Literature DB >> 25285200

The mathematician's control toolbox for management of type 1 diabetes.

Marie Csete1, John Doyle2.   

Abstract

Blood glucose levels are controlled by well-known physiological feedback loops: high glucose levels promote insulin release from the pancreas, which in turn stimulates cellular glucose uptake. Low blood glucose levels promote pancreatic glucagon release, stimulating glycogen breakdown to glucose in the liver. In healthy people, this control system is remarkably good at maintaining blood glucose in a tight range despite many perturbations to the system imposed by diet and fasting, exercise, medications and other stressors. Type 1 diabetes mellitus (T1DM) results from loss of the insulin-producing cells of the pancreas, the beta cells. These cells serve as both sensor (of glucose levels) and actuator (insulin/glucagon release) in a control physiological feedback loop. Although the idea of rebuilding this feedback loop seems intuitively easy, considerable control mathematics involving multiple types of control schema were necessary to develop an artificial pancreas that still does not function as well as evolved control mechanisms. Here, we highlight some tools from control engineering used to mimic normal glucose control in an artificial pancreas, and the constraints, trade-offs and clinical consequences inherent in various types of control schemes. T1DM can be viewed as a loss of normal physiologic controls, as can many other disease states. For this reason, we introduce basic concepts of control engineering applicable to understanding pathophysiology of disease and development of physiologically based control strategies for treatment.

Entities:  

Keywords:  control engineering; glucagon; glucose homeostasis; insulin; model predictive control; type 1 diabetes

Year:  2014        PMID: 25285200      PMCID: PMC4142019          DOI: 10.1098/rsfs.2014.0042

Source DB:  PubMed          Journal:  Interface Focus        ISSN: 2042-8898            Impact factor:   3.906


  26 in total

1.  Modeling the effects of subcutaneous insulin administration and carbohydrate consumption on blood glucose.

Authors:  Matthew W Percival; Wendy C Bevier; Youqing Wang; Eyal Dassau; Howard C Zisser; Lois Jovanovič; Francis J Doyle
Journal:  J Diabetes Sci Technol       Date:  2010-09-01

2.  Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms.

Authors:  B Wayne Bequette
Journal:  J Diabetes Sci Technol       Date:  2010-03-01

3.  A model-based algorithm for blood glucose control in type I diabetic patients.

Authors:  R S Parker; F J Doyle; N A Peppas
Journal:  IEEE Trans Biomed Eng       Date:  1999-02       Impact factor: 4.538

4.  Use of a food and drug administration-approved type 1 diabetes mellitus simulator to evaluate and optimize a proportional-integral-derivative controller.

Authors:  Srinivas Laxminarayan; Jaques Reifman; Garry M Steil
Journal:  J Diabetes Sci Technol       Date:  2012-11-01

5.  A bihormonal closed-loop artificial pancreas for type 1 diabetes.

Authors:  Firas H El-Khatib; Steven J Russell; David M Nathan; Robert G Sutherlin; Edward R Damiano
Journal:  Sci Transl Med       Date:  2010-04-14       Impact factor: 17.956

6.  In silico preclinical trials: methodology and engineering guide to closed-loop control in type 1 diabetes mellitus.

Authors:  Stephen D Patek; B Wayne Bequette; Marc Breton; Bruce A Buckingham; Eyal Dassau; Francis J Doyle; John Lum; Lalo Magni; Howard Zisser
Journal:  J Diabetes Sci Technol       Date:  2009-03-01

7.  Feasibility of automating insulin delivery for the treatment of type 1 diabetes.

Authors:  Garry M Steil; Kerstin Rebrin; Christine Darwin; Farzam Hariri; Mohammed F Saad
Journal:  Diabetes       Date:  2006-12       Impact factor: 9.461

8.  Mixed meal simulation model of glucose-insulin system.

Authors:  Chiara Dalla Man; Robert A Rizza; Claudio Cobelli
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2006

9.  Blood glucose control in type 1 diabetes with a bihormonal bionic endocrine pancreas.

Authors:  Steven J Russell; Firas H El-Khatib; David M Nathan; Kendra L Magyar; John Jiang; Edward R Damiano
Journal:  Diabetes Care       Date:  2012-08-24       Impact factor: 19.112

10.  The impact of mathematical modeling on the understanding of diabetes and related complications.

Authors:  I Ajmera; M Swat; C Laibe; N Le Novère; V Chelliah
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2013-07-10
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  2 in total

Review 1.  Engineering control into medicine.

Authors:  David J Stone; Leo Anthony Celi; Marie Csete
Journal:  J Crit Care       Date:  2015-01-30       Impact factor: 3.425

2.  Personalized glucose forecasting for type 2 diabetes using data assimilation.

Authors:  David J Albers; Matthew Levine; Bruce Gluckman; Henry Ginsberg; George Hripcsak; Lena Mamykina
Journal:  PLoS Comput Biol       Date:  2017-04-27       Impact factor: 4.475

  2 in total

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