Dynamical systems approaches to personalized medicine

• Type of Material: Article
• Authors: Jacob D. Davis, Georgia Institute of Technology; Carla M. Kumbale, Georgia Institute of Technology; Qiang Zhang, Emory University; Eberhard Voit, Emory University
• Language: English
• Date: 2019-08-01
• Publisher: Current Biology Ltd.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 Elsevier Ltd.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.copbio.2019.03.005
• Title of Journal or Parent Work: Current Opinion in Biotechnology
• Volume: 58
• Start Page: 168
• End Page: 174
• Grant/Funding Information: This work was supported in part by the following grants: NSF-MCB-1517588 (PI: EOV), NSF-MCB-1615373 (PI: Diana Downs), NIH-2P30ES019776-05 (PI: Carmen Marsit) and NIH-P42ES04911 (PI: Norbert E. Kaminski).
• Keywords: Biotechnology & Applied Microbiology; Science & Technology; Health; Biochemistry & Molecular Biology; Models; Cancer; Biology; Life Sciences & Biomedicine; Biochemical Research Methods
• Research Categories: Engineering, Biomedical; Biology, Molecular; Chemistry, Biochemistry

Abstract

The complexity of the human body is a major roadblock to diagnosis and treatment of disease. Individuals may be diagnosed with the same disease but exhibit different biomarker profiles or physiological changes and, importantly, they may respond differently to the same risk factors and the same treatment. There is no doubt that computational methods of data analysis and interpretation must be developed for medicine to evolve from the traditional population-based approaches to personalized treatment strategies. We discuss how computational systems biology is contributing to this current evolution.