Simulation of glucose regulating mechanism with an agent-based software engineering tool

Authors

DOI:

https://doi.org/10.11121/ijocta.01.2019.00685

Keywords:

Agent, agent-based modeling and simulation, homeostasis, negative feedback control, blood glucose levels

Abstract

This study provides a detailed explanation of a regulating mechanism of the blood glucose levels by an agent-based software engineering tool. Repast Simphony which is used in implementation of this study is an agent-based software engineering tool based on the object-oriented programming using Java language. Agent-based modeling and simulation is a computational methodology for simulating and exploring phenomena that includes a large set of active components represented by agents. The agents are main components situated in space and time of agent-based simulation environment. In this study, we present hormonal regulation of blood glucose levels by our improved agent-based control mechanism. Hormonal regulation of blood glucose levels is an important process to maintain homeostasis inside the human body. We offer a negative feedback control mechanism with agent-based modeling approach to regulate the secretion of insulin hormone which is responsible for increasing the blood glucose levels. The negative feedback control mechanism run by three main agents that interact with each other to perform their local actions in the simulation environment. The result of this study shows the local behavior of the agents in the negative feedback loop and illustrates how to balance the blood glucose levels. Finally, this study which is thought a potential implementation of agent-based modeling and simulation may contribute to the exploration of other homeostatic control systems inside the human body.

Downloads

Download data is not yet available.

References

Jhonstone, K., & Adam, K. (2012). The Human: As a Biological System. Core Body of Knowledge for the Generalist OHS Professional. Safety Institute of Australia Ltd, Tullamarine, Victoria, Australia

Marieb, E.N., & Hoehn, K. (2010). Human Anatomy and Physiology. 8th ed., San Francisco: Benjamin Cummings, 634-654.

Guyton, A.C., & Hall J.E. (2006). Textbook of Medical Physiology. Elseiver Inc, 11th ed.

Klügl, F. & Bazzan, A.L.C. (2012). Agent-Based Modeling and Simulation. Association for the Advancement of Artificial Intelligence, 29-40.

Bandini, S., Manzoni, S.T., & Vizzari, G. (2009). Agent Based Modeling and Simulation: An Informatics Perspective. Journal of Artificial Societies and Social Simulation, vol. 12.

Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 99(3), 7280-7287.

Bora, Ş., Evren, V., Emek, S., & Çakırlar, I. (2017). Agent-based modeling and simulation of blood vessels in the cardiovascular system. Simulation: Transactions of the Society for Modeling and Simulation International, 1-16. Doi: 10.1177/0037549717712602

DeAngelis, D.L., & Grimm V. (2014). Individual-based models in ecology after four decades. F1000Prime Reports; 6:39. DOI: 10. 12703/P6-39

Macal, C.M., & North, M.J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4:151-162. DOI: 10.1057/jos.2010.3

Ramaprased, R. (1983). On the Definition of Feedback. Behavioral Science, 28(1):4-13.

Nikolai, C., & Madey, G. (2009). Tools of the trade: a survey of various agent based modeling platforms. J. Artif. Soc. Social Simul., vol. 12.

James, P., & McFadden R. (2004). Understanding the processes behind the regulation of blood glucose. Diabetes Knowledge, 100(16):56-58.

Bora, Ş., Emek, S., Evren, V. (2017). An Agent-based Approach in Homeostatic Control Systems: Thermoregulation. IEEE 9th International Conference on Computational Intelligence and Communication Networks, 113-116. DOI: 10.1109/CICN.2017.8319367

Wang, X., Misava, R., Zielinski, M.C., Cowen, P., Jo, J., Periwal, V., Ricordi, C., Khan, A., Szust, J., Shen, J., Millis, J.M., Witkowski, P., & Hara, M. (2013). Regional Differences in Islet Distribution in the Human Pancreas - Preferential Beta-Cell Loss in the Head Region in Patients with Type 2 Diabetes. PloS One, vol. 8(6).

Krull, D.L., & Peterson, R.A. (2011). Preclinical Applications of Quantitative Imaging, Cytometry to Support Drug Discovery. Methods in Cell Biology, Chapter 11, vol. 102, 291-308.

Berg, J.M., Tymoczko, J.L., & Stryer, L. (2002). Glycogen Metabolism. Biochemistry. 5th edition, Chapter 21. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21190/ Accessed 17 August 2018.

Buppajarntham, S., & Junpaparp, P. (2014). Insulin, Referance Range. Available from: https://emedicine.medscape.com/article/2089224-overview. Accessed 17 August 2018.

Genuth, S.M. (1973). Plasma Insulin and Glucose Profiles in Normal, Obese, and Diabetic Persons. Ann Intern Med. Vol. 79(6), 812–822. doi: 10.7326/0003-4819-79-6-812

Austin Community College. Glucose Regulation. Available from: http://www.austincc.edu/apreview/EmphasisItems/Glucose_regulation.html. Accessed 17 August 2018.

Krinsley, J.S., & Preiser, J-C. (2015). Time in blood glucose range 70 to 140 mg/dl > 80% is strongly associated with increased survival in non-diabetic critically ill adults. Krinsley and Preiser Critical Care, 19:179. DOI 10.1186/s13054-015-0908-7

Downloads

Published

2019-03-20
CITATION
DOI: 10.11121/ijocta.01.2019.00685
Published: 2019-03-20

How to Cite

Emek, S., Evren, V., & Bora, Şebnem. (2019). Simulation of glucose regulating mechanism with an agent-based software engineering tool. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 9(3), 15–20. https://doi.org/10.11121/ijocta.01.2019.00685

Issue

Section

Research Articles