title: A bio-inspired glucose controller based on pancreatic β-cell physiology.
abstract: BACKGROUND
  Control algorithms for closed-loop insulin delivery in type 1 diabetes have been mainly based on control engineering or artificial intelligence techniques. These, however, are not based on the physiology of the pancreas but seek to implement engineering solutions to biology. Developments in mathematical models of the β-cell physiology of the pancreas have described the glucose-induced insulin release from pancreatic β cells at a molecular level. This has facilitated development of a new class of bio-inspired glucose control algorithms that replicate the functionality of the biological pancreas. However, technologies for sensing glucose levels and delivering insulin use the subcutaneous route, which is nonphysiological and introduces some challenges. In this article, a novel glucose controller is presented as part of a bio-inspired artificial pancreas.
  
  
  METHODS
  A mathematical model of β-cell physiology was used as the core of the proposed controller. In order to deal with delays and lack of accuracy introduced by the subcutaneous route, insulin feedback and a gain scheduling strategy were employed. A United States Food and Drug Administration-accepted type 1 diabetes mellitus virtual population was used to validate the presented controller.
  
  
  RESULTS
  Premeal and postmeal mean ± standard deviation blood glucose levels for the adult and adolescent populations were well within the target range set for the controller [(70, 180) mg/dl], with a percent time in range of 92.8 ± 7.3% for the adults and 83.5 ± 14% for the adolescents.
  
  
  CONCLUSIONS
  This article shows for the first time very good glucose control in a virtual population with type 1 diabetes mellitus using a controller based on a subcellular β-cell model.
authors: Center for Bio-Inspired Technology, Institute of Biomedical Engineering, Imperial College London, London, United Kingdom. pherrero@imperial.ac.uk
authors: Pau
authors: P
authors: Herrero
authors: Pantelis
authors: P
authors: Georgiou
authors: Nick
authors: N
authors: Oliver
authors: Desmond G
authors: DG
authors: Johnston
authors: Christofer
authors: C
authors: Toumazou
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D015374:Biosensing Techniques / Q000295:instrumentation; D001774:Blood Chemical Analysis / Q000295:instrumentation; D001786:Blood Glucose / Q000187:drug effects* / Q000378:metabolism; D003198:Computer Simulation; D003922:Diabetes Mellitus, Type 1 / Q000097:blood / Q000175:diagnosis / Q000188:drug therapy* / Q000503:physiopathology; D004334:Drug Administration Schedule; D004867:Equipment Design; D006801:Humans; D007003:Hypoglycemia / Q000097:blood / Q000139:chemically induced / Q000517:prevention & control; D007004:Hypoglycemic Agents / Q000008:administration & dosage* / Q000009:adverse effects; D007263:Infusions, Parenteral; D007328:Insulin / Q000008:administration & dosage* / Q000009:adverse effects; D007332:Insulin Infusion Systems; D050417:Insulin-Secreting Cells / Q000187:drug effects* / Q000378:metabolism; D008954:Models, Biological; D018670:Monitoring, Ambulatory / Q000295:instrumentation; D019397:Pancreas, Artificial; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D016896:Treatment Outcome; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin