The Emerging Paradigm of Network Medicine in the Study of Human Disease
By S. Y. Chan, Joseph Loscalzo. 2012
192 citations recorded by the source corpus.
The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of “network medicine” proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.
Subfields: Computational Theory and Mathematics, Molecular Biology
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Citations in the corpus
- EgoNet: identification of human disease ego-network modules (2014, background): “applies systems biology and network science methods to human disease [1-3].”
- Detecting subnetwork-level dynamic correlations (2017, background): “Integrating expression data with existing biological networks has proven to be an effective approach to reduce spurious findings and achieving more interpretable results (Barab asi, 2007; Barab asi et al., 2011; Chan and Loscalzo, 2012).”