TY - GEN
T1 - Parameter Estimation of A Physiological Diabetes Model Using Neural Networks
AU - Moreira, Ana
AU - Philipps, Maren
AU - Riel, Natal Van
N1 - Funding Information: ACKNOWLEDGMENTS This work was developed as part of the Erasmus+ Traineeships programme of Ana Moreira. Maren Philipps was funded by the Federal Ministry for Education and Research/BMBF (project GENImmune, grant number 031L0292F). Natal van Riel received funding from the Dutch Research Council (NWO) for the project DiaGame (project number 628.011.027), part of the research programme Data2Person. Publisher Copyright: © 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Diabetes Mellitus is a chronic disease characterized by elevated glucose levels in the blood due to deregulated insulin levels. The management of diabetes is greatly based on self-management of the patient by insulin injections, diet and exercise. Because of this, the study of mathematical models capable of describing the glucose-insulin metabolism in patients with diabetes can be a great tool to help this management. An example of such model, which has been extensively used, is the Eindhoven Diabetes Education Simulator (E-DES) model. Systems biology is a field of study focused on the structure and dynamics of biological systems, and mathematical modelling of these systems through non-linear ordinary differential equations. Parameter estimation, the process of determining the values of undetermined parameters in a mathematical model, is important for achieving reliable predictive models. Many parameter estimation methods exist, including traditional optimization methods and newer techniques such as artificial neural networks, in particular Systems Biology Informed Neural Networks (SBINNs). The goal of this study was to evaluate the performance of SBINNs in estimating E-DES parameters and in fitting to simulated glucose and insulin plasma measurements. Considering different variations of SBINNs, we were able to find a network capable of estimating some of the most important parameters of the E-DES model using simulated plasma glucose and insulin data with a good performance on simulated data.
AB - Diabetes Mellitus is a chronic disease characterized by elevated glucose levels in the blood due to deregulated insulin levels. The management of diabetes is greatly based on self-management of the patient by insulin injections, diet and exercise. Because of this, the study of mathematical models capable of describing the glucose-insulin metabolism in patients with diabetes can be a great tool to help this management. An example of such model, which has been extensively used, is the Eindhoven Diabetes Education Simulator (E-DES) model. Systems biology is a field of study focused on the structure and dynamics of biological systems, and mathematical modelling of these systems through non-linear ordinary differential equations. Parameter estimation, the process of determining the values of undetermined parameters in a mathematical model, is important for achieving reliable predictive models. Many parameter estimation methods exist, including traditional optimization methods and newer techniques such as artificial neural networks, in particular Systems Biology Informed Neural Networks (SBINNs). The goal of this study was to evaluate the performance of SBINNs in estimating E-DES parameters and in fitting to simulated glucose and insulin plasma measurements. Considering different variations of SBINNs, we were able to find a network capable of estimating some of the most important parameters of the E-DES model using simulated plasma glucose and insulin data with a good performance on simulated data.
KW - Systems Biology-Informed Neural Networks
KW - diabetes
KW - modeling
KW - neural networks
KW - systems biology
UR - http://www.scopus.com/inward/record.url?scp=85174904486&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/CIBCB56990.2023.10264904
DO - https://doi.org/10.1109/CIBCB56990.2023.10264904
M3 - Conference contribution
T3 - CIBCB 2023 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
BT - CIBCB 2023 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2023
Y2 - 29 August 2023 through 31 August 2023
ER -