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 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. 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 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 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. 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.
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.
DNA-DNA chromatin networks Within a nucleus,
DNA is constantly in motion. Perpetual actions such as genome folding and Cohesin extrusion morph the shape of a genome in real time. The spatial location of strands of
chromatin 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 Network medicine is an emerging field that applies network principles to understand the molecular basis of human disease. 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. 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. 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. 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. 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. 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. 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. 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 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.
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. 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. 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. 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 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. == Modelling biological networks ==