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Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu


Quantitative and Stability Analysis of Three Time Delays in Glucose and Insulin Oscillations Profile using Artificial Pancreas

Journal of Applied Nonlinear Dynamics 8(3) (2019) 493--507 | DOI:10.5890/JAND.2019.09.011

Saloni Rathee, Nilam

Department of Applied Mathematics, Delhi Technological University, Delhi-110042, India

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Abstract

In the present paper, we extend our attempt of modeling the closed loop control of glucose concentration level by considering three time delays for the proper functioning of artificial pancreas. Several time delays exist in the glucose - insulin regulatory system, the time delays which we are considering in the present study are delay in insulin secretion, delay in inhibition in hepatic glucose production stimulated by insulin and delay in time taken by insulin to reach interstitial compartment to lower glucose level (i.e. glucose utilization delay or insulin action delay). None of the time delay is negligible. Our analytical and numerical results shows that periodic and sustained oscillations of glucose and insulin concentration exists for type 1 diabetic people and delay in insulin secretion may be one of the major possible reason behind the occurrence of ultradian oscillations. Range of all three time delays have been quantified from the simulation of present model, which may be proved very useful in better designing and improved functioning of artificial pancreas.

Acknowledgments

The authors are thankful to Delhi Technological University, Delhi for the financial support.

References

  1. [1]  Rao, G.S., Bajaj, J.S., and Rao, J.S. (1990), A mathematical model for insulin kinetics. II. Extension of the model to include response to oral glucose administration and application to insulin-dependent diabetes mellitus (IDDM), Journal of Theoretical Biology, 142, 473-483.
  2. [2]  Rathee, S. and Nilam (2015), Study of the effects of FFA and obesity on diabetes through numerical simulation of the mathematical model, Journal of Mathematics and System Science, 5, 252-261, DOI: 10.17265/2159- 5291/2015.06.004
  3. [3]  Bolie, V.W. (1961), Coefficients of normal blood glucose regulation, Journal of Applied Physiology, 16, 783- 788.
  4. [4]  Li, J., Kuang, Y., and Mason, C. (2006), Modeling the glucose-insulin regulatory system and ultradian insulin secretory oscillations with two time delays, Journal of Theoretical Biology, 242, 722-735.
  5. [5]  Sturis, J., Polonsky, K.S., Msekilde, E., and Cauter, E.V. (1991), Computer model for mechanisms underlying ultradian oscillations of insulin and glucose, American Journal of Physiology, 260, 801-809.
  6. [6]  Tolic, I.M., Mosekilde, E., and Sturis, J. (2000), Modeling the insulin-glucose feedback system: The significance of pulsatile insulin secretion, Journal of Theoretical Biology, 207, 361-375.
  7. [7]  Wang, H., Li, J., and Kuang, Y. (2007), Mathematical modeling and qualitative analysis of insulin therapies, Mathematical Bioscience, 210, 17-33.
  8. [8]  Rathee, S. and Nilam (2015), Quantitative analysis of time delays of glucose - insulin dynamics using artificial pancreas, Discrete and continuous dynamical sysyem : series B, 20, 3115-3129, DOI:10.3934/dcdsb.2015. 20.3115.
  9. [9]  Bergman, R.N., Ider, Y.Z., Bowden, C.R., and Cobelli, C. (1979), Quantitative estimation of insulin sensitivity, American Journal of Physiology, 236, E667-E677.
  10. [10]  Bergman, R.N., Phillips, L.S., and Cobelli, C. (1961), Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose, Journal of Clinical Investment, 68, 1456-1467.
  11. [11]  Li, J. and Kuang, Y. (2001), Analysis of IVGTT glucose-insulin interaction models with time delays, Discrete and continuous dynamical systems-series B, 1.
  12. [12]  Song, X., Huang, M., and Li, J. (2014), Modeling impulsive insulin delivery in insuin pump with time delays, Siam Journal of Applied Mathematics, Society for Industrial and Applied Mathematics, 74, 1763-1785.
  13. [13]  Nilam, Alexander, M.E., Mathur, R., Moghadas S.M., and Shivakumar, P.N. (2006), Modelling the effect of CSII on the control of glucose concentration in type 1diabetes, Applied mathematics and Computing.
  14. [14]  Huang, M., Li, J., Song, X., and Guo, H. (2012), Modeling impulsive injections of insuin: Towards artificial pancreas, Siam Journal of Applied mathematics, 72, 1524-1548.
  15. [15]  Simon, C. and Brandenberger, G. (2002), Ultradian oscillations of insulin secretion in humans, Diabetes, 51, 258-261.
  16. [16]  Bennett, S. (1993), A history of control engineering, IEE Control Engrg. Ser., 47, Peter Peregrinus, Ltd., London, 1930-1955.
  17. [17]  Bennett, D.L. and Gourley, S.A. (2004), Asymptotic properties of a delay differential equation model for the interaction of glucose with the plasma and interstitial insulin, Applied Mathematics and Computing, 151, 189-207.
  18. [18]  Fabietti, P.G., Canonico, V., Federici, M.O., Benedetti, M.M., and Sarti, E. (2006), Control oriented model of insulin and glucose dynamics in type 1 diabetics, Medical and Biological Engineering and Computation, 4, 69-78.
  19. [19]  Hovorka, Canonico, V., Chassin, L.J., Haueter, U., Massi-Benedetti, M., Orsini Federici, M., Pieber, T.R., Schaller, H.C., Schaupp, L., Vering, T., and Wilinska, M.E. (2004), Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes, Physiological Measurements, 25, 905-920.
  20. [20]  Kovachev, B.P., Breton, M., Man, C.D., and Cobelli, C. (2009), In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes, Journal of Diabetes and Science and Technology, 3, 44-55.
  21. [21]  Steil, G.M., Rebrin, K., and Mastrototaro, J.J. (2006), Metabolic modelling and the closed-loop insulin delivery problem, Diabetes Research and Clinical Practice, 74, 183-186.
  22. [22]  Steil, G.M. and Reifman, J. (2009),Mathematical modeling research to support the development of automated insulin-delivery systems, Journal of Diabetes Science and Technology, 3, 388-395.
  23. [23]  Steil, G.M., Hipszer, B., and Reifman, J. (2010), Update on mathematical modeling research to support the development of automated insulin delivery systems, Journal of Diabetes Science and Technology, 4, 759-769.
  24. [24]  De Gaetano, A. and Arino, O. (2000), Mathematical modelling of the intravenous glucose tolerance test, Journal of Mathematical Biology, 40, 136-168.
  25. [25]  Kovatchev, B.P. and Clarke, W.L. (2008), Peculiarities of the continuous glucose monitoring data stream and their impact on developing closed-loop control technology, Journal of Diabetes Science and Technology, 2, 158-163.
  26. [26]  Kulcu, E., Tamada, J.A., Reach, G., Potts, R.O., and Lesho, M.J. (2003), Physiological differences between interstitial glucose and blood glucose measured in human subject, Diabetes Care, 26, 2405-2409.
  27. [27]  Stout, P.J., Racchini, J.R., and Hilgers, M.E. (2004), A novel approach to mitigating the physiological lag between blood and interstitial fluid glucose measurements, Diabetes Technology and Therapeutics, 6, 635-644.
  28. [28]  Cobelli, C., Renard, E., and Kovatchev, B. (2011), Artificial pancreas: past, present, future, Diabetes, 60.
  29. [29]  Jiaxu, L. and Yang, K. (2007), Analysis of a model of the glucose-insulin regulatory system with two delays, Siam Journal on Applied mathematics, 67, 757-776.
  30. [30]  Ermentrout, B. (2002), Simulating, analyzing, and animating dynamical systems: a guide to XPPAUT for researchers and students. Philadelphia, PA: SIAM.
  31. [31]  Doedal, E.J., Champneys, A.R., Fairgrieve, T.F., Kuznetsov, Yu.A., Sandstede, B., and Wang, X.J. (1997), auto97: continuation and bifurcation software for ordinary differential equations (with HomCont), users guide, Concordia University, Montreal, Canada (http://indy. cs.concordia.ca).
  32. [32]  Dhooge, A., Govaerts, W., Kuznetsov, Yu.A., Mestrom, W., Riet, A.M., and Sautois, B. (2006), MATCONT and CL MATCONT : continuation toolboxes in MATLAB .
  33. [33]  Stefanovski, D., Moate, P.J., and Boston, R.C. (2003), WinSAAM: a windowsbased compartmental modeling system, Metabolism, 52, 1153-566.