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each protein appears only once in the interactome, whereas in reality, one protein can occur in different contexts and different cellular locations. Such signaling modules are therapeutically best targeted at several sites, which is now the new and clinically applied definition of network pharmacology. To achieve higher than current precision, patients must not be selected solely on descriptive phenotypes but also based on diagnostics that detect the module dysregulation. Moreover, such mechanism-based network pharmacology has the advantage that each of the drugs used within one module is highly synergistic, which allows for reducing the doses of each drug, which then reduces the potential of these drugs acting on other proteins outside the module and hence the chance for unwanted side effects.
24:
322:, 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
525:
The development of organs and other biological systems can be modelled as network structures where the clinical (e.g., radiographic, functional) characteristics can be represented as nodes and the relationships between these characteristics are represented as the links among such nodes. Therefore, it
297:, 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
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in these networks and understand the role of the environment on the interactome. The human symptom-disease network (HSDN), published in June 2014, showed that the symptoms of disease and disease associated cellular components were strongly correlated and that diseases of the same categories tend to
516:
Recently, some researchers tended to represent medication use in form of networks. The nodes in these networks represent medications and the edges represent some sort of relationship between these medications. Cavallo et al. (2013) described the topology of a co-prescription network to demonstrate
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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
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which drug classes are most co-prescribed. Bazzoni et al. (2015) concluded that the DPNs of co-prescribed medications are dense, highly clustered, modular and assortative. Askar et al. (2021) created a network of the severe drug-drug interactions (DDIs) showing that it consisted of many clusters.
467:
have also been studied in this field. The next iteration of network pharmacology used entirely different disease definitions, defined as dysfunction in signaling modules derived from protein-protein interaction modules. The latter as well as the interactome had many conceptual shortcomings, e.g.,
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as well as the type of drug-drug interactions, thus can help design efficient and safe therapeutic strategies. In addition, the drug-target network (DTN) can play an important role in understanding the mechanisms of action of approved and experimental drugs. The network theory view of
454:
for disease detection. There can be a variety of ways to identifying drugs using network pharmacology; a simple example of this is the "guilt by association" method. This states if two diseases are treated by the same drug, a drug that treats one disease may treat the other.
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Schäfer, Samuel; Smelik, Martin; Sysoev, Oleg; Zhao, Yelin; Eklund, Desiré; Lilja, Sandra; Gustafsson, Mika; Heyn, Holger; Julia, Antonio; Kovács, István A.; Loscalzo, Joseph; Marsal, Sara; Zhang, Huan; Li, Xinxiu; Gawel, Danuta (20 March 2024).
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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
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Nogales C, Mamdouh ZM, List M, Kiel C, Casas AI, Schmidt HHHW. Network pharmacology: curing causal mechanisms instead of treating symptoms. Trends
Pharmacol Sci. 2022 Feb;43(2):136-150. doi: 10.1016/j.tips.2021.11.004. Epub 2021 Dec 9. PMID
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Rual, J. F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., ... & Vidal, M. (2005). Towards a proteome-scale map of the human protein–protein interaction network. Nature, 437(7062),
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Scala, A. Auconi, P., Scazzocchio, M., Caldarelli, G., McNamara, J., Franchi, L. (2014). Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony, New J. Phys. 16 115017
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Catalyst (The
Harvard Clinical and Translational Science Center) offers a three-day course entitled "Introduction to Network Medicine", open to clinical and science professionals with doctorate degrees.
146:, 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
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exceeds a predefined threshold. This does not look at the mechanism of action of diseases, but captures disease progression and how highly connected diseases correlate to higher mortality rates.
326:, 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
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Chiang, A. P., & Butte, A. J. (2009). Systematic evaluation of drug–disease relationships to identify leads for novel drug uses. Clinical
Pharmacology & Therapeutics, 86(5), 507–510.
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Goh, K. I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A. L. (2007). The human disease network. Proceedings of the
National Academy of Sciences, 104(21), 8685–8690.
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Roeland van Wijk et al., Non-monotonic dynamics and crosstalk in signaling pathways and their implications for pharmacology. Scientific
Reports 5:11376 (2015) doi: 10.1038/srep11376
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Hidalgo, C. A., Blumm, N., Barabási, A. L., & Christakis, N. A. (2009). A dynamic network approach for the study of human phenotypes. PLoS Computational
Biology, 5(4), e1000353.
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Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A. L. (2002). Hierarchical organization of modularity in metabolic networks. science, 297(5586), 1551–1555.
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Mehrad Babaei et al., Biochemical reaction network topology defines dose-dependent Drug–Drug interactions. Comput Biol Med 155:106584 (2023) doi: 10.1016/j.compbiomed.2023.106584
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Liu, Y. I., Wise, P. H., & Butte, A. J. (2009). The "etiome": identification and clustering of human disease etiological factors. BMC bioinformatics, 10(Suppl 2), S14.
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P. Auconi, G. Caldarelli, A. Scala, G. Ierardo, A. Polimeni (2011). A network approach to orthodontic diagnosis, Orthodontics and
Craniofacial Research 14, 189-197.
953:"scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases"
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Loscalzo, J., & Barabasi, A. L. (2011). Systems biology and the future of medicine. Wiley
Interdisciplinary Reviews: Systems Biology and Medicine, 3(6), 619–627.
162:, diseases, phenotypes, etc.) and links (edges) represent their relationships (physical interactions, shared metabolic pathway, shared gene, shared trait, etc.).
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Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New
England Journal of Medicine, 357(4), 370–379.
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Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature
Reviews Genetics, 12(1), 56–68.
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Braun, P., Rietman, E., & Vidal, M. (2008). Networking metabolites and diseases. Proceedings of the National Academy of Sciences, 105(29), 9849–9850.
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that looks at the effect of drugs on both the interactome and the diseasome. The topology of a biochemical reaction network determines the shape of drug
1063:, & Vespignani, A. (2014). Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLOS Currents Outbreaks.
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Chan, S. Y., & Loscalzo, J. (2012). The emerging paradigm of network medicine in the study of human disease. Circulation research, 111(3), 359–374.
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Bazzoni, Gianfranco (April 2015). "The Drug Prescription Network: A System-Level View of Drug Co-Prescription in Community-Dwelling Elderly People".
42:
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Yıldırım, M. A., Goh, K. I., Cusick, M. E., Barabási, A. L., & Vidal, M. (2007). Drug—target network. Nature Biotechnology, 25(10), 1119–1126.
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offers an undergraduate course called "Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics". Also,
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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
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linked together by a particular biological or molecular relationship. For networks pertaining to medicine, nodes represent biological factors (
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and social networks play a role in the spread of disease. Social networks have been used to assess the role of social ties in the spread of
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states that if a metabolic pathway is linked to two different diseases, then the two diseases likely have a shared metabolic origin (
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Pastor-Satorras, R., & Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical review letters, 86(14), 3200.
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states that if a gene is linked to two different disease phenotypes, then the two diseases likely have a common genetic origin (
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Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295–307.
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Some disease networks connect diseases to associated factors outside the human cell. Networks of environmental and genetic
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Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690.
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Zhou, X., Menche, J., Barabási, A. L., & Sharma, A. (2014). Human symptoms–disease network. Nature Communications, 5.
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442:(API) aimed at one target may not affect the entire disease module. The concept of disease modules can be used to aid in
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Pastor-Satorras, R., & Vespignani, A. (2002). Immunization of complex networks. Physical Review E, 65(3), 036104.
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are used to model the spreading of disease across populations. Network medicine is a medically focused area of
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1266:"Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics"
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Barabasi suggested that understanding human disease requires us to focus on three key networks, the
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Askar, Mohsen (June 2021). "An introduction to network analysis for studies of medication use".
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and their interactions between each other as links. These maps utilize databases such as
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is based on the effect of the drug in the interactome, especially the region that the
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uses phenotypic disease networks (PDN), where two diseases are linked if the observed
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towards identifying, preventing, and treating diseases. This field focuses on using
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Sonawane, Abhijeet R.; Weiss, Scott T.; Glass, Kimberly; Sharma, Amitabh (2019).
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linked with shared diseases, called the "etiome", can be also used to assess the
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Cavallo, Pierpaolo (February 2013). "Network analysis of drug prescriptions".
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for a complex disease (polypharmacology) is suggested in this field since one
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is possible to use networks to model how organ systems dynamically interact.
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The whole set of molecular interactions in the human cell, also known as the
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Network epidemics has been built by applying network science to existing
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if they are in the same pathway. Researchers have used databases such as
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was created in 2012 to study, reclassify, and develop treatments for
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in an the article "Network Medicine – From Obesity to the 'Diseasome
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Caldarelli G. (2007). Scale-Free Networks. Oxford University Press.
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form highly connected communities, with respect to their symptoms.
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representing the connections of diseases to their associated
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towards identifying diseases and developing medical drugs.
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focuses on complex respiratory diseases, specifically
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in large, long-term epidemiology studies, such as the
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and has been recently used to model the spread of the
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in populations. Epidemic models and concepts, such as
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is extensively studied using network science as well;
1284:"Introduction to Network Medicine – Harvard Catalyst"
766:"Network Medicine in the Age of Biomedical Big Data"
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1055:Gomes, M. F., Piontti, A. P., Rossi, L., Chao, D.,
500:policies, in order to implement strategies such as
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may be too technical for most readers to understand
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585:uses multidisciplinary approaches, including as
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271:to map these networks. Others networks include
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43:make it understandable to non-experts
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301:for a specific pathophenotype using
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359:Shared metabolic pathway formalism
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644:Glossary of graph theory
277:gene regulatory networks
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114:transportation networks
461:drug-drug interactions
320:Human disease networks
235:betweenness centrality
130:Albert-László Barabási
76:is the application of
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1132:Rejuvenation Research
770:Frontiers in Genetics
659:Human disease network
502:targeted immunization
396:environmental factors
347:Shared gene formalism
315:Human disease network
303:clustering algorithms
704:Systems pharmacology
563:Nurses' Health Study
419:systems pharmacology
409:Systems pharmacology
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388:etiological factors
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336:metabolic disorders
324:non-essential genes
179:metabolic reactions
90:Biological networks
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624:Biological network
261:metabolic pathways
144:biological systems
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1264:Dr. Michael Lee.
1173:(12): 2054–2061.
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51:November 2014
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31:This article
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694:Pharmacology
649:Graph theory
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559:metabolomics
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465:side-effects
415:pharmacology
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403:Pharmacology
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160:biomolecules
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106:Epidemiology
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1057:Longini, I.
664:Interactome
448:drug design
432:drug target
376:comorbidity
265:metabolites
219:interactome
213:Interactome
207:Interactome
1180:2106.00413
847:1173–1178.
716:References
480:, as many
452:biomarkers
434:occupies.
392:clustering
380:phenotypes
328:metabolite
289:phenotypes
283:networks.
275:networks,
223:scale-free
193:using the
124:Background
92:, such as
1205:235266038
1010:34895945.
979:1756-994X
963:(1): 42.
792:1664-8021
599:biomarker
490:spreading
463:and drug
309:Diseasome
1298:Category
1197:34226152
1152:25531938
1117:42462968
1109:23180729
997:38509600
988:10956347
810:31031797
617:See also
555:genomics
413:Network
251:and the
801:6470635
776:: 294.
610:Harvard
601:design.
486:obesity
249:BioGRID
37:Please
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577:asthma
299:module
279:, and
255:. The
181:, and
169:, the
1201:S2CID
1175:arXiv
1113:S2CID
553:uses
245:nodes
191:genes
156:nodes
1193:PMID
1148:PMID
1105:PMID
993:PMID
975:ISSN
806:PMID
788:ISSN
575:and
573:COPD
557:and
492:and
269:KEGG
195:OMIM
112:and
96:and
84:and
1185:doi
1140:doi
1097:doi
983:PMC
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796:PMC
778:doi
394:of
330:or
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