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Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature
I Stamati1, F Logist1, E Van Derlinden1
1BioTeC & OPTEC, Chemical Engineering Department, KU Leuven, W. de Croylaan 46, 3001 Leuven, Belgium.
Mathematical Biosciences
|February 1, 2014
Summary
Optimal experimental design for model discrimination (OED-MD) helps select the best mathematical model for microbial growth. The Schwaab-approach showed superior performance over the T12-criterion in simulations for temperature-dependent growth models.
Area of Science:
- Predictive microbiology
- Mathematical modeling
- Experimental design
Background:
- Mathematical models are crucial for understanding microbial dynamics.
- Multiple models often exist for similar biological processes.
- Selecting the most accurate model is essential for reliable predictions.
Purpose of the Study:
- To compare the effectiveness of two optimal experimental design for model discrimination (OED-MD) methods.
- To discriminate between rival models describing microbial growth rate as a function of temperature.
- To evaluate the T12-criterion and the Schwaab-approach in silico.
Main Methods:
- In silico simulation study.
- Application of the T12-criterion (Atkinson and Fedorov, 1975).
- Application of the Schwaab-approach (Schwaab et al., 2008).
Main Results:
- Both OED-MD methods successfully designed inputs for model discrimination.
- The Schwaab-approach yielded inputs with higher discrimination potential.
- The Schwaab-approach resulted in more accurate parameter estimates compared to the T12-criterion.
Conclusions:
- Mathematical models can be effectively discriminated using OED-MD.
- The Schwaab-approach is a more effective method for discriminating temperature-dependent microbial growth models.
- Accurate model selection enhances the reliability of predictive microbiology.
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