Glucostasis Simulator: An Educational Tool for Visualizing Glucose-Insulin Feedback Using the Clinically Accepted Hovorka Model
Keywords:
Hovorka Model, Negative Feedback Control, Glucose-insulin dynamics, Diabetes, GlucostasisAbstract
Effective diabetes management requires a fundamental understanding of glucose-insulin dynamics and their regulation through physiological feedback systems. This paper presents an interactive simulation tool, developed in MATLAB App Designer and grounded in the clinically validated Hovorka model, to visualize these complex mechanisms. The simulator was tested across key clinical scenarios including baseline, hyperglycemia, and insulin dosing errors, with results demonstrating physiologically accurate glucose and insulin trajectories that confirm the model's successful implementation. To enhance its educational utility, the tool incorporates myth versus fact flashcards and dynamic visual aids. By bridging theoretical knowledge and practical insight, this work provides a foundational platform for exploring physiological control systems, with potential to inform the future development of personalized diabetes management strategies.
References
Ahrén, B., & Pacini, G. (2021). Glucose effectiveness: Lessons from studies on insulin‐independent glucose clearance in mice. Journal of Diabetes Investigation, 12(5), 675–685. https://doi.org/10.1111/jdi.13446
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979). Quantitative estimation of insulin sensitivity. American Journal of Physiology-Endocrinology and Metabolism, 236(6), E667. https://doi.org/10.1152/ajpendo.1979.236.6.E667
Cobelli, C., Renard, E., & Kovatchev, B. (2011). Artificial Pancreas: Past, Present, Future. Diabetes, 60(11), 2672–2682. https://doi.org/10.2337/db11-0654
Dimitriadis, G. D., Maratou, E., Kountouri, A., Board, M., & Lambadiari, V. (2021). Regulation of Postabsorptive and Postprandial Glucose Metabolism by Insulin-Dependent and Insulin-Independent Mechanisms: An Integrative Approach. Nutrients, 13(1), 159. https://doi.org/10.3390/nu13010159
Eizirik, D. L., Szymczak, F., & Mallone, R. (2023). Why does the immune system destroy pancreatic β-cells but not α-cells in type 1 diabetes? Nature Reviews Endocrinology, 19(7), 425–434. https://doi.org/10.1038/s41574-023-00826-3
Gallardo-Hernández, A. G., González-Olvera, M. A., Castellanos-Fuentes, M., Escobar, J., Revilla-Monsalve, C., Hernandez-Perez, A. L., & Leder, R. (2022). Minimally-Invasive and Efficient Method to Accurately Fit the Bergman Minimal Model to Diabetes Type 2. Cellular and Molecular Bioengineering, 15(3), 267–279. https://doi.org/10.1007/s12195-022-00719-x
Hovorka, R., Canonico, V., Chassin, L. J., Haueter, U., Massi-Benedetti, M., Federici, M. O., Pieber, T. R., Schaller, H. C., Schaupp, L., Vering, T., & Wilinska, M. E. (2004). Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurement, 25(4), 905–920. https://doi.org/10.1088/0967-3334/25/4/010
Huising, M. O. (2020). Paracrine regulation of insulin secretion. Diabetologia, 63(10), 2057–2063. https://doi.org/10.1007/s00125-020-05213-5
Kang, S. L., Hwang, Y. N., Kwon, J. Y., & Kim, S. M. (2022). Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis. Diabetology & Metabolic Syndrome, 14(1), 187. https://doi.org/10.1186/s13098-022-00962-2
Manne-Goehler, J., Geldsetzer, P., Agoudavi, K., Andall-Brereton, G., Aryal, K. K., Bicaba, B. W., Bovet, P., Brian, G., Dorobantu, M., Gathecha, G., Singh Gurung, M., Guwatudde, D., Msaidie, M., Houehanou, C., Houinato, D., Jorgensen, J. M. A., Kagaruki, G. B., Karki, K. B., Labadarios, D., … Jaacks, L. M. (2019). Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys. PLOS Medicine, 16(3), e1002751. https://doi.org/10.1371/journal.pmed.1002751
Palumbo, P., Ditlevsen, S., Bertuzzi, A., & De Gaetano, A. (2013). Mathematical modeling of the glucose–insulin system: A review. Mathematical Biosciences, 244(2), 69–81. https://doi.org/10.1016/j.mbs.2013.05.006
Ponsiglione, A. M., Montefusco, F., Donisi, L., Tedesco, A., Cosentino, C., Merola, A., Romano, M., & Amato, F. (2023). A General Approach for the Modelling of Negative Feedback Physiological Control Systems. Bioengineering, 10(7), 835. https://doi.org/10.3390/bioengineering10070835
Shi, D., Dassau, E., & Doyle, F. J. (2019). Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties. IEEE Transactions on Biomedical Engineering, 66(4), 1045–1054. https://doi.org/10.1109/TBME.2018.2866392
Subramanian S, & Baidal D. (2021). The Management of Type 1 Diabetes. (Book) https://www.ncbi.nlm.nih.gov/books/NBK279114/
Tomita, T. (2017). Apoptosis of pancreatic β-cells in Type 1 diabetes. Biomolecules and Biomedicine, 17(3), 183–193. https://doi.org/10.17305/bjbms.2017.1961







