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A new general glucose homeostatic model using a proportional-integral-derivative controller.

E M Watson¹, M J Chappell, F Ducrozet

¹AstraZeneca, Discovery Department, Mereside, Alderley Park, Macclesfield SK104TG, UK. E.M.Watson@warwick.ac.uk

Computer Methods and Programs in Biomedicine

|December 18, 2010

Summary

This study introduces a novel model for the glucose-insulin system, inspired by engineering control principles, to better characterize glycaemic regulation. The model effectively simulates glucose and insulin dynamics, showing good agreement with clinical data.

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Improving IV Insulin Administration in a Community Hospital

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Published on: June 11, 2012

Area of Science:

Physiology
Biomedical Engineering
Control Systems

Background:

The glucose-insulin system presents modeling challenges due to complex feedback mechanisms.
Accurate modeling is crucial for understanding and managing glycaemic regulation.

Purpose of the Study:

To develop a novel model for characterizing glycaemic regulation.
To incorporate a three-phase insulin secretion model analogous to proportional-integral-derivative (PID) controllers.
To assess model identifiability and parameter estimation using clinical data.

Main Methods:

A new model was developed, incorporating states for glucose, insulin, insulin action, and an integral glucose function.
Structural identifiability analysis was performed to assess parameter uniqueness.
Parameter estimation was conducted using data from Intravenous Glucose Tolerance Tests (IVGTT) and hyperglycaemic clamp studies.

Main Results:

The model successfully characterizes glycaemic regulation by simulating three phases of insulin secretion.
Structural identifiability analysis identified two unidentifiable parameters, with clear justifications provided.
Model simulations demonstrated strong agreement with real-world IVGTT and hyperglycaemic clamp data.

Conclusions:

The proposed model offers an effective approach to characterizing the glucose-insulin system.
The model's structure, inspired by PID controllers, provides a biologically plausible framework.
The model's validation with clinical data supports its utility in glycaemic regulation research.