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Developing Next-Generation Predictive Models: Systems Biology Approach

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Advances in Food Process Engineering Research and Applications

Part of the book series: Food Engineering Series ((FSES))

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Abstract

In the area of predictive microbiology, most models focus on simplicity and general applicability and can be classified as black box models with the main emphasis on the description of the macroscopic (population level) microbial behavior as a response to the environment. Their validity to describe pure cultures in simple, liquid media under moderate environmental conditions is widely illustrated and accepted. However, experiments have shown that extrapolation of these models outside the range of experimental validation is not allowed as such. In general, the applicability and robustness of existing models under a wider range of conditions and in more realistic situations can definitely be improved upon by unraveling the underlying mechanisms and incorporating intracellular (microscopic) information. Following a systems biology approach, the link between intracellular fluxes and extracellular measurements is established by techniques of metabolic flux analysis. The modeling approach presented in this chapter will lead to more accurate predictive models for more complex systems, such as cocultures and structured environments based on a top-down systems biology approach.

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Acknowledgments

This research was supported in part by Project PFV/10/002 (Center of Excellence OPTEC Optimization in Engineering) and OT/10/035 of the KU Leuven Research Fund, Project KP/09/005 (SCORES4CHEM) of the KU Leuven Industrial Research Fund, Project FWOG-08-00360 of the Research Foundation-Flanders, and the Belgian Program on Interuniversity Poles of Attraction, initiated by the Belgian Federal Science Policy Office. J.F. Van Impe holds the chair in Safety Engineering sponsored by the Belgian Chemistry and Life Sciences Federation Essenscia. D. Vercammen was supported by a doctoral grant of the Agency for Innovation through Science and Technology (IWT).

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Authors and Affiliations

  1. BioTeC -Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, Katholieke Universiteit Leuven, W. de Croylaan 46, B-3001, Heverlee, Belgium

    D. Vercammen, E. Van Derlinden, F. Logist & J. F. Van Impe

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  1. D. Vercammen
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  2. E. Van Derlinden
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  3. F. Logist
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  4. J. F. Van Impe
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Correspondence to J. F. Van Impe .

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Editors and Affiliations

  1. Agricultural University of Athens Dept. Food Science & Technology, Athens, Greece

    Stavros Yanniotis

  2. School of Chemical Engineering Laboratory of Food Chemistry and Technol, National Technical University of Athens Division IV-Product and Process Developm, Athens, Greece

    Petros Taoukis

  3. Department of Food Science and Technolog, Agricultural University of Athens, Athens, Greece

    Nikolaos G. Stoforos

  4. Department of Nutrition and Dietetics, Harokopion University, Kallithea, Athens, Greece

    Vaios T. Karathanos

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Vercammen, D., Van Derlinden, E., Logist, F., Van Impe, J.F. (2013). Developing Next-Generation Predictive Models: Systems Biology Approach. In: Yanniotis, S., Taoukis, P., Stoforos, N., Karathanos, V. (eds) Advances in Food Process Engineering Research and Applications. Food Engineering Series. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-7906-2_27

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