Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature: From in silico to in vivo.

Temperature is an important food preservation factor, affecting microbial growth. Secondary predictive models can be used for describing the impact of this factor on microbial growth. In other words, the microbial behavior can be described in a dynamic environment with the use of a primary and secondary model. Two models for describing the effect of temperature on the microbial growth rate are the cardinal temperature model with inflection (CTMI) (Rosso et al., 1993) and its adapted version (aCTMI) (Le Marc et al., 2002). Although Escherichia coli is commonly modeled using CTMI, there are indications that aCTMI may be more appropriate (Van Derlinden and Van Impe, 2012a). For clarifying this, the method of Optimal experiment design for model discrimination (OED/MD) will be used in this work (Donckels et al., 2009; Schwaab et al., 2008). Results from an in silico study point out the required direction. Whereas the results of the in vivo study give a more realistic answer to the research question. Finally, discrimination unravelled the appropriate model for the needed use.

[1]  J. Jacquez,et al.  Numerical parameter identifiability and estimability: Integrating identifiability, estimability, and optimal sampling design , 1985 .

[2]  K Bernaerts,et al.  Simultaneous versus sequential optimal experiment design for the identification of multi-parameter microbial growth kinetics as a function of temperature. , 2010, Journal of theoretical biology.

[3]  Bernard De Baets,et al.  An anticipatory approach to optimal experimental design for model discrimination , 2009 .

[4]  Eric Walter,et al.  Identification of Parametric Models: from Experimental Data , 1997 .

[5]  I. Chou,et al.  Recent developments in parameter estimation and structure identification of biochemical and genomic systems. , 2009, Mathematical biosciences.

[6]  D. Ucinski,et al.  T‐optimum designs for discrimination between two multiresponse dynamic models , 2005 .

[7]  J. F. Van Impe,et al.  Modeling microbial kinetics as a function of temperature: Evaluation of dynamic experiments to identify the growth/inactivation interface , 2012 .

[8]  T. Ross,et al.  Modelling the effects of temperature, water activity, pH and lactic acid concentration on the growth rate of Escherichia coli. , 2003, International journal of food microbiology.

[9]  J Van Impe,et al.  Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature. , 2014, Mathematical biosciences.

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

[11]  José Carlos Pinto,et al.  Sequential experimental design for model discrimination: Taking into account the posterior covariance matrix of differences between model predictions , 2008 .

[12]  J P Guyonnet,et al.  Modelling the growth kinetics of Listeria as a function of temperature, pH and organic acid concentration. , 2002, International journal of food microbiology.

[13]  Kenneth Davey,et al.  Validation of a model for predicting the combined effect of three environmental factors on both exponential and lag phases of bacterial growth: temperature, salt concentration and pH , 1995 .

[14]  Jan Van Impe,et al.  Design of dynamic experiments for discrimination between models for microbial growth kinetics as a function of temperature , 2012 .

[15]  Steven P. Asprey,et al.  On the design of optimally informative dynamic experiments for model discrimination in multiresponse nonlinear situations , 2003 .

[16]  J. Baranyi,et al.  Predictive Microbiology - Quantitative Microbial Ecology Culture - March 2004 , 2004 .

[17]  Bernard De Baets,et al.  An ideal point method for the design of compromise experiments to simultaneously estimate the parameters of rival mathematical models , 2010 .

[18]  József Baranyi,et al.  Predictive modelling of Salmonella: From cell cycle measurements to e-models , 2012 .

[19]  J P Flandrois,et al.  The particular behaviour of Listeria monocytogenes under sub-optimal conditions. , 1996, International journal of food microbiology.

[20]  A. Atkinson,et al.  The design of experiments for discriminating between two rival models , 1975 .

[21]  J P Flandrois,et al.  An unexpected correlation between cardinal temperatures of microbial growth highlighted by a new model. , 1993, Journal of theoretical biology.

[22]  E. Van Derlinden,et al.  Modeling growth rates as a function of temperature: model performance evaluation with focus on the suboptimal temperature range. , 2012 .

[23]  John E. Dennis,et al.  Normal-Boundary Intersection: A New Method for Generating the Pareto Surface in Nonlinear Multicriteria Optimization Problems , 1998, SIAM J. Optim..

[24]  J. Baranyi,et al.  Quantitative Microbial Ecology of Food , 2005 .

[25]  Filip Logist,et al.  Fast Pareto set generation for nonlinear optimal control problems with multiple objectives , 2010 .

[26]  K Bernaerts,et al.  Accurate estimation of cardinal growth temperatures of Escherichia coli from optimal dynamic experiments. , 2008, International journal of food microbiology.

[27]  K van't Riet,et al.  Modeling of bacterial growth as a function of temperature , 1991, Applied and environmental microbiology.

[28]  A. N. Stokes,et al.  Model for bacterial culture growth rate throughout the entire biokinetic temperature range , 1983, Journal of bacteriology.

[29]  J Baranyi,et al.  A dynamic approach to predicting bacterial growth in food. , 1994, International journal of food microbiology.

[30]  Filip Logist,et al.  On the effect of sampling rate and experimental noise in the discrimination between microbial growth models in the suboptimal temperature range , 2016, Comput. Chem. Eng..

[31]  William G. Hunter,et al.  Designs for Discriminating Between Two Rival Models , 1965 .

[32]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .