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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Biological network | 2/5 | https://en.wikipedia.org/wiki/Biological_network | reference | science, encyclopedia | 2026-05-05T14:01:40.853976+00:00 | kb-cron |
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. The data used to develop this network graph was discovered through GAM. 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 . 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 ===
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.
=== 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 is transduced from the cell surface to the cell nucleus by a series of protein-protein interactions, phosphorylation reactions, and other events. Signaling networks typically integrate protein–protein interaction networks, gene regulatory networks, and metabolic networks. 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 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. For instance, small-world network 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 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.
=== Network Medicine ===