A novel multi-objective optimization based experimental design and its application for physiological model of type 1 diabetes

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 8, Issue PART 1, 2012, Pages 638-643

  Maheshwari, Vaibhav ^a   Rangaiah, Gade Pandu ^a   Samavedham, Lakshminarayanan ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Fisher information matrix (FIM); Modified bergman minimal model; Multi objective optimization; Parameter correlation; Pareto optimal front

Indexed keywords

CURVE FITTING; DESIGN; DESIGN OF EXPERIMENTS; ECONOMIC AND SOCIAL EFFECTS; FISHER INFORMATION MATRIX; MULTIOBJECTIVE OPTIMIZATION; PARETO PRINCIPLE; PHYSIOLOGY; PROCESS CONTROL; STATISTICS;

BIOLOGICAL ENGINEERING; CORRELATION MEASURES; EXPERIMENTAL DESIGN TECHNIQUES; INFORMATION MEASURES; MINIMAL MODEL; OPTIMAL EXPERIMENTAL DESIGNS; PARAMETER CORRELATION; PARETO-OPTIMAL FRONT;

PHYSIOLOGICAL MODELS;

EID: 84867040198     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20120710-4-SG-2026.00117     Document Type: Conference Paper

References (21)

1. AGARWAL, A. K. & BRISK, M. L. 1985. Sequential experimental design for precise parameter estimation. 2. Design criteria. Industrial & Engineering Chemistry Process Design and Development, 24, 207-210.
2. BALAKRISHNAN, N. P., RANGAIAH, G. P. & SAMAVEDHAM, L. 2011. Review and analysis of blood glucose (BG) models for Type 1 diabetic patients. Industrial & Engineering Chemistry Research, 50, 12041-12066.
3. BALSA-CANTO, E., RODRIGUEZ-FERNANDEZ, M. & BANGA, J. R. 2007. Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation. Journal of Food Engineering, 82, 178-188.
4. BANGA, J. R. & BALSA-CANTO, E. 2008. Parameter estimation and optimal experimental design. Essays in Biochemistry: Systems Biology, 45, 195-210.
5. BERGMAN, R. N., PHILLIPS, L. S. & COBELLI, C. 1981. Physiologic evaluation of factors controlling glucose tolerance in man: Measurement of insulin sensitivity andbeta-cell glucose sensitivity from the response to intravenous glucose. Journal of Clinical Investigation, 68, 1456.
6. CHEN, C. L. & TSAI, H. W. 2009. Model-based insulin therapy scheduling: A mixed-integer nonlinear dynamic optimization approach. Industrial & Engineering Chemistry Research, 48, 8595-8604.
7. CHU, Y. & HAHN, J. 2008. Integrating parameter selection with experimental design under uncertainty for nonlinear dynamic systems. AIChE Journal, 54, 2310-2320.
8. CHUNG, S. H., MA, D. L. & BRAATZ, R. D. 2000. Optimal model-based experimental design in batch crystallization. Chemometrics and Intelligent Laboratory Systems, 50, 83-90.
9. FRANCESCHINI, G. & MACCHIETTO, S. 2008a. Modelbased design of experiments for parameter precision: State of the art. Chemical Engineering Science, 63, 4846-4872.
10. FRANCESCHINI, G. & MACCHIETTO, S. 2008b. Novel anticorrelation criteria for model-based experiment design: Theory and formulations. AIChE Journal, 54, 1009-1024.
11. FUJIWARA, M., NAGY, Z. K., CHEW, J. W. & BRAATZ, R. D. 2005. First-principles and direct design approaches for the control of pharmaceutical crystallization. Journal of Process Control, 15, 493-504.
12. GALVANIN, F., BAROLO, M., BEZZO, F. & MACCHIETTO, S. 2010. A backoff strategy for modelbased experiment design under parametric uncertainty. AIChE Journal, 56, 2088-2102.
13. HERNJAK, N. & DOYLE III, F. J. 2005. Glucose control design using nonlinearity assessment techniques. AIChE Journal, 51, 544-554.
14. HOVORKA, R., CANONICO, V., CHASSIN, L. J., HAUETER, U., MASSI-BENEDETTI, M., FEDERICI, M. O., PIEBER, T. R., SCHALLER, H. C., SCHAUPP, L. & VERING, T. 2004. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological measurement, 25, 905-920.
15. KÖRKEL, S., KOSTINA, E., BOCK, H. G. & SCHLÖDER, J. P. 2004. Numerical methods for optimal control problems in design of robust optimal experiments for nonlinear dynamic processes. Optimization Methods and Software, 19, 327-338.
16. LYNCH, S. M. & BEQUETTE, B. W. 2002. Model predictive control of blood glucose in type I diabetics using subcutaneous glucose measurements. American Control Conference, 2002. 5, 4039-4043.
17. NUCCI, G. & COBELLI, C. 2000. Models of subcutaneous insulin kinetics. A critical review. Computer methods and programs in biomedicine, 62, 249-257.
18. PRITCHARD, D. J. & BACON, D. W. 1978. Prospects for reducing correlations among parameter estimates in kinetic models. Chemical Engineering Science, 33, 1539-1543.
19. RODRIGUEZ-FERNANDEZ, M., MENDES, P. & BANGA, J. R. 2006. A hybrid approach for efficient and robust parameter estimation in biochemical pathways. Biosystems, 83, 248-265.
20. YANG, A., MARTIN, E., MONTAGUE, G. & MORRIS, J. 2006. Optimal experimental design for the precision of a subset of model parameters in process development Computer Aided Chemical Engineering. 21, 563-568.
21. ZHANG, Y. & EDGAR, T. F. 2008. PCA combined modelbased design of experiments (DOE) criteria for differential and algebraic system parameter estimation. Industrial & Engineering Chemistry Research, 47, 7772-7783.