Baltes, M., Schneider, R., Sturm, C., and Reuss, M. (1994). Optimal experimental design for parameter estimation in unstructured growth models. Biotechnol. Prog., 10(5), 480-488.
Bastin, G. and Dochain, D. (1990). On-line Estimation and Adaptive Control of Bioreactors. Elsevier Science Ltd.
Cloutier, M., Bouchard-Marchand, É., Perrier, M., and Jolicoeur, M. (2008). A predictive nutritional model for plant cells and hairy roots. Biotechnol. Bioeng., 99(1), 189-200.
Jolicoeur, M., Bouchard-Marchand, É., Bécard, G., and Perrier,M. (2003). Regulation of mycorrhizal symbiosis: development of a structured nutritional dual model. Ecol. Modell., 163(3), 247-267.
Joshi, M., Seidel-Morgenstern, A., and Kremling, A. (2006). Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems. Metab. Eng., 8, 447-455.
Saltelli, A. (2002). Making best use of model valuations to compute sensitivity indices. Comput. Phys. Commun., 145, 280-297.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S. (2008). Global Sensitivity Analysis. The Primer. John Wiley & Sons, Ltd.
Sobol', I.M. (2001). Global sensitivity indices for nonlinear mathematical models and their monte carlo estimates. Mathematics and Computers in Simulation, 55, 271-280.
Tarantola, S., Gatelli, D., and Mara, T. (2006). Random balance designs for the estimation of the first order global sensitivity indices. Reliab. Eng. Syst. Saf., 91, 717-727.
Walter, É . and Pronzato, L. (1997). Identification of Parametric Models from Experimental Data. Springer-Verlag, Berlin-Heidelberg.