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Homogeneous Time-resolved Förster Resonance Energy Transfer-based Assay for Detection of Insulin Secretion
Published on: May 10, 2018
Parameter set uniqueness and confidence limits in model identification of insulin transport models from simulation
Terry G Farmer1, Thomas F Edgar, Nicholas A Peppas
1Department of Chemical Engineering, The University of Texas at Austin, Texas 78712-0231, USA.
Diabetes Technology & Therapeutics
|February 12, 2008
Summary
Accurate patient models for diabetes research require precise parameter estimation. Using user-defined gradients and Hessian matrices significantly improves the accuracy of insulin pharmacokinetic model parameter estimation, enhancing simulation reliability for glucose control system design.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Endocrinology
Background:
- Patient-specific models of glucose and insulin dynamics are crucial for developing novel diabetes therapies through simulation.
- Effective model parameter estimation using patient data is essential for the utility of these metabolic models.
Purpose of the Study:
- To investigate the accuracy of least squares for estimating model parameters from simulation data.
- To evaluate the impact of user-defined gradient and Hessian calculations on parameter estimation and confidence limits.
Main Methods:
- Generated simulation data using a known-parameter intravenous insulin pharmacokinetic model for a type 1 diabetes mellitus patient.
- Assessed parameter estimation accuracy and 95% confidence limits with and without user-supplied gradient and Hessian calculations.
Main Results:
- Parameter estimations without user-supplied quantities were highly dependent on initial guesses, with confidence limits exceeding +/-100%.
- User-defined quantities enabled effective estimation of one-compartment model parameters.
- While two-compartment model estimation still depended on initial guesses, confidence limits decreased, and data fits were very good.
Conclusions:
- User-defined gradients and Hessian matrices lead to more accurate parameter estimations for insulin transport models.
- Improved parameter estimation accuracy can enhance the reliability of simulations used in designing glucose control systems.
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