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Diabetes: Models, Signals, and Control
Open accessAbstract
The control of diabetes is an interdisciplinary endeavor, which includes a significant biomedical engineering component, with traditions of success beginning in the early 1960s. It began with modeling of the insulin-glucose system, and progressed to large-scale in silico experiments, and automated closed-loop control (artificial pancreas). Here, we follow these engineering efforts through the last, almost 50 years. We begin with the now classic minimal modeling approach and discuss a number of subsequent models, which have recently resulted in the first in silico simulation model accepted as substitute to animal trials in the quest for optimal diabetes control. We then review metabolic monitoring, with a particular emphasis on the new continuous glucose sensors, on the analyses of their time-series signals, and on the opportunities that they present for automation of diabetes control. Finally, we review control strategies that have been successfully employed in vivo or in silico, presenting a promise for the development of a future artificial pancreas and, in particular, discuss a modular architecture for building closed-loop control systems, including insulin delivery and patient safety supervision layers. We conclude with a brief discussion of the unique interactions between human physiology, behavioral events, engineering modeling and control relevant to diabetes.
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“in silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner. 1,2”
From The UVA/PADOVA Type 1 Diabetes Simulator: New Features (2014) · cites this paper as [1]
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“The development of artificial pancreas control algorithms in the past few years has been accelerated by computer simulation; for example, in silico testing has provided directions for clinical studies and ruling out of ineffective control scenarios in a cost-effective manner. 1,2”
From The UVA/PADOVA Type 1 Diabetes Simulator: New Features (2014) · cites this paper as [1]
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Personal Health Systems for Diabetes Management, Early Diagnosis and Prevention
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Controlled bio-inspired self-organised criticality
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Self-citationIdentification of nonlinear dynamical system with synthetic data: a preliminary investigation
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Solving Robust Glucose-Insulin Control by Dixon Resultant Computations
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Model Predictive Control of Type 1 Diabetes
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Self-citationVisual Predictive Check in Models with Time-Varying Input Function
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Self-citationModeling glucose and free fatty acid kinetics in glucose and meal tolerance test
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Metadata
- Authors
- Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., De Nicolao, G., & Kovatchev, B. P.
- Affiliations
- University of Padua (IT)
- University of Pavia (IT)
- University of Virginia (US)
- Venue
- IEEE Reviews in Biomedical Engineering
- Published
- 2009
- Volume
- 2
- Pages
- 54-96
- ISSN
- 1937-3333 (ISSN-L)
- 1941-1189
- PMC
- PMC2951686
- OpenAlex
- W2171613573
- Cited by
- 511
- References
- 304
- Citations by year
- 2026: 13 · 2025: 27 · 2024: 11 · 2023: 17 · 2022: 25 · 2021: 27 · 2020: 32 · 2019: 27 · 2018: 39 · 2017: 42 · 2016: 29 · 2015: 40 · 2014: 44 · 2013: 39 · 2012: 43
- FWCI
- 24.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Padua Research Archive (University of Padova) (submittedVersion)
- Funders
- National Institutes of Health (5r01dk051562-23)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01-DK-051562)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- diabetes management, computer simulation, artificial pancreas, feedback control, continuous glucose monitoring, modular control architecture
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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