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Arthritis Rheum. 2009 Sep;60(9):2585-94. doi: 10.1002/art.24797.

Mathematical modeling of the circadian rhythm of key neuroendocrine-immune system players in rheumatoid arthritis: a systems biology approach.

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Systems Immunology, Frankfurt Institute for Advanced Studies, Ruth-Moufang-Strasse 1, Frankfurt/Main, Germany.



Healthy subjects and patients with rheumatoid arthritis (RA) exhibit circadian rhythms of the neuroendocrine-immune system. Understanding circadian dynamics is complex due to the nonlinear behavior of the neuroendocrine-immune network. This study was undertaken to seek and test a mathematical model for studying this network.


We established a quantitative computational model to simulate nonlinear interactions between key factors in the neuroendocrine-immune system, such as plasma tumor necrosis factor (TNF), plasma cortisol (and adrenal cholesterol store), and plasma noradrenaline (NA) (and presynaptic NA store).


The model was nicely fitted with measured reference data on healthy subjects and RA patients. Although the individual circadian pacemakers of cortisol, NA, and TNF were installed without a phase shift, the relative phase shift between these factors evolved as a consequence of the modeled network interactions. Combined long-term and short-term TNF increase (the "RA model") increased cortisol plasma levels for only a few days, and cholesterol stores started to become markedly depleted. This nicely demonstrated the phenomenon of inadequate cortisol secretion relative to plasma TNF levels, as a consequence of adrenal deficiency. Using the RA model, treatment with glucocorticoids between midnight and 2:00 AM was found to have the strongest inhibitory effect on TNF secretion, which supports recent studies on RA therapy. Long-term reduction of TNF levels by simulation of anti-TNF therapy normalized cholesterol stores under "RA" conditions.


These first in silico studies of the neuroendocrine-immune system in rheumatology demonstrate that computational biology in medicine, making use of large collections of experimental data, supports understanding of the pathophysiology of complex nonlinear systems.

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