Bioprocess modelling presents a challenging subject, which requires a meticulous modelling strategy. During the modelling process, experimental data form a key ingredient during structure characterization and parameter estimation. Accurate system identification can only be guaranteed if the experimental data contain sufficient information on the process dynamics. In this respect, sufficient effort should be spent on optimal experiment design in order to maximize the information that can be extracted from data, particularly because experimental data generation for bioprocesses is usually a time-consuming, labour-intensive and costly job. This paper reviews the modelling cycle of bioprocesses, emphasizing the need for careful experimental data collection. The concepts of optimal experiment design for parameter estimation are outlined in particular. Application of this methodology is illustrated for a case study involving the optimal estimation of two model parameters describing temperature dependence of microbial growth kinetics.
Asprey, S. P. and Macchietto S.2000: Statistical tools for optimal dynamic model building. Computers and Chemical Engineering24, 1261-1267.
2.
Bailey, J. E. and Ollis, D. F.1986: Biochemical engineering fundamentals. New York: McGraw-Hill.
3.
Balsa-Canto, E., Alonso, A. A. and Banga, J. R. 1998: Dynamic optimization of bioprocesses: deterministic and stochastic strategies. In: Skjöldebrand, C. and Trystram, G., editors. Proceedings of ACoFoP IV Automatic control of food and biological processes. Göteborg: SIK The Swedish Institute for Food and Biotechnology, 271-276.
4.
Baltes, M., Schneider, R.Sturm, C. and Reuss, M.1994: Optimal experimental design for parameter estimation in unstructured growth models. Biotechnological Progress10, 480-488.
5.
Banga, J. R., Versyck, K. J. and Van Impe, J. F.2002: Computation of optimal identification experiments for nonlinear dynamic process models: a stochastic global optimization approach. Industrial and Engineering Chemistry Research41, 2425-2430.
6.
Banga, J. R., Balsa-Canto, E., Moles, C. G. and Alonso, A. A.2003: Improving food processing using modern optimization methods. Trends in Food Science and Technology, 14, 131-144.
7.
Baranyi, J. and Roberts, T. A.1994: A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology23, 277-294.
8.
Barton, P. I., Allgor, R. J., Feehery, W. F. and Galaan, S.1998: Dynamic optimisation of a discontinuous world. Industrial and Engineering Chemistry Research37, 966-981.
9.
Bates, D. M. and Watts D. G.1988: Nonlinear regression analysis and its applications. New York: John Wiley.
10.
Bernaerts, K. and Van Impe, J. F. 2002: Optimal dynamic experiment design for estimation of microbial growth kinetics at sub-optimal temperatures: modes of implementation. In: O’Connor, B. and Thiel, D., editors. Proceedings of the Second International Conference on Simulation in food and bio-industry. Blarney (Ireland), 212-216.
11.
Bernaerts, K., Versyck, K. J. and Van Impe, J. F.2000: On the design of optimal dynamic experiments for parameter estimation of a ratkowsky-type growth kinetics at suboptimal temperatures. International Journal of Food Microbiology54, 27-38.
12.
Bernaerts, K., Servaes, R. D., Kooyman, S. and Van Impe, J. F. 2001: Iterative optimal experiment design for estimation of microbial growth kinetics as function of temperature. In: Dochain, D. and Perrier, M., editors. 8th International Conference on Computer Applications in Biotechnology. Québec, 19-24.
13.
Bernaerts, K., Servaes, R. D., Kooyman, S., Versyck, K. J. and Van Impe, J. F.2002. Optimal temperature input design for estimation of the Square Root model parameters: parameter accuracy and model validity restrictions. International Journal of Food Microbiology, Special issue73, 147-159.
14.
Box, G. E. P. and Hill, W. J.1976: Discrimination among mechanistic models. Technometrics9, 57-71.
15.
Box, M.1970: Some experiences with a nonlinear experimental design criterion. Techno-metrics12, 569-589.
16.
Box, M. and Draper, N. R.1971: Factorial designs, the X’X criterion, and some related matters. Technometrics13, 731-742.
17.
Chappell, M. J.1995: Structural identifiability and indistinguishability of certain two-compartment models incorporating nonlinear efflux from the peripheral compartment. Mathematical Biosciences125, 61-81.
18.
Chappell, M. J. and Godfrey, K. R.1991: Structural identifiability of nonlinear systems: application to a batch reactor. In: Baanyaasz, C. and Keviczky, L., editors. Identification and system parameter estimation. Preprints of the 9th IFAC/IFORS Symposium. Budapest, 492-497.
19.
Chapell, M. J., Godfrey, K. F. and Vadja, S.1990: Global identifiability of the parameters of nonlinear systems with specified inputs: a comparison of methods. Biosciences102, 41-73.
20.
Cooney, M. J. and McDonald, K. A.1995: Optimal dynamic experiments for bioreactor model discrimination. Applied Microbiology Biotechnology43, 826-837.
21.
Dochain, D., Vanrolleghem, P. A. and Van Daele, M.1995: Structural identifiability of biokinetic models of activated sludge respiration. Water Research29, 2571-2578.
22.
Ejiofor, A. O., Posten, C. H., Solomon, B. O. and Deckwer, W.-D.1994: A robust fed-batch feeding strategy for optimal parameter estimation for baker’s yeast production. Bioprocess Engineering11, 135-144.
23.
Espie, D. and Machietto, S.1989: The optimal design of dynamic experiments. AIChE Journal35, 223-229.
24.
Godfrey, K. R. and Distefano, J. J.1985: Identifiability of model parameters. In: Barker, H. A., and Young, P. C., editors. 3rd IFAC Identification and System Parameter Estimation. Oxford: Pergamon Press, 89-114.
25.
Goodwin, G. C. and Payne, R. L.1977: Dynamic system identification: experiment design and data analysis. New York: Academic Press.
26.
Holmberg, A. and Ranta, J.1982: Procedures for parameter and state estimation of microbial growth process models. Automatica18, 181-193.
27.
Körkel, S., Bauer, I. and Schölder, J. P. 1999: A sequential approach for nonlinear optimum experimental design in DAE systems. In: Keil, F., editor. Proceedings of the international workshop on scientific computing in chemical engineering, Vol. 2. Berlin, Springer, 338-345.
28.
Ljung, L.1999: System identification: theory for the user, 2nd edition. Upper Saddle River, NJ: Prentice Hall.
29.
McMeekin, T. A., Olley, J. N., Ross, T. and Ratkowsky, D. A.1993: Predictive microbiology: Ko theory and application. Exeter: Research Studies Press.
30.
Mehra, R. K.1974: Optimal input signals for parameter estimation in dynamic systems survey and new results. IEEE Transactions on Automatic Control19, 753-768.
31.
Merkel, W., Schwarz A., Fritz, S., Reuss, M. and Krauth, K.1996: New strategies for estimating kinetic parameters in anaerobic wastewater treatment plants. Water Science and Technology34, 393-401.
32.
Munack, A.1989: Optimal feeding strategy for identification of Monod-type models by fed-batch experiments. In: Fish, N. M., Fox, R. I., Thornhill, N. F., editors. Computer applications in fermentation technology, modeling and control of biotechnological processes. Amsterdam: Elsevier, 195-204.
33.
Munack, A.1991: Optimization of sampling. In: Rehm, H.-J. and Reed, G., editors. Biotechnology, a multi-volume comprehensive treatise, Vol. 4, Measuring, modeling and control, Vol. 4. Weinheim: VCH, 252-264.
34.
Munack, A.1992: Some improvements in the identification of bioprocesses. In: Modeling and control of biotechnical processes. Oxford: Pergamon Press, 89-94.
35.
Munack, A. and Posten, C. 1989: Design of optimal dynamical experiments for parameter estimation. In: Proceedings of the American Control Conference, Pittsburgh, 2010-2016.
36.
Neter, J., Wasserman, W. and Kutner, M. H.1990: Applied linear statistical models. Boston: Irwin.
37.
Pohjanpalo, H.1978: System identifiability based on the power series expansion of the solution. Mathematical Biosciences4, 21-33
38.
Ratkowsky, D. A., Olley, J., McMeekin, T. A. and Ball, A.1982: Relationship between temperature and growth rate of bacterial cultures. Journal of Bacteriology149, 1-5.
39.
Rosso, L. 1995: Modélisation et microbiologie prévisionnelle: Elaboration d’un nouvel outil pour l’agro-alimentaire. PhD thesis, Université Claude Bernard-Lyon I. Villeurbannne Cedex, France.
40.
Steinberg, D. M. and Hunter, W. G.1984: Experimental design: review and comment. Technometrics26, 71-97.
41.
Sydall, M. T., Paul, G. C. and Kent, C. A. 1998: Improving the estimation of parameters of penicillin fermentation models. In: Yoshida, T. and Shioya, S., editors. Preprints of the 7th International Conference on Computer applications in biotechnology. Osaka, 23-28.
42.
Takors, R., Wiechert, W. and Weuster-Botz, D. D.1997: Experimental design for the identification of macrokinetic models and model discrimination. Biotechnology and Bioengineering56, 564-576.
43.
Vanrolleghem, P. A. and Dochain, D.1998: Bioprocess model identification. In: Van Impe, J. F., Vanrolleghem, P. A. and Iserentant, D. M., editors. Advanced instrumentation, data interpretation and control of biotechnological processes. Dordrecht: Kluwer, 250-318.
44.
Vanrolleghem, P. A., Van Daele, M. and Dochain D.1995: Practical identifiability of a bio-kinetic model of activated sludge respiration. Water Research29, 2561-2570.
45.
Versyck, K. J., Bernaerts, K., Geeraerd, A. H. and Van Impe, J. F.1999: Introducing optimal experimental design in predictive microbiology: a motivating example. International Journal of Food Microbiology51, 39-51.
46.
Walter, E. and Pronzato, L.1997: Identification of parametric models from experimental data. Masson: Springer.
