J Rheum Dis.  2013 Dec;20(6):348-355. 10.4078/jrd.2013.20.6.348.

Systems Approach to Rheumatoid Arthritis

Affiliations
  • 1Division of Rheumatology, Department of Internal Medicine, St. Vincent's Hospital, The Catholic University of Korea, Suwon, Korea. wan725@catholic.ac.kr
  • 2Center for Systems Biology of Plant Senescence and Life History, Daegu Gyeongbuk Institute of Science & Technology, Daegu, Korea.

Abstract

Phenotypic characteristics of complex diseases such as rheumatoid arthritis are a consequence of interactions of genetic and environmental factors. Biomolecules closely interact with other molecular components and form functional modules, resulting in significant biologic action capability. While traditional biochemical research focuses on a single disease using narrowly constrained data, systems biology aims to interpret large volumes of highly complex and multilevel data obtained from high-through-put technologies to understand how biological systems function as a whole. Such a systems approach to complex diseases, so-called network medicine, can shape our comprehensive understanding of disease mechanisms by identifying modules temporally and spatially perturbed in the context of health and diseases. Given the unmet needs for diagnosis, monitoring, and treatment in rheumatoid arthritis, systems biology is obviously an emerging powerful tool to gain insight into disease mechanisms, study comorbidities, analyze therapeutic drugs and their targets, and discover novel network-based biomarkers.

Keyword

Systems biology; Network medicine; Rheumatoid arthritis

MeSH Terms

Arthritis, Rheumatoid*
Biomarkers
Comorbidity
Diagnosis
Systems Biology

Figure

  • Figure 1. Systems genetics and techniques for analysis of complex disease. NGS, next generation sequencing; 2D-PAGE, two-dimensional polyacrylamide gel electrophoresis; MALDI- TOF-MS, matrix-assisted laser desorption/ionization-time-of-fl ight mass spectrometry; CE-ESI-MS, capillary electrophoresis coupled with electrospray ionization mass spectrometry; NMR, nuclear magnetic resonance; GC-MS, gas chromatography-mass spectrometry; LC-MS, liquid chromatography-mass spectrometry.

  • Figure 2. Transformation of data into network matrix and its components.


Reference

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