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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Biological network | 5/5 | https://en.wikipedia.org/wiki/Biological_network | reference | science, encyclopedia | 2026-05-05T14:01:40.853976+00:00 | kb-cron |
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
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
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
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
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 ===
Studying the community structure of a network by subdividing groups of nodes into like-regions can be an integral tool for bioinformatics when exploring data as a network. A food web of The Secaucus High School 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. 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. 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. 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. 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 where motif analysis is commonly used to understand interconnected neuronal functions at varying scales. 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.
== See also == List of omics topics in biology Biological network inference Biostatistics Cellular model Computational biology Systems biology Weighted correlation network analysis Interactome Network medicine Ecological network
== References ==
== Books ==
== External links == Networkbio.org, The site of the series of Integrative Network Biology (INB) meetings. For the 2012 event also see www.networkbio.org Network Tools and Applications in Biology (NETTAB) workshops. Networkbiology.org, NetworkBiology wiki site. Linding Lab, Technical University of Denmark (DTU) studies Network Biology and Cellular Information Processing, and is also organizing the Denmark branch of the annual "Integrative Network Biology and Cancer" symposium series. NRNB.org, The National Resource for Network Biology. A US National Institute of Health (NIH) Biomedical Technology Research Center dedicated to the study of biological networks. Network Repository The first interactive data and network data repository with real-time visual analytics. Animal Social Network Repository (ASNR) The first multi-taxonomic repository that collates 790 social networks from more than 45 species, including those of mammals, reptiles, fish, birds, and insects