Every student of physiology learns the concept of negative feedback, whether it be in terms of baroreceptors and blood pressure, the control of glucose metabolism by insulin, calcium regulation by parathyroid hormone, or sodium regulation of renin, angiotensin, and aldosterone. Due to the growing prevalence of diabetes and the pioneering identification of insulin as the major regulator of blood glucose by Banting and Best, many kineticists have developed dynamic models of insulin secretion and glucose dynamics (Bergman, 1989; Biermann, 1994; Gatewood et al., 1970; Segre et al., 1973). Part of the reason for the interest is the idea that the serious conditions that affect patients with diabetes (such as heart and kidney disease, retinal degeneration and blindness, loss of peripheral nerve function, and bouts of gangrene) may be ameliorated with better glycemic control (Bellomo et al., 1982). A highly pragmatic reason for this interest is not only to understand the distinctions among the different types of diabetes, but also to attain better blood glucose control by use of the artificial pancreas for detection of blood glucose and infusion of insulin (Abisser et al., 1974; Hauser et al., 1994).
Insulin Secretion Oral Glucose Tolerance Test Respiratory Quotient Good Glycemic Control Artificial Pancreas
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.
Abisser, A.M., B.S. Leibel, T.G. Ewart, Z. Dadovac, C.K. Botz, W. Zingg, H. Schipper, and R. Gander. “Clinical control of diabetes by the artificial pancreas.” Diabetes 23 (1974): 397–404.CrossRefGoogle Scholar
Bellomo, J., P. Bruneti, G. Calabrese, D. Mazotti, E. Sarti, and A. Vincenzi. “Optimal feedback glycaemia regulation in diabetics.” Med. Biol. Eng. Comp. 20 (1982): 329–335.CrossRefGoogle Scholar
Bergman, R.N. “Toward physiological understanding of glucose tolerance. Minimal model approach.” Diabetes 38 (1989): 11512–11527.CrossRefGoogle Scholar
Biermann, E. “Simulation of metabolic abonormalities of Type II diabetes mellitus by use of a personal computer.” Comp. Meth. Progr. Biomed. 41 (1984): 217–229.CrossRefGoogle Scholar
Edelstein-Keshet, L. Mathematical Models in Biology. New York: Random House/ Birkhauser Mathematics Series, 1988.Google Scholar
Gatewood, L.C., E.L. Ackerman, J.W. Rosevear, and G. Molnar, “Modeling blood glucose dynamics.” Behav. Sci. 15 (1970): 72–87.CrossRefPubMedGoogle Scholar
Hauser, T., L. Campbell, E. Kraegen, and D. Chisholm. “Glycemic response to an insulin dose change: computer simulator predictions vs. mean patient responses.” Diabetes Nutr. Metab. 7 (1994): 89–95.Google Scholar
Quon, M., and L. Campfield. “A mathematical model and computer simulation study of insulin receptor regulation.” J. Theor. Biol. 150(1991): 59–72.CrossRefPubMedGoogle Scholar
Segre, G., G.L. Turco, and G. Vercellone. “Modeling blood glucose and insulin kinetics in normal, diabetic, and obese subjects.” Diabetes 22 (1973): 94–103.CrossRefPubMedGoogle Scholar
Sturis, J., K.S. Polonsky, E. Mosekilde, and E. van Cauter. “Computer model for mechanisms underlying ultradian oscillations of insulin and glucose.” Am.J. Physiol. 260 (1991): E801–E809.PubMedGoogle Scholar