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http://dx.doi.org/10.3831/KPI.2015.18.020

Systems Biology - A Pivotal Research Methodology for Understanding the Mechanisms of Traditional Medicine  

Lee, Soojin (Department of Physiology, College of Korean Medicine, Sangji University)
Publication Information
Journal of Pharmacopuncture / v.18, no.3, 2015 , pp. 11-18 More about this Journal
Abstract
Objectives: Systems biology is a novel subject in the field of life science that aims at a systems' level understanding of biological systems. Because of the significant progress in high-throughput technologies and molecular biology, systems biology occupies an important place in research during the post-genome era. Methods: The characteristics of systems biology and its applicability to traditional medicine research have been discussed from three points of view: data and databases, network analysis and inference, and modeling and systems prediction. Results: The existing databases are mostly associated with medicinal herbs and their activities, but new databases reflecting clinical situations and platforms to extract, visualize and analyze data easily need to be constructed. Network pharmacology is a key element of systems biology, so addressing the multi-component, multi-target aspect of pharmacology is important. Studies of network pharmacology highlight the drug target network and network target. Mathematical modeling and simulation are just in their infancy, but mathematical modeling of dynamic biological processes is a central aspect of systems biology. Computational simulations allow structured systems and their functional properties to be understood and the effects of herbal medicines in clinical situations to be predicted. Conclusion: Systems biology based on a holistic approach is a pivotal research methodology for understanding the mechanisms of traditional medicine. If systems biology is to be incorporated into traditional medicine, computational technologies and holistic insights need to be integrated.
Keywords
computational modeling; multi-target drugs; network pharmacology; systems biology; traditional medicine;
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