Model-Based Design of Superior Cell Factory: An Illustrative Example of Penicillium chrysogenum View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2012

AUTHORS

I. Emrah Nikerel , Peter J. T. Verheijen , Walter M. van Gulik , Joseph J. Heijnen

ABSTRACT

A dynamic model for metabolic reaction network of Penicillium chrysogenum, coupling the central metabolism to growth, product formation and storage pathways is presented. In constructing the model, we started from an existing stoichiometric model, and systematically reduced this initial model to a one compartment model and further eliminated unidentifiabilities due to time scales. Kinetic analysis focuses on a time scale of seconds, thereby neglecting biosynthesis of new enzymes. We used linlog kinetics in representing the kinetic rate equations of each individual reaction. The final parameterization is performed for the final reduced model using previously published short term glucose perturbation data. The constructed model is a self-contained model in the sense that it can also predict the cofactor dynamics. Using the model, we calculated the Metabolic Control Analysis (MCA) parameters and found that the interplay among the growth, product formation and production of storage materials is strongly governed by the energy budget in the cell, which is in agreement with the previous findings. The model predictions and experimental observations agree reasonably well for most of the metabolites. More... »

PAGES

221-270

References to SciGraph publications

Book

TITLE

Systems Metabolic Engineering

ISBN

978-94-007-4533-9
978-94-007-4534-6

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-94-007-4534-6_8

DOI

http://dx.doi.org/10.1007/978-94-007-4534-6_8

DIMENSIONS

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43 schema:description A dynamic model for metabolic reaction network of Penicillium chrysogenum, coupling the central metabolism to growth, product formation and storage pathways is presented. In constructing the model, we started from an existing stoichiometric model, and systematically reduced this initial model to a one compartment model and further eliminated unidentifiabilities due to time scales. Kinetic analysis focuses on a time scale of seconds, thereby neglecting biosynthesis of new enzymes. We used linlog kinetics in representing the kinetic rate equations of each individual reaction. The final parameterization is performed for the final reduced model using previously published short term glucose perturbation data. The constructed model is a self-contained model in the sense that it can also predict the cofactor dynamics. Using the model, we calculated the Metabolic Control Analysis (MCA) parameters and found that the interplay among the growth, product formation and production of storage materials is strongly governed by the energy budget in the cell, which is in agreement with the previous findings. The model predictions and experimental observations agree reasonably well for most of the metabolites.
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