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Discontinuity, Nonlinearity, and Complexity

Dimitry Volchenkov (editor), Dumitru Baleanu (editor)

Dimitry Volchenkov(editor)

Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409, USA

Email: dr.volchenkov@gmail.com

Dumitru Baleanu (editor)

Cankaya University, Ankara, Turkey; Institute of Space Sciences, Magurele-Bucharest, Romania

Email: dumitru.baleanu@gmail.com


Mathematical Model Analysis of Diabetes for Glucose-Insulin Interaction in Human Body

Discontinuity, Nonlinearity, and Complexity 13(1) (2024) 173--188 | DOI:10.5890/DNC.2024.03.013

Sonia Akter$^{1}$, Md Haider Ali Biswas$^{2}$, M. N. Srinivas$^{3}$, Kalyan Das $^{4}$

$^{1}$ Department of Electrical and Electronic Engineering, Gono University, Dhaka- 1344, Bangladesh

$^{2 }$ Mathematics Discipline, Khulna University, Khulna-9208, Bangladesh

$^{3 }$ Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore 632014, Tamilnadu, India

$^{4 }$ Department of Basic and Applied Sciences, National Institute of Food Technology Entrepreneurship and Management, HSIIDC Industrial Estate, Kundli-131 028, Haryana, India

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Abstract

This research work proposes a novel method for controlling diabetes blood glucose levels. For the research of Type 1 Diabetes, we proposed a novel mathematical model. The homeostasis related with endocrinal logical regulation of glucose and glycogen levels in the human body by the hormones insulin and glucagon is attempted to be incorporated into a therapeutically feasible mathematical model. All plasma glucose concentrations, generalized insulin, and plasma insulin concentrations are taken into consideration by the model. Steady state analysis of the model is discussed. Local stability of the proposed model has been discussed using Routh-Hurwitz criteria. Global steadiness is also discussed. The numerical solution depicts the difficult condition faced by diabetes patients. MATLAB was used to carry out computer simulations for the analytical findings. As diabetics as very sensitive disease which is highly interrelated with various elements, in this perception we carried out computer simulations of various elements (attributes) in the segment of sensitivity analysis.

References

  1. [1]  Bellen,A., and Zennaro M.(2003), Numerical Methods for Delay Differential Equations, Oxford University Press.
  2. [2]  OberleH., and Pesch H. (1981), Numerical treatment of delay differential equations by Hermite interpolation, Numerical Mathematics, 37, 235-255.
  3. [3]  Boutayeb, A., and Chetouani, A. (2006), A critical review of mathematical models and data used in diabetology, Biomedical engineering online, 5(1), 1-9.
  4. [4]  Thompson S., and Shampine L.F. (2001), Solving DDEs in Matlab, Applied Numerical mathematics 37, 441-458.
  5. [5]  Makroglou A., Li J., and Kuang Y. (2006), Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview, Applied Numerical Mathematics, 56, 559-573.
  6. [6]  Wang H., Li J., and Kuang Y. (2007), Mathematical modeling and qualitative analysis of insulin therapies, Mathematical Biosciences, 210, 17-33.
  7. [7]  Tolic I., Mosekilde E., and Sturis J. (2000), Modelling the Insulin-Glucose Feedback System: The Significance of Pulsatile Insulin Secretion, Journal of Theoretical Biology, 207, 361-375.
  8. [8]  Bennett D., and Gourley S. (2004), Asymptotic properties of a delay differential equation model for the interaction of glucose with plasma and interstitial insulin, Applied Mathematics and Computation, 151, 189-207.
  9. [9]  Engelborghs, K., Lemaire, V., Belair, J., and Roose, D. (2001), Numerical bifurcation analysis of delay differential equations arising from physiological modeling. Journal of mathematical biology, 42(4), 361-385.
  10. [10]  Li J., Kuang Y., and Mason C. (2006), Modelling the glucose-insulin regulatory system and ultradian insulin oscillations with two explicit time delays, Journal of Theoretical Biology 242, 722-735.
  11. [11]  Wille D., and Baker C.T.H. DELSOL (1992), -A numerical code for the solution of a system of delay differential equations, Applied Numerical Mathematics, 9, 223-234.
  12. [12]  Biswas, M. H. A. (2013), Necessary Conditions for Optimal Control Problems with State Constraints: Theory and Applications. PhD Thesis, University of Porto, Porto.
  13. [13]  Biswas, M. H. A. (2012), Model and Control Strategy of the Deadly Nipah Virus (NiV) Infections in Bangladesh. Research \& Reviews in Biosciences, 6, 370-377.
  14. [14]  Biswas, M. H. A. (2014), On the Evolution of AIDS/HIV Treatment: An Optimal Control Approach. Current HIV Research, 12, 1-12.
  15. [15]  Tarin, C., Teufel, E., Picó, J., Bondia, J., and Pfleiderer, H. J. (2005), Comprehensive pharmacokinetic model of insulin glargine and other insulin formulations. IEEE Transactions on Biomedical Engineering, 52(12), 1994-2005.
  16. [16]  Wang,H., Li, J. and Kuang, Y.(2009), Enhanced modeling of the glucose-insulin system and its applications in insulin therapies, J. of Biological Systems, 3, 22-38.
  17. [17]  Bortolon, L. N. M., Triz, L. D. P. L., de Souza Faustino, B., de Sá, L. B. C., Rocha, D. R. T. W., and Arbex, A. K. (2016), Gestational diabetes mellitus: new diagnostic criteria. Open Journal of Endocrine and Metabolic Diseases, 6(1), 13-19.
  18. [18]  Caleb L., Adams, D., Glenn Lasseigne, (2018), An extensible mathematical model of glucose metabolism. Part I: the basic glucose-insulin-glucagon model, basal conditions and basic dynamics, Letters in Biomathematics, 5(1), 100-120.
  19. [19]  Yadav, R. (2020), A Mathematical Model For The Study Of Diabetes Mellitus. In Journal of Physics: Conference Series, 1531(1), 012078. IOP Publishing.
  20. [20]  Sandhya and Deepak Kumar, (2011), Mathematical Model for Glucose-Insulin Regulatory System of Diabetes Mellitus, Advances in Applied Mathematical Biosciences, 2(1), 39-46.
  21. [21]  Stuart M. Furler, M., Biomed.e., Edward W., Kraegen, Robert H. (1985), Smallwood and Donald j. Chisholm, F.R.A.C.P, Blood glucose control by intermittent loop closure in the basal mode: Computer Simulation Studies with a Diabetic model, Diabetes Care, 8(6), 553-561.
  22. [22]  Sanchaikumar, N., Komahan, G. Dr.A.Rajkumar , (2019), Mathematical modelling on $\beta$-Cell Mass, Insulin, Glucose Dynamics and glucose-insulin-glucagon interaction models: Effect of Genetic Predisposition to Diabetes. Compliance Engineering Journal, 10(9), 298-308.
  23. [23]  Campfield, L. A., and Smith, F. J. (2003), Blood glucose dynamics and control of meal initiation: a pattern detection and recognition theory. Physiological reviews, 83(1), 25-58.
  24. [24]  Nielsen, K. H., Pociot, F. M., and Ottesen, J. T. (2014), Bifurcation analysis of an existing mathematical model reveals novel treatment strategies and suggests potential cure for type 1 diabetes. Mathematical medicine and biology: a journal of the IMA, 31(3), 205-225.
  25. [25]  Kouidere, A., Labzai, A., Ferjouchia, H., Balatif, O., and Rachik, M. (2020), A new mathematical modeling with optimal control strategy for the dynamics of population of diabetics and its complications with effect of behavioral factors. Journal of Applied Mathematics, 2020, Article ID 1943410, 12 pages.
  26. [26]  Ajmera, I., Swat, M., Laibe, C., Le Novere, N. and Chelliah, V. (2013), The impact of mathematical modeling on the understanding of diabetes and related complications. CPT: pharmacometrics and systems pharmacology, 2(7), 1-14.
  27. [27]  Liu, W.J. and Tang, F.S. (2008), Modeling a simplified regulatory system of blood glucose at molecular levels, Journal of Theoretical Biology, 252, 608-620.
  28. [28]  Das, K., Srinivas, M. N., Srinivas, M. A. S., and Gazi, N. H. (2012), Chaotic dynamics of a three species prey--predator competition model with bionomic harvesting due to delayed environmental noise as external driving force.~Comptes Rendus Biologies,~335(8), 503-513.
  29. [29]  Das, K., Reddy, K. S., Srinivas, M. N., and Gazi, N. H. (2014), Chaotic dynamics of a three species prey--predator competition model with noise in ecology.~Applied Mathematics and Computation,~231, 117-133.
  30. [30]  Das, K., Srinivas, M. N., Kumar, G. R., and Hooda, M. D. (2021), Qualitative analysis of a generalist prey-predator model with time delay, Journal of MESA,~12(2), 615-630.
  31. [31]  Das, K., Srinivas, M. N., Madhusudanan, V., and Pinelas, S. (2019), Mathematical analysis of a prey--predator system: An adaptive back-stepping control and stochastic approach, Mathematical and Computational Applications, 24(1), 22, 1-20.