Application of Global Sensitivity Analysis to Determine Goals for Design of Experiments: An Example Study on Antibody‐Producing Cell Cultures

Global sensitivity analysis (GSA) can be used to quantify the importance of model parameters and their interactions with respect to model output. In this study, the Sobol′ method for GSA is applied to a dynamic model of monoclonal antibody‐producing mammalian cell cultures in order to identify the parameters that need to be accurately determined experimentally. Our results show that most parameters have low sensitivity indices and exhibit strong interactions with one another. These parameters can be set at their nominal values and unnecessary experimentation can therefore be avoided. In contrast, certain parameters are identified as sensitive, necessitating their estimation given sufficiently rich experimental data. Moreover, parameter sensitivity varies during culture time in a biologically meaningful manner. In conclusion, GSA can serve as an excellent precursor to optimal experiment design.

[1]  Friedrich Srienc,et al.  Cytometric data as the basis for rigorous models of cell population dynamics , 1999 .

[2]  J. Claes,et al.  Practical Identification of Unstructured Growth Kinetics by Application of Optimal Experimental Design , 1997 .

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

[4]  U. Schibler,et al.  The synthesis and processing of the messenger RNAs specifying heavy and light chain immunoglobulins in MPC-11 cells , 1978, Cell.

[5]  M. Soroush,et al.  Multi-rate nonlinear state and parameter estimation in a bioreactor , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[6]  Michael D. McKay,et al.  Evaluating Prediction Uncertainty , 1995 .

[7]  D. Ryu,et al.  Recent Progress in Biomolecular Engineering , 2000, Biotechnology progress.

[8]  B. Palsson,et al.  Toward Metabolic Phenomics: Analysis of Genomic Data Using Flux Balances , 1999, Biotechnology progress.

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

[10]  Axel Munack,et al.  Design of Optimal Dynamical Experiments for Parameter Estimation , 1989, 1989 American Control Conference.

[11]  M. Al‐Rubeai,et al.  Cell death in bioreactors: A role for apoptosis , 1994, Biotechnology and bioengineering.

[12]  John P. Barford,et al.  An unstructured kinetic model of macromolecular metabolism in batch and fed-batch cultures of hybridoma cells producing monoclonal antibody , 2000 .

[13]  Bernhard Ø Palsson,et al.  In silico biotechnology. Era of reconstruction and interrogation. , 2004, Current opinion in biotechnology.

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

[15]  Steven P. Asprey,et al.  Global Parametric Identifiability of a Dynamic Unstructured Model of Hybridoma Cell Culture , 2001 .

[16]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[17]  G. Moore,et al.  The effect of dihydrofolate reductase-mediated gene amplification on the expression of transfected immunoglobulin genes. , 1987, Journal of immunology.

[18]  Shankar Subramaniam,et al.  Bioinformatics and cellular signaling. , 2004, Current opinion in biotechnology.

[19]  Eric Walter,et al.  Identifiability of parametric models , 1987 .

[20]  C. Fortuin,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory , 1973 .

[21]  Axel Munack,et al.  Optimal Feeding Strategy for Identification of Monod-Type Models by Fed-Batch Experiments , 1989 .

[22]  M H Nathanson,et al.  Multiple-objective criteria for optimal experimental design: application to ferrokinetics. , 1985, The American journal of physiology.

[23]  T. Schäfer,et al.  Modelling hybridoma cell growth and metabolism--a comparison of selected models and data. , 1996, Journal of biotechnology.

[24]  Athanassios Sambanis,et al.  Modeling of cell culture processes , 2004, Cytotechnology.

[25]  V Hatzimanikatis,et al.  Proteomics: Theoretical and Experimental Considerations , 1999, Biotechnology progress.

[26]  R. Fisher.  The Advanced Theory of Statistics , 1943, Nature.

[27]  Matthias Reuss,et al.  Optimal Experimental Design for Parameter Estimation in Unstructured Growth Models , 1994 .

[28]  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.

[29]  J. I.  The Design of Experiments , 1936, Nature.

[30]  J. Ljunggren,et al.  Physiology of cultured animal cells. , 1997, Journal of biotechnology.

[31]  T. Bibila,et al.  In Pursuit of the Optimal Fed‐Batch Process for Monoclonal Antibody Production , 1995, Biotechnology progress.

[32]  K. Shuler,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. II Applications , 1973 .

[33]  K J Versyck,et al.  Introducing optimal experimental design in predictive modeling: a motivating example. , 1999, International journal of food microbiology.

[34]  M. Flickinger,et al.  Use of a structured kinetic model of antibody synthesis and secretion for optimization of antibody production systems: I. Steady‐state analysis , 1992, Biotechnology and bioengineering.

[35]  K. Shuler,et al.  Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. III. Analysis of the approximations , 1975 .

[36]  J. Villadsen,et al.  On the use of population balances , 1999 .

[37]  J E Bailey,et al.  Mathematical Modeling and Analysis in Biochemical Engineering: Past Accomplishments and Future Opportunities , 1998, Biotechnology progress.

[38]  K.,et al.  Nonlinear sensitivity analysis of multiparameter model systems , 1977 .

[39]  S. Dhir,et al.  Dynamic optimization of hybridoma growth in a fed-batch bioreactor. , 2000, Biotechnology and bioengineering.