Parametric sensitivity analysis and reduction of a detailed nutritional model of plant cell cultures

Biotechnology and Bioengineering

Volumn 108, Issue 5, 2011, Pages 1108-1118

  Mailier, J ^a   Delmotte, A ^a   Cloutier, M ^b   Jolicoeur, M ^b   Wouwer, A Vande ^a  

^a UNIVERSITY OF MONS   (Belgium)

^b ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

Biotechnology; Model reduction; Modeling; Parameter estimation; Sensitivity analysis

Indexed keywords

BIOPROCESSES; EXTRACELLULAR; GLOBAL SENSITIVITY ANALYSIS; HAIRY ROOTS; INTRACELLULAR CONCENTRATION; KINETIC MODELS; MODEL PARAMETERS; MODEL REDUCTION; MODELING; PARAMETRIC SENSITIVITY; PLANT GROWTH; PLANT NUTRITION; PREDICTIVE MODELS; SUSPENSION CELL;

BIOTECHNOLOGY; CARBOHYDRATES; CELL CULTURE; ENZYME ACTIVITY; PARAMETER ESTIMATION; PLANT CELL CULTURE;

SENSITIVITY ANALYSIS;

CARBOHYDRATE; NITROGEN; PHOSPHATE;

ARTICLE; BIOMASS; CORRELATION ANALYSIS; HAIRY ROOT CULTURE; MICHAELIS MENTEN KINETICS; NONHUMAN; NUTRIENT CONCENTRATION; NUTRITIONAL PARAMETERS; PLANT CELL CULTURE; PLANT GROWTH; PLANT MODEL; PLANT NUTRITION; PREDICTION; SENSITIVITY ANALYSIS; SUSPENSION CELL CULTURE;

CELLS, CULTURED; KINETICS; MODELS, BIOLOGICAL; PLANTS;

EID: 79953151472     PISSN: 00063592     EISSN: 10970290     Source Type: Journal    
DOI: 10.1002/bit.23030     Document Type: Article

References (11)

1. Baltes M, Schneider R, Sturm C, Reuss M. 1994. Optimal experimental design for parameter estimation in unstructured growth models. Biotechnol Prog 10(5): 480-488.
2. Bastin G, Dochain D. 1990. On-line estimation and adaptive control of bioreactors. New York: Elsevier Science Ltd.
3. Cloutier M, Bouchard-Marchand È, Perrier M, Jolicoeur M. 2008. A predictive nutritional model for plant cells and hairy roots. Biotechnol Bioeng 99(1): 189-200.
4. Jolicoeur M, Bouchard-Marchand E, Bècard G, Perrier M. 2003. Regulation of mycorrhizal symbiosis: Development of a structured nutritional dual model. Ecol Model 163(3): 247-267.
5. Joshi M, Seidel-Morgenstern A, Kremling A. 2006. Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems. Metab Eng 8: 447-455.
6. Morris MD. 1991. Factorial sampling plans for preliminary computational experiments. Technometrics 33(2): 161-174.
7. Saltelli A. 2002. Making best use of model valuations to compute sensitivity indices. Comput Phys Commun 145: 280-297.
8. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. 2008. Global sensitivity analysis. The Primer. Chichester: John Wiley & Sons, Ltd.
9. Sobol' IM. 2001. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math Comput Simulation 55: 271-280.
10. Tarantola S, Gatelli D, Mara TA. 2006. Random balance designs for the estimation of the first order global sensitivity indices. Reliab Eng Syst Saf 91: 717-727.
11. Walter È, Pronzato L. 1997. Identification of parametric models from experimental data. Berlin: Springer.