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Network medicine

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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, mapping relationships of disease-associated cellular components and disease phenotypes (pathophenotypes), 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.

Background

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The term "network medicine" was first introduced in The New England Journal of Medicine in July 2007 by Albert-László Barabási in "Network Medicine - From Obesity to the "Diseasome"".[1] 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. Using the recent development of network theory, the organizing principles can be comprehensively analyzed by representing systems as complex networks, which are collections of nodes linked together by a particular 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.).[2]

Three key networks for understanding human disease are emphasized: the metabolic network, the disease network, and the social network. Barabási references the complexity of gene regulation, metabolic reactions, and protein-protein interactions and that representing as complex networks in the cell will shed light on the causes and mechanisms of disease. In April 2007, Goh et al. published a bipartite graph of diseases connected with their associated genes using the OMIM database.[3] 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. Lastly, he emphasizes that human-to-human interactions play a role in the spread of pathogens and network theory can be applied to social networks for epidemiology.

Relevant Areas

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Interactome

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The whole set of molecular interactions in the human cell, also known as the interactome, can be used for disease identification and prevention.[4] These networks have been classified as scale-free, disassortative, small-world networks, having a high betweenness centrality.[5]

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.[4] Therefore, network medicine looks to identify the disease module for a specific pathophenotype using clustering algorithms.

Diseasome

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Human disease networks, also called the diseasome, are networks in which the nodes are diseases and links are associated cellular components that two diseases share. One of these components are genes, as network medicine looks at essential genes and non-essential genes and how they relate to diseases. The human disease network (HDN) found 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. Disease modules can also be used to predict disease genes that have not been discovered yet. Three representations of the diseasome are:[5]

  • Shared gene formalism states that if a gene is linked to two different disease phenotypes, then the two diseases likely have a common genetic origin (genetic disorders).
  • Shared metabolic pathway formalism states that if a metabolic pathway is linked to two different diseases, then the two diseases likely have a shared metabolic origin (metabolic disorders).
  • Disease comorbidity formalism uses phenotypic disease networks (PDN), where two diseases are linked if the observed comorbidity between their phenotypes 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.

Networks of environmental and genetic etiological factors linked with shared diseases, called the "etiome", can be also used to assess the clustering of environmental factors in these networks and understand the role of the environment on the interactome.[6]

Pharmacology

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Network pharmacology is a developing field based in systems pharmacology that looks at the effect of drugs on both the interactome and the diseasome.[7] The drug-target network (DTN) can play an important role in understanding the mechanisms of action of approved and experimental drugs.[8] The network theory view of pharmaceuticals is of the drug's effect on the interactome, especially the region that the drug target occupies. Combination therapy for the treatment for a complex disease (polypharmacology) is suggested in this field since one active pharmaceutical ingredient (API) aimed at one target may not effect the entire disease module.[7] The concept of disease modules can be used to aid in drug discovery, drug design, and the development of biomarkers for disease detection.[2] There can be a variety of ways to identifying drugs using network pharmacology, such as the "guilt by association" method; if two diseases are treated by the same drug, a drug that treats one disease may treat the other.[9] Drug repurposing, drug-drug interactions and drug side-effects have also been studied in this field.[2]

Network Epidemics

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Network epidemics has been built by applying the science to existing epidemic models, as many transportation networks and social networks play a role in the spread of disease.[10] Social networks have been used to assess the role of social ties in the spread of obesity in populations.[11] Epidemic models and concepts, such as spreading and contact tracing, have been adapted to be used in network analysis.[12] Alessandro Vespignani, director of the Laboratory for the Modeling of Biological and Socio-technical Systems (MoBS) at Northeastern University, has modeled the progression of the Ebola virus epidemic in West Africa across countries and continents.[13][14]

Implementation

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The Channing Division of Network Medicine at Brigham and Women's Hospital was created in 2012 to study, reclassify, and develop treatments for complex diseases using network science and systems biology.[15] It focuses on three areas:

DZZOM, "'The Network Medicine Company'", offers solutions to the biopharmaceutical industry using network medicine.[17] Specifically, they focus on areas in network pharmacology, looking to identify novel disease genes and pathways, novel drug target-sets for multi-target action, and novel therapeutic modalities for existing drugs.[18] DZZOM also looks to use network approaches towards improving clinical trial design, identifying patient subpopulations, and enhancing the scaling-up of biosimilar production. This company implements software that co-founders Peter Csermely and Kristóf Szalay developed called Turbine, simulating definable dynamic algorithms on very large networks.[19]

Massachusetts Institute of Technology offers an undergraduate course called "Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics".[20] Also, Harvard 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.[21]

See also

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References

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  1. ^ Barabási, A. L. (2007). Network medicine—from obesity to the “diseasome”. New England Journal of Medicine, 357(4), 404-407.
  2. ^ a b c Chan, S. Y., & Loscalzo, J. (2012). The emerging paradigm of network medicine in the study of human disease. Circulation research, 111(3), 359-374.
  3. ^ 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. doi:10.1073/pnas.0701361104
  4. ^ a b Barabási, A. L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature Reviews Genetics, 12(1), 56-68.
  5. ^ a b Loscalzo, J., & Barabasi, A. L. (2011). Systems biology and the future of medicine. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(6), 619-627.
  6. ^ 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.
  7. ^ a b Hopkins, A. L. (2008). Network pharmacology: the next paradigm in drug discovery. Nature chemical biology, 4(11), 682-690.
  8. ^ Yıldırım, M. A., Goh, K. I., Cusick, M. E., Barabási, A. L., & Vidal, M. (2007). Drug—target network. Nature biotechnology, 25(10), 1119-1126.
  9. ^ 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.
  10. ^ Pastor-Satorras, R., & Vespignani, A. (2001). Epidemic spreading in scale-free networks. Physical review letters, 86(14), 3200.
  11. ^ 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.
  12. ^ Keeling, M. J., & Eames, K. T. (2005). Networks and epidemic models. Journal of the Royal Society Interface, 2(4), 295-307.
  13. ^ Gomes, M. F., Piontti, A. P., Rossi, L., Chao, D., Longini, I., Halloran, M. E., & Vespignani, A. (2014). Assessing the international spreading risk associated with the 2014 West African Ebola outbreak. PLOS Currents Outbreaks.
  14. ^ http://news.sciencemag.org/health/2014/08/disease-modelers-project-rapidly-rising-toll-ebola
  15. ^ http://brighamandwomens.org/research/depts/medicine/channing/default.aspx
  16. ^ https://connects.catalyst.harvard.edu/profiles/display/Person/119309
  17. ^ http://dzzom.com/
  18. ^ http://dzzom.com/solutions/
  19. ^ Szalay, K. Z., & Csermely, P. (2013). Perturbation centrality and Turbine: a novel centrality measure obtained using a versatile network dynamics tool. PloS one, 8(10), e78059.
  20. ^ http://ocw.mit.edu/courses/biology/7-343-network-medicine-using-systems-biology-and-signaling-networks-to-create-novel-cancer-therapeutics-fall-2012/index.htm
  21. ^ http://catalyst.harvard.edu/services/networkmedicine/
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