=Loiselle |first5=Bette A |title=Social networks in the lek-mating wire-tailed manakin (''Pipra filicauda'') |journal=Proceedings of the Royal Society B: Biological Sciences |date=22 June 2008 |volume=275 |issue=1641 |pages=1367–1374 |doi=10.1098/rspb.2008.0205 |pmid=18381257 |pmc=2602714 |bibcode=2008PBioS.275.1367R }}</ref> In bottlenose dolphin groups, an individual's degree and betweenness centrality values may predict whether or not that individual will exhibit certain behaviors, like the use of side flopping and upside-down lobtailing to lead group traveling efforts; individuals with high betweenness values are more connected and can obtain more information, and thus are better suited to lead group travel and therefore tend to exhibit these signaling behaviors more than other group members.<ref>{{cite journal |last1=Lusseau |first1=David |title=Evidence for social role in a dolphin social network |journal=Evolutionary Ecology |date=May 2007 |volume=21 |issue=3 |pages=357–366 |doi=10.1007/s10682-006-9105-0 |arxiv=q-bio/0607048 |bibcode=2007EvEco..21..357L }}</ref>

Social network analysis can also be used to describe the social organization within a species more generally, which frequently reveals important proximate mechanisms promoting the use of certain behavioral strategies. These descriptions are frequently linked to ecological properties (e.g., resource distribution). For example, network analyses revealed subtle differences in the group dynamics of two related equid fission-fusion species, Grevy's zebra and onagers, living in variable environments; Grevy's zebras show distinct preferences in their association choices when they fission into smaller groups, whereas onagers do not.<ref>{{cite journal |last1=Sundaresan |first1=Siva R. |last2=Fischhoff |first2=Ilya R. |last3=Dushoff |first3=Jonathan |last4=Rubenstein |first4=Daniel I. |title=Network metrics reveal differences in social organization between two fission–fusion species, Grevy's zebra and onager |journal=Oecologia |date=5 January 2007 |volume=151 |issue=1 |pages=140–149 |doi=10.1007/s00442-006-0553-6 |pmid=16964497 |bibcode=2007Oecol.151..140S }}</ref> Similarly, researchers interested in primates have also utilized network analyses to compare social organizations across the diverse primate order, suggesting that using network measures (such as centrality, assortativity, modularity, and betweenness) may be useful in terms of explaining the types of social behaviors we see within certain groups and not others.<ref>{{cite journal |last1=Kasper |first1=Claudia |last2=Voelkl |first2=Bernhard |title=A social network analysis of primate groups |journal=Primates |date=October 2009 |volume=50 |issue=4 |pages=343–356 |doi=10.1007/s10329-009-0153-2 |pmid=19533270 }}</ref>

Finally, social network analysis can also reveal important fluctuations in animal behaviors across changing environments. For example, network analyses in female chacma baboons (''Papio hamadryas ursinus'') revealed important dynamic changes across seasons that were previously unknown; instead of creating stable, long-lasting social bonds with friends, baboons were found to exhibit more variable relationships which were dependent on short-term contingencies related to group-level dynamics as well as environmental variability.<ref>{{cite journal |last1=Henzi |first1=S. P. |last2=Lusseau |first2=D. |last3=Weingrill |first3=T. |last4=van Schaik |first4=C. P. |last5=Barrett |first5=L. |title=Cyclicity in the structure of female baboon social networks |journal=Behavioral Ecology and Sociobiology |date=May 2009 |volume=63 |issue=7 |pages=1015–1021 |doi=10.1007/s00265-009-0720-y |bibcode=2009BEcoS..63.1015H }}</ref> 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.<ref>{{cite journal |last1=Hunt |first1=Edmund R. |last2=Mi |first2=Brian |last3=Fernandez |first3=Camila |last4=Wong |first4=Brandyn M. |last5=Pruitt |first5=Jonathan N. |last6=Pinter-Wollman |first6=Noa |title=Social interactions shape individual and collective personality in social spiders |journal=Proceedings of the Royal Society B: Biological Sciences |date=12 September 2018 |volume=285 |issue=1886 |article-number=20181366 |doi=10.1098/rspb.2018.1366 |pmid=30185649 |pmc=6158534 }}</ref> This is a very small set of broad examples of how researchers can use network analysis to study animal behavior. Research in this area is currently expanding very rapidly, especially since the broader development of animal-borne tags and computer vision can be used to automate the collection of social associations.<ref>{{cite journal |last1=Krause |first1=Jens |last2=Krause |first2=Stefan |last3=Arlinghaus |first3=Robert |last4=Psorakis |first4=Ioannis |last5=Roberts |first5=Stephen |last6=Rutz |first6=Christian |title=Reality mining of animal social systems |journal=Trends in Ecology & Evolution |date=September 2013 |volume=28 |issue=9 |pages=541–551 |doi=10.1016/j.tree.2013.06.002 |pmid=23856617 |bibcode=2013TEcoE..28..541K }}</ref> 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.

== 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, computer science, and bioinformatics.

===Association=== thumb|upright=1.0|Using X and Y as variables, the first graph shows a very high correlation, the middle graph shows a fair correlation, and the third shows no/little correlation. 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 which specifically centers around the linear relationship between two variables.<ref>{{cite encyclopedia |first=Mark Gerard |last=Haug |title=measure of association |date= |encyclopedia= |publisher=Encyclopedia Britannica |url=https://www.britannica.com/topic/measure-of-association}}</ref> As an example, weighted gene co-expression network analysis uses Pearson correlation to analyze linked gene expression and understand genetics at a systems level.<ref>{{cite journal |last1=Zhang |first1=Bin |last2=Horvath |first2=Steve |title=A General Framework for Weighted Gene Co-Expression Network Analysis |journal=Statistical Applications in Genetics and Molecular Biology |date=12 January 2005 |volume=4 |issue=1 |pages= Article17|doi=10.2202/1544-6115.1128 |pmid=16646834 }}</ref> Another measure of correlation is linkage disequilibrium. Linkage disequilibrium describes the non-random association of genetic sequences among loci in a given chromosome.<ref>{{cite web |title=Linkage Disequilibrium |work=ISOGG Wiki |publisher=International Society of Genetic Genealogy |url=https://isogg.org/wiki/Linkage_disequilibrium}}</ref> An example of its use is in detecting relationships in GAM data across genomic intervals based upon detection frequencies of certain loci.<ref name="Complex multi-enhancer contacts captured by GAM"/>

===Centrality=== The concept of centrality can be extremely useful when analyzing biological network structures. There are many different methods to measure centrality such as degree, betweenness, closeness, 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.<ref>{{cite journal |last1=Peng |first1=Sancheng |last2=Zhou |first2=Yongmei |last3=Cao |first3=Lihong |last4=Yu |first4=Shui |last5=Niu |first5=Jianwei |last6=Jia |first6=Weijia |title=Influence analysis in social networks: A survey |journal=Journal of Network and Computer Applications |date=March 2018 |volume=106 |pages=17–32 |doi=10.1016/j.jnca.2018.01.005 }}</ref> In 2005, Researchers at Harvard Medical School 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.<ref>{{cite journal |vauthors=Joy MP, Brock A, Ingber DE, Huang S |title=High-betweenness proteins in the yeast protein interaction network |journal=J Biomed Biotechnol |volume=2005 |issue=2 |pages=96–103 |date=June 2005 |pmid=16046814 |pmc=1184047 |doi=10.1155/JBB.2005.96 |doi-access=free}}</ref>

These differing centrality measures reflect distinct structural roles of nodes within biological networks, including protein–protein interaction networks, gene regulatory networks, and metabolic networks.<ref>{{cite book |first=M. |last=Newman |title=Networks |publisher=Oxford University Press |date=2018 |pages=158–160 |isbn=978-0-19-880509-0 |doi=10.1093/oso/9780198805090.001.0001 }}</ref>

'''Degree centrality''' measures how many direct connections a node has. It is defined as thumb|center|Equation for degree centrality where ''ki'' is the number of nodes directly connected to node ''i'', and ''n'' is the total number of nodes in the network. In biological networks, nodes with high degree can be referred to as hubs and are associated with proteins or genes that participate in many interactions, contributing to core cellular functions.

'''Betweenness centrality''' measures the extent to which a node lies on shortest paths between other nodes. It is defined as thumb|center|Equation for betweenness centrality where ''σst'' is the total number of shortest paths between nodes ''s'' and ''t'', and ''σst(i)'' is the number of those paths that pass through node ''i''. Nodes with high betweenness centrality can connect different regions of a network and facilitate interactions between them.

'''Closeness centrality''' is based on the average shortest path distance from a node to all other nodes in the network. It is defined as thumb|center|Equation for closeness centrality where ''d(i,j)'' is the shortest path distance between nodes ''i'' and ''j'', and ''n'' is the total number of nodes. Nodes with high closeness centrality occupy central positions within the network and can interact with other nodes through relatively short paths, allowing efficient communication across the network.

'''Eigenvector centrality''' assigns scores to nodes based on the centrality of their neighbors. It is defined as thumb|center|Equation for Eigenvector centrality where ''Aij'' is the adjacency matrix (1 if nodes ''i'' and ''j'' are connected, 0 otherwise), ''CE(j)'' is the centrality of neighbor ''j'', and ''λ'' is a constant (the largest eigenvalue of the adjacency matrix). In biological networks, this measure identifies nodes that are connected to other highly connected or influential nodes and is used to detect key regulators within complex systems.

'''Katz centrality''' extends eigenvector centrality by incorporating both direct and indirect connections, with reduced influence assigned to longer paths. It is defined as thumb|center|Equation for Katz centrality where ''Aij'' tells you if node ''i'' is connected to node ''j'' (1 = yes, 0 = no), ''CK(j)'' is the score of node ''j'', ''α'' controls how much influence farther-away nodes have (smaller = less influence), and ''β'' gives every node a small base score. This measure accounts for the cumulative influence of a node across multiple steps in a network, which is relevant in multi-step biological processes.

These centrality measures provide complementary approaches for analyzing the structure and organization of biological networks.

===Communities=== thumb|upright=1.0|The graphic displays a food web of Secaucus High School Marsh without grouping/communities (left) and the food web with communities (right). 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.<ref>{{cite journal |last1=Porter |first1=Mason |last2=Onnela |first2=Jukka-Pekka |last3=Mucha |first3=Peter |title=Communities in Networks |journal=Notices of the AMS |date=2009 |volume=56 |issue=9 |pages=1082–1166 |url=https://www.ams.org/notices/200909/rtx090901082p.pdf }}</ref> 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 algorithms exist for creating these relationships.<ref>{{cite journal |last1=Javed |first1=Muhammad Aqib |last2=Younis |first2=Muhammad Shahzad |last3=Latif |first3=Siddique |last4=Qadir |first4=Junaid |last5=Baig |first5=Adeel |title=Community detection in networks: A multidisciplinary review |journal=Journal of Network and Computer Applications |date=April 2018 |volume=108 |pages=87–111 |doi=10.1016/j.jnca.2018.02.011 }}</ref> 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 of calculation. In 2002, a food web of marine mammals in the Chesapeake Bay 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.<ref>{{cite journal |vauthors=Girvan M, Newman ME |title=Community structure in social and biological networks |journal=Proc Natl Acad Sci U S A |volume=99 |issue=12 |pages=7821–6 |date=June 2002 |pmid=12060727 |pmc=122977 |doi=10.1073/pnas.122653799 |arxiv=cond-mat/0112110 |bibcode=2002PNAS...99.7821G |doi-access=free }}</ref> Two very common community detection algorithms for biological networks are the Louvain Method and Leiden Algorithm.

The Louvain method is a greedy algorithm that attempts to maximize modularity, 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.<ref>{{cite journal |vauthors=Markovitch O, Krasnogor N |title=Predicting species emergence in simulated complex pre-biotic networks |journal=PLOS ONE |volume=13 |issue=2 |article-number=e0192871 |date=2018 |pmid=29447212 |pmc=5813963 |doi=10.1371/journal.pone.0192871 |bibcode=2018PLoSO..1392871M |doi-access=free}}</ref><ref name="Louvain to Leiden">{{cite journal |vauthors=Traag VA, Waltman L, van Eck NJ |title=From Louvain to Leiden: guaranteeing well-connected communities |journal=Sci Rep |volume=9 |issue=1 |article-number=5233 |date=March 2019 |pmid=30914743 |pmc=6435756 |doi=10.1038/s41598-019-41695-z |arxiv=1810.08473 |bibcode=2019NatSR...9.5233T }}</ref> Once no modularity increase can occur by joining nodes to a community, a new weighted network 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.<ref>{{cite journal |last1=Ozaki |first1=Naoto |last2=Tezuka |first2=Hiroshi |last3=Inaba |first3=Mary |title=A Simple Acceleration Method for the Louvain Algorithm |journal=International Journal of Computer and Electrical Engineering |date=2016 |volume=8 |issue=3 |pages=207–218 |doi=10.17706/IJCEE.2016.8.3.207-218 }}</ref> 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.<ref name="Louvain to Leiden"/>

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.<ref name="Louvain to Leiden"/>

===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 where motif analysis is commonly used to understand interconnected neuronal functions at varying scales.<ref name="Network motifs in cellular neurophysiology">{{cite journal |vauthors=Mittal D, Narayanan R |title=Network motifs in cellular neurophysiology |journal=Trends Neurosci |volume=47 |issue=7 |pages=506–521 |date=July 2024 |pmid=38806296 |doi=10.1016/j.tins.2024.04.008 |url=https://mpra.ub.uni-muenchen.de/85266/1/MPRA_paper_85266.pdf }}</ref> As an example, in 2017, researchers at Beijing Normal University analyzed highly represented 2 and 3 node network motifs in directed functional brain networks constructed by Resting state fMRI data to study the basic mechanisms in brain information flow.<ref name="Topological motif patterns">{{cite journal |vauthors=Wei Y, Liao X, Yan C, He Y, Xia M |title=Identifying topological motif patterns of human brain functional networks |journal=Hum Brain Mapp |volume=38 |issue=5 |pages=2734–50 |date=May 2017 |pmid=28256774 |pmc=6866742 |doi=10.1002/hbm.23557 }}</ref>

==References== {{Reflist}}

== Books == {{refbegin}} *{{cite book |first=E. |last=Estrada |title=The Structure of Complex Networks: Theory and Applications |publisher=Oxford University Press |location= |date=2011 |isbn=978-0-199-59175-6 |doi=10.1093/acprof:oso/9780199591756.001.0001 |oclc=780445324}} *{{cite book |first1=J. |last1=Krause |first2=R. |last2=James |first3=D. |last3=Franks |first4=D. |last4=Croft |title=Animal Social Networks |publisher=Oxford University Press |location= |date=2015 |isbn=978-0-19-967904-1 |doi=10.1093/acprof:oso/9780199679041.001.0001 |oclc=900722740 }} *{{cite book |first=M. |last=Newman |title=Networks |publisher=Oxford University Press |location= |date=2018 |isbn=978-0-19-880509-0 |doi=10.1093/oso/9780198805090.001.0001 }} {{refend}}

==External links== * [https://web.archive.org/web/20110920220214/http://www.networkbio.org/ Networkbio.org], The site of the series of Integrative Network Biology (INB) meetings. For the 2012 event also see www.networkbio.org * [http://www.nettab.org Network Tools and Applications in Biology] (NETTAB) workshops. * [http://networkbiology.org Networkbiology.org], NetworkBiology wiki site. * [https://www.lindinglab.science/ 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. * [https://nrnb.org 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. *[https://networkrepository.com/asn.php Network Repository] The first interactive data and network data repository with real-time visual analytics. *[https://bansallab.github.io/asnr/ 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

Category:Biological techniques and tools Category:Bioinformatics Category:Systems biology Category:Networks