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Assessment of a new model of glucagon action with glucagon receptor dynamics

  • Clara Furió-Novejarque
  • , Ricardo Sanz
  • , Asbjørn Thode Reenberg
  • , Tobias K.S. Ritschel
  • , Ajenthen G. Ranjan
  • , Kirsten Nørgaard
  • , Jose Luis Díez
  • , John Bagterp Jørgensen
  • , Jorge Bondia*
  • *Corresponding author for this work
  • Polytechnic University of Valencia
  • Steno Diabetes Centre

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

In this work, a novel insulin-glucagon-glucose model is proposed, where the glucagon effect on the endogenous glucose production (EGP) is described by the dynamics of the glucagon receptors. In order to assess the quality of the model, its parameters are fitted in such a way that the influence of glucagon on EGP is isolated. Experimental data is used to validate the model structure and show that the receptor dynamics allow to explain some of the glucagon-related phenomena observed in the clinical data. This physiology-focused model will be useful in the development of artificial pancreas algorithms both for more realistic in silico validations and in the development of model-based control strategies.

Original languageEnglish
Title of host publicationProceedings of 10th Vienna International Conference on Mathematical Modelling MATHMOD 2022
Volume55
PublisherScienceDirect
Publication date2022
Edition20
Pages647-652
DOIs
Publication statusPublished - 2022
Event10th Vienna International Conference on Mathematical Modelling: MATHMOD 2022 - Vienna, Austria
Duration: 27 Jul 2022 → 29 Jul 2022
Conference number: 10

Conference

Conference10th Vienna International Conference on Mathematical Modelling
Number10
Country/TerritoryAustria
CityVienna
Period27/07/2022 → 29/07/2022

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Clinical data
  • Diabetes
  • Glucagon receptors
  • Parameter estimation
  • PK-PD model

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