Identification of Physiological Models of Type 1 Diabetes Mellitus by Model-Based Design of Experiments

[1]  L. Schaupp,et al.  On‐line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with Type 1 diabetes , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[2]  Maria Pia Saccomani,et al.  Parameter identifiability of nonlinear systems: the role of initial conditions , 2003, Autom..

[3]  F. El-Khatib,et al.  Adaptive Closed-Loop Control Provides Blood-Glucose Regulation Using Dual Subcutaneous Insulin and Glucagon Infusion in Diabetic Swine , 2007, Journal of diabetes science and technology.

[4]  J. D. Stigter,et al.  On adaptive optimal input design: A bioreactor case study , 2006 .

[5]  Peter J Moate,et al.  MINMOD Millennium: a computer program to calculate glucose effectiveness and insulin sensitivity from the frequently sampled intravenous glucose tolerance test. , 2003, Diabetes technology & therapeutics.

[6]  Sandro Macchietto,et al.  Model-Based Design of Parallel Experiments , 2007 .

[7]  Sandro Macchietto,et al.  Designing robust optimal dynamic experiments , 2002 .

[8]  C. Cobelli,et al.  Global Identifiability of Nonlinear Model Parameters , 1997 .

[9]  Yves Lecourtier,et al.  Unidentifiable compartmental models: what to do? , 1981 .

[10]  Thomas F. Edgar,et al.  PCA Combined Model-Based Design of Experiments (DOE) Criteria for Differential and Algebraic System Parameter Estimation , 2008 .

[11]  W. Zingg,et al.  An Artificial Endocrine Pancreas , 1974, Diabetes.

[12]  Howard Wolpert,et al.  Clinical application of emerging sensor technologies in diabetes management: consensus guidelines for continuous glucose monitoring (CGM). , 2008, Diabetes technology & therapeutics.

[13]  J. Brauker,et al.  Continuous glucose sensing: future technology developments. , 2009, Diabetes technology & therapeutics.

[14]  Daniel W. Apley,et al.  Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments , 2006 .

[15]  Francis J. Doyle,et al.  Robust H∞ glucose control in diabetes using a physiological model , 2000 .

[16]  Garry M. Steil,et al.  Automated insulin delivery for type 1 diabetes , 2006 .

[17]  R.S. Parker,et al.  A model-based algorithm for blood glucose control in Type I diabetic patients , 1999, IEEE Transactions on Biomedical Engineering.

[18]  Cleo Kontoravdi,et al.  Application of Global Sensitivity Analysis to Determine Goals for Design of Experiments: An Example Study on Antibody‐Producing Cell Cultures , 2008, Biotechnology progress.

[19]  Hans Bock,et al.  Numerical methods for optimum experimental design in DAE systems , 2000 .

[20]  R. Hovorka,et al.  Partitioning glucose distribution/transport, disposal, and endogenous production during IVGTT. , 2002, American journal of physiology. Endocrinology and metabolism.

[21]  Richard N Bergman,et al.  Orchestration of Glucose Homeostasis , 2007, Diabetes.

[22]  R. Bellman,et al.  On structural identifiability , 1970 .

[23]  Claudio Cobelli,et al.  GIM, Simulation Software of Meal Glucose—Insulin Model , 2007, Journal of diabetes science and technology.

[24]  Dale E. Seborg,et al.  IDENTIFICATION OF LINEAR DYNAMIC MODELS FOR TYPE 1 DIABETES: A SIMULATION STUDY , 2006 .

[25]  Eric Walter,et al.  On the identifiability and distinguishability of nonlinear parametric models , 1996 .

[26]  Lennart Ljung,et al.  On global identifiability for arbitrary model parametrizations , 1994, Autom..

[27]  Claudio Cobelli,et al.  The minimal model of glucose disappearance: optimal input studies , 1987 .

[28]  Dale E. Seborg,et al.  An Improved PID Switching Control Strategy for Type 1 Diabetes , 2008, IEEE Transactions on Biomedical Engineering.

[29]  B. Bequette A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. , 2005, Diabetes technology & therapeutics.

[30]  Claudio Cobelli,et al.  Meal Simulation Model of the Glucose-Insulin System , 2007, IEEE Transactions on Biomedical Engineering.

[31]  R. Ollerton Application of optimal control theory to diabetes mellitus , 1989 .

[32]  Nicholas A Peppas,et al.  Effectiveness of Intravenous Infusion Algorithms for Glucose Control in Diabetic Patients Using Different Simulation Models. , 2009, Industrial & engineering chemistry research.

[33]  Sten Bay Jørgensen,et al.  Structural parameter identifiability analysis for dynamic reaction networks , 2008 .

[34]  Rudiyanto Gunawan,et al.  Iterative approach to model identification of biological networks , 2005, BMC Bioinformatics.

[35]  Eyal Dassau,et al.  Practical Approach to Design and Implementation of a Control Algorithm in an Artificial Pancreatic Beta Cell , 2009 .

[36]  G. Ward,et al.  The minimal model of glucose disposal in the analysis of glucose effectiveness: importance of early insulin data. , 2009, Diabetes technology & therapeutics.

[37]  Malgorzata E. Wilinska,et al.  Insulin kinetics in type-1 diabetes: continuous and bolus delivery of rapid acting insulin , 2005, IEEE Transactions on Biomedical Engineering.

[38]  R. Hovorka,et al.  Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. , 2004, Physiological measurement.

[39]  Federico Galvanin Optimal design of clinical tests for the identification of physiological models of type 1 diabetes mellitus in the presence of model uncertainty , 2009 .

[40]  Geoffrey M. Shaw,et al.  Derivative weighted active insulin control algorithms and intensive care unit trials , 2005 .

[41]  Y. Yamasaki,et al.  A mathematical insulin-secretion model and its validation in isolated rat pancreatic islets perifusion. , 1984, Computers and biomedical research, an international journal.

[42]  Claudio Cobelli,et al.  Models of subcutaneous insulin kinetics. A critical review , 2000, Comput. Methods Programs Biomed..

[43]  R S Parker,et al.  Control-relevant modeling in drug delivery. , 2001, Advanced drug delivery reviews.

[44]  Peter Reichert,et al.  Practical identifiability of ASM2d parameters--systematic selection and tuning of parameter subsets. , 2002, Water research.

[45]  Pier Giorgio Fabietti,et al.  Control oriented model of insulin and glucose dynamics in type 1 diabetics , 2006, Medical and Biological Engineering and Computing.

[46]  B.W. Bequette,et al.  Model predictive control of blood glucose in type I diabetics using subcutaneous glucose measurements , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[47]  B. Bequette,et al.  A tutorial on biomedical process control , 2007 .

[48]  Sandro Macchietto,et al.  Validation of a Model for Biodiesel Production through Model-Based Experiment Design , 2007 .

[49]  Eyal Dassau,et al.  Coordinated Basal—Bolus Infusion for Tighter Postprandial Glucose Control in Insulin Pump Therapy , 2009, Journal of diabetes science and technology.

[50]  Claudio Cobelli,et al.  A System Model of Oral Glucose Absorption: Validation on Gold Standard Data , 2006, IEEE Transactions on Biomedical Engineering.

[51]  Yang Kuang,et al.  Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview , 2006 .

[52]  Massimiliano Barolo,et al.  A backoff strategy for model‐based experiment design under parametric uncertainty , 2009 .

[53]  Steven P. Asprey,et al.  Toward Global Parametric Estimability of a Large-Scale Kinetic Single-Cell Model for Mammalian Cell Cultures , 2005 .

[54]  R. Bellazzi,et al.  The subcutaneous route to insulin-dependent diabetes therapy. , 2001, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[55]  Massimiliano Barolo,et al.  Online Model-Based Redesign of Experiments for Parameter Estimation in Dynamic Systems , 2009 .

[56]  R. Bergman,et al.  Physiologic evaluation of factors controlling glucose tolerance in man: measurement of insulin sensitivity and beta-cell glucose sensitivity from the response to intravenous glucose. , 1981, The Journal of clinical investigation.

[57]  D. M. Titterington,et al.  Recent advances in nonlinear experiment design , 1989 .

[58]  Sandro Macchietto,et al.  Statistical tools for optimal dynamic model building , 2000 .

[59]  M. Fisher,et al.  A semiclosed-loop algorithm for the control of blood glucose levels in diabetics , 1991, IEEE Transactions on Biomedical Engineering.

[60]  H. Pohjanpalo System identifiability based on the power series expansion of the solution , 1978 .

[61]  Giovanni Sparacino,et al.  Numerical non-identifiability regions of the minimal model of glucose kinetics: superiority of Bayesian estimation. , 2003, Mathematical biosciences.

[62]  Sandro Macchietto,et al.  Model-based design of experiments for parameter precision: State of the art , 2008 .