An Information Theoretical Analysis of Human Insulin-Glucose System Towards The Internet of Bio-Nano Things
Abbasi, Naveed A
IEEE Transactions on Nanobioscience
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Abbasi, N. A., & Akan, O. (2017). An Information Theoretical Analysis of Human Insulin-Glucose System Towards The Internet of Bio-Nano Things. IEEE Transactions on Nanobioscience https://doi.org/10.1109/TNB.2017.2762160
Molecular communication is an important tool to understand biological communications with many promising applications in Internet of Bio-Nano Things (IoBNT). The insulinglucose system is of key significance among the major intrabody nanonetworks since it fulfills metabolic requirements of the body. Study of biological networks from information and communication theoretical (ICT) perspective is necessary for their introduction in the IoBNT framework. Therefore, the objective of this work is to provide and analyze for the first time in literature, a simple molecular communication model of the human insulin-glucose system from ICT perspective. The data rate, channel capacity and the group propagation delay are analyzed for a two-cell network between a pancreatic beta cell and a muscle cell that are connected through a capillary. The results point out a correlation between an increase in insulin resistance and a decrease in the data rate and channel capacity, an increase in the insulin transmission rate and an increase in the propagation delay. We also propose applications for introduction of the system in IoBNT framework. Multi-cell insulin glucose system models may be based on this simple model to help in the investigation, diagnosis and treatment of insulin resistance by means of novel IoBNT applications.
insulin, sugar, muscles, biological system modeling, nanobioscience, biochemistry, immune system
This work was supported in part by ERC project MINERVA (ERC-2013-CoG #616922), and EU project CIRCLE (EUH2020- FET-Open #665564).
European Research Council (616922)
European Commission Horizon 2020 (H2020) Future and Emerging Technologies (FET) (665564)
External DOI: https://doi.org/10.1109/TNB.2017.2762160
This record's URL: https://www.repository.cam.ac.uk/handle/1810/271332