Multiobjective Framework for Model-based Design of Experiments to Improve Parameter Precision and Minimize Parameter Correlation

The need for first principles based models for chemical and biological processes has led to the development of techniques for model-based design of experiments (MBDOE). These techniques help in speeding up the parameter estimation efforts and typically lead to improved parameter precision with a relatively short experimental campaign. In the case of complex kinetic networks involving parallel and/or consecutive reactions, correlation among model parameters makes the inverse problem of parameter estimation very difficult. It is therefore important to develop experimental design techniques that not only increase information content about the system to facilitate precise parameter estimation but also reduce the correlation among parameters. This article presents a multiobjective optimization (MOO) based framework for experimental design, where, in addition to the traditional objective of eliciting maximally informative data for parameter estimation, an explicit objective to reduce correlation among parameter...

[1]  M. Nihtilä,et al.  Practical identifiability of growth and substrate consumption models , 1977, Biotechnology and bioengineering.

[2]  David W. Bacon,et al.  Prospects for reducing correlations among parameter estimates in kinetic models , 1978 .

[3]  Michael L. Brisk,et al.  Sequential experimental design for precise parameter estimation. 2. Design criteria , 1985 .

[4]  Michael L. Brisk,et al.  Sequential experimental design for precise parameter estimation. 1. Use of reparameterization , 1985 .

[5]  W. R. Witkowski,et al.  Approximation of parameter uncertainty in nonlinear optimization-based parameter estimation schemes , 1993 .

[6]  Andrea P. Reverberi,et al.  New Procedure for Optimal Design of Sequential Experiments in Kinetic Models , 1994 .

[7]  Yuji Naka,et al.  Mathematical Problems in Fitting Kinetic Models—Some New Perspectives , 1999 .

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

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

[10]  K J Versyck,et al.  On the design of optimal dynamic experiments for parameter estimation of a Ratkowsky-type growth kinetics at suboptimal temperatures. , 2000, International journal of food microbiology.

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

[12]  Jan Van Impe,et al.  Computation of optimal identification experiments for nonlinear dynamic process models: a stochastic global optimization approach , 2002 .

[13]  Kwang-Hyun Cho,et al.  Experimental Design in Systems Biology, Based on Parameter Sensitivity Analysis Using a Monte Carlo Method: A Case Study for the TNFα-Mediated NF-κ B Signal Transduction Pathway , 2003, Simul..

[14]  Michel Cabassud,et al.  Precise parameter estimation for chemical batch reactions in heterogeneous medium , 2003 .

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

[16]  Richard D. Braatz,et al.  First-principles and direct design approaches for the control of pharmaceutical crystallization , 2005 .

[17]  Maria Rodriguez-Fernandez,et al.  A hybrid approach for efficient and robust parameter estimation in biochemical pathways. , 2006, Bio Systems.

[18]  Bernd Hitzmann,et al.  Experimental design for optimal parameter estimation of an enzyme kinetic process based on the analysis of the Fisher information matrix. , 2006, Journal of theoretical biology.

[19]  Eva Balsa-Canto,et al.  Optimal design of dynamic experiments for improved estimation of kinetic parameters of thermal degradation , 2007 .

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

[21]  Sandro Macchietto,et al.  Novel anticorrelation criteria for model‐based experiment design: Theory and formulations , 2008 .

[22]  Sandro Macchietto,et al.  Novel anticorrelation criteria for design of experiments: Algorithm and application , 2008 .

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

[24]  Sandro Macchietto,et al.  Anti-Correlation Approach to Model-Based Experiment Design: Application to a Biodiesel Production Process , 2008 .

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

[26]  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 .

[27]  Cheng-Liang Chen,et al.  Model-Based Insulin Therapy Scheduling: A Mixed-Integer Nonlinear Dynamic Optimization Approach , 2009 .

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

[29]  G. P. Rangaiah,et al.  Review and Analysis of Blood Glucose (BG) Models for Type 1 Diabetic Patients , 2011 .

[30]  Kim B. McAuley,et al.  Mathematical modelling of chemical processes—obtaining the best model predictions and parameter estimates using identifiability and estimability procedures , 2012 .

[31]  Filip Logist,et al.  Optimal experiment design for dynamic bioprocesses: A multi-objective approach , 2012 .