An advisory protocol for rapid- and slow-acting insulin therapy based on a run-to-run methodology

F. Campos-Cornejo, D.U. Campos-Delgado, D. Espinoza-Trejo, H. Zisser, L. Jovanovic, F.J. Doyle III, E. Dassau, “An advisory protocol for rapid- and slow-acting insulin therapy based on a run-to-run methodology,” Diabetes Technology & Therapeutics, vol. 12, no. 7, pp. 555-65, Jul 2010. [DOI]

Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events

B. Grosman, E. Dassau, H.C. Zisser, L. Jovanovic, F.J. Doyle III, “Zone model predictive control: a strategy to minimize hyper- and hypoglycemic events,” Journal of Diabetes Science and Technology, vol. 4, no. 4, pp. 961-75, Jul 2010. [PMID]

Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor

R. Mauseth, Y. Wang, E. Dassau, R. Kircher Jr, D. Matheson, H. Zisser, L. Jovanovic, F.J. Doyle III, “Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor,” Journal of Diabetes Science and Technology, vol. 4, no. 4, pp. 913-22, Jul 2010. [PMID]

Fathead Minnow Steroidogenesis: in silico Analyses Reveals Tradeoffs Between Nominal Target Efficacy and Robustness to Cross-talk

J.E. Shoemaker, K. Gayen, N. Garcia-Reyero, E.J. Perkins, D.L. Villeneuve, L. Liu, F.J. Doyle III, “Fathead Minnow Steroidogenesis: in silico Analyses Reveals Tradeoffs Between Nominal Target Efficacy and Robustness to Cross-talk,” BMC Systems Biology, June 2010.[DOI]

Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring: A safety net for the artificial pancreas

E. Dassau, F. Cameron, H. Lee, B.W. Bequette, H. Zisser, L. Jovanovic, H.P. Chase, D.M. Wilson, B.A. Buckingham, F.J. Doyle III, “Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring: A safety net for the artificial pancreas,” Diabetes Care, vol. 33, no. 6, pp. 1013–1017, June 2010. [DOI]

Model Predictive Control with Learning-Type Set-point:Application to Artificial Pancreatic β-Cell

Y. Wang, H. Zisser, E. Dassau, L. Jovanovic, F.J. Doyle III, “Model Predictive Control with Learning-Type Set-point:Application to Artificial Pancreatic β-Cell ,” AIChE Journal, vol. 56, no. 6, pp. 1510–1518, June 2010.[DOI]

Prevention of Nocturnal Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension

B.A. Buckingham, H.P. Chase, E. Dassau, E. Corby, P. Clinton, V. Gage, K. Caswell, J. Wilkinson, F. Cameron, H. Lee, B.W. Bequette, F.J. Doyle III, “Prevention of Nocturnal Hypoglycemia Using Predictive Alarm Algorithms and Insulin Pump Suspension,” Diabetes Care, vol. 33, no. 5, pp. 1013–1017, May 2010.[DOI]

Velocity Response Curves Support the Role of Continuous Entrainment in Circadian Clocks

S.R. Taylor, A.B. Webb, K.S. Smith, L.R. Petzold, F.J. Doyle III, “Velocity Response Curves Support the Role of Continuous Entrainment in Circadian Clocks,” J Biol Rhythms, vol. 25, no. 2, pp. 138–149, April 2010.[DOI]

Closed-Loop Control of Artificial Pancreatic β-Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control

Y. Wang, E. Dassau, F.J. Doyle III, “Closed-Loop Control of Artificial Pancreatic β-Cell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control,” Biomedical Engineering, IEEE Transactions on, vol. 57, no. 2, pp. 211–219, February 2010.[DOI]

Survey on Iterative Learning Control, Repetitive Control, and Run-to-Run Control

Y. Wang, F. Gao, F.J. Doyle III, “Survey on Iterative Learning Control, Repetitive Control, and Run-to-Run Control,” J Proc Cont, vol. 19, no. 10, pp. 1589–1600, December 2009.[DOI]