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Network medicine
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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 networks, such as protein-protein interactions and metabolic pathways, are utilized by network medicine. Disease networks, which map relationships between diseases and biological factors, also play an important role in the field. Epidemiology is extensively studied using network science as well; social networks and transportation networks are used to model the spreading of disease across populations. Network medicine is a medically focused area of systems biology.
The term "network medicine" was introduced by Albert-László Barabási in an the article "Network Medicine – From Obesity to the 'Diseasome'", published in The New England Journal of Medicine, in 2007. Barabási states that biological systems, similarly to social and technological systems, contain many components that are connected in complicated relationships but are organized by simple principles. Relaying on the tools and principles of network theory, the organizing principles can be analyzed by representing systems as complex networks, which are collections of nodes linked together by a particular biological or molecular relationship. For networks pertaining to medicine, nodes represent biological factors (biomolecules, diseases, phenotypes, etc.) and links (edges) represent their relationships (physical interactions, shared metabolic pathway, shared gene, shared trait, etc.).
Barabasi suggested that understanding human disease requires us to focus on three key networks, the metabolic network, the disease network, and the social network. The network medicine is based on the idea that understanding complexity of gene regulation, metabolic reactions, and protein-protein interactions and that representing these as complex networks will shed light on the causes and mechanisms of diseases. It is possible, for example, to infer a bipartite graph representing the connections of diseases to their associated genes using the OMIM database. The projection of the diseases, called the human disease network (HDN), is a network of diseases connected to each other if they share a common gene. Using the HDN, diseases can be classified and analyzed through the genetic relationships between them. Network medicine has proven to be a valuable tool in analyzing big biomedical data.
The whole set of molecular interactions in the human cell, also known as the interactome, can be used for disease identification and prevention. These networks have been technically classified as scale-free, disassortative, small-world networks, having a high betweenness centrality.
Protein-protein interactions have been mapped, using proteins as nodes and their interactions between each other as links. These maps utilize databases such as BioGRID and the Human Protein Reference Database. The metabolic network encompasses the biochemical reactions in metabolic pathways, connecting two metabolites if they are in the same pathway. Researchers have used databases such as KEGG to map these networks. Others networks include cell signaling networks, gene regulatory networks, and RNA networks.
Using interactome networks, one can discover and classify diseases, as well as develop treatments through knowledge of its associations and their role in the networks. One observation is that diseases can be classified not by their principle phenotypes (pathophenotype) but by their disease module, which is a neighborhood or group of components in the interactome that, if disrupted, results in a specific pathophenotype. Disease modules can be used in a variety of ways, such as predicting disease genes that have not been discovered yet. Therefore, network medicine looks to identify the disease module for a specific pathophenotype using clustering algorithms.
Human disease networks, also called the diseasome, are networks in which the nodes are diseases and the links, the strength of correlation between them. This correlation is commonly quantified based on associated cellular components that two diseases share. The first-published human disease network (HDN) looked at genes, finding that many of the disease associated genes are non-essential genes, as these are the genes that do not completely disrupt the network and are able to be passed down generations. Metabolic disease networks (MDN), in which two diseases are connected by a shared metabolite or metabolic pathway, have also been extensively studied and is especially relevant in the case of metabolic disorders.
Three representations of the diseasome are:
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Network medicine AI simulator
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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 networks, such as protein-protein interactions and metabolic pathways, are utilized by network medicine. Disease networks, which map relationships between diseases and biological factors, also play an important role in the field. Epidemiology is extensively studied using network science as well; social networks and transportation networks are used to model the spreading of disease across populations. Network medicine is a medically focused area of systems biology.
The term "network medicine" was introduced by Albert-László Barabási in an the article "Network Medicine – From Obesity to the 'Diseasome'", published in The New England Journal of Medicine, in 2007. Barabási states that biological systems, similarly to social and technological systems, contain many components that are connected in complicated relationships but are organized by simple principles. Relaying on the tools and principles of network theory, the organizing principles can be analyzed by representing systems as complex networks, which are collections of nodes linked together by a particular biological or molecular relationship. For networks pertaining to medicine, nodes represent biological factors (biomolecules, diseases, phenotypes, etc.) and links (edges) represent their relationships (physical interactions, shared metabolic pathway, shared gene, shared trait, etc.).
Barabasi suggested that understanding human disease requires us to focus on three key networks, the metabolic network, the disease network, and the social network. The network medicine is based on the idea that understanding complexity of gene regulation, metabolic reactions, and protein-protein interactions and that representing these as complex networks will shed light on the causes and mechanisms of diseases. It is possible, for example, to infer a bipartite graph representing the connections of diseases to their associated genes using the OMIM database. The projection of the diseases, called the human disease network (HDN), is a network of diseases connected to each other if they share a common gene. Using the HDN, diseases can be classified and analyzed through the genetic relationships between them. Network medicine has proven to be a valuable tool in analyzing big biomedical data.
The whole set of molecular interactions in the human cell, also known as the interactome, can be used for disease identification and prevention. These networks have been technically classified as scale-free, disassortative, small-world networks, having a high betweenness centrality.
Protein-protein interactions have been mapped, using proteins as nodes and their interactions between each other as links. These maps utilize databases such as BioGRID and the Human Protein Reference Database. The metabolic network encompasses the biochemical reactions in metabolic pathways, connecting two metabolites if they are in the same pathway. Researchers have used databases such as KEGG to map these networks. Others networks include cell signaling networks, gene regulatory networks, and RNA networks.
Using interactome networks, one can discover and classify diseases, as well as develop treatments through knowledge of its associations and their role in the networks. One observation is that diseases can be classified not by their principle phenotypes (pathophenotype) but by their disease module, which is a neighborhood or group of components in the interactome that, if disrupted, results in a specific pathophenotype. Disease modules can be used in a variety of ways, such as predicting disease genes that have not been discovered yet. Therefore, network medicine looks to identify the disease module for a specific pathophenotype using clustering algorithms.
Human disease networks, also called the diseasome, are networks in which the nodes are diseases and the links, the strength of correlation between them. This correlation is commonly quantified based on associated cellular components that two diseases share. The first-published human disease network (HDN) looked at genes, finding that many of the disease associated genes are non-essential genes, as these are the genes that do not completely disrupt the network and are able to be passed down generations. Metabolic disease networks (MDN), in which two diseases are connected by a shared metabolite or metabolic pathway, have also been extensively studied and is especially relevant in the case of metabolic disorders.
Three representations of the diseasome are: