In order to enable future pathway engineering of a complex system, such as the nitrogen metabolism in yeast, mathematical modelling tools have to be developed. The stoichiometric and biochemical characteristics of the glutamate and glutamine nodes (the Central Nitrogen Metabolism, CNM) are qualitatively known. Quantitative knowledge about the dynamics of the network lacks and needs to be developed for metabolic reprogramming. A model-based-experiment approach is proposed in which the development of a model initiates new experiments of which the results then improve the model. As a first step in this iterative system identification cycle, recent experimental data, both qualitative and quantitative, obtained from defined studies on the CNM of the yeast Saccharomyces cerevisiae have been translated into an initial mathematical model. The model approach is based on a combination of Flux Analysis and simple enzyme kinetics. The model is constructed using nonlinear Ordinary Differential Equations and regulation of the synthesis and activity of key enzymes of the CNM is included. The parameters of the model are estimated with a constrained Least Squares algorithm using the steady-state and dynamic pulse data of a glutamine limited continuous culture. The resulting model describes a continuous culture of a wild-type strain correctly and in general the trends of the dynamic behaviour after both glutamine and ammonia pulses to this culture are good. Inclusion of countercurrent reactions and compartmentation in the model is essential for the descriptive quality of the model under dynamic conditions. It is clear that more experimental work is needed. The model indicates that the GOGAT/Glutamine Synthetase (GS) pathway plays a more important physiological stabilizing role in yeast than is generally assumed. New, model-based, experiments have to investigate the function of GOGAT, especially under dynamic conditions. Also redox cofactors and ATP have to be measured. The resulting model is validated with data of similar experiments with a GS-mutant. The quality of the prediction of the behaviour of the mutant is comparable to the descriptive property, which is a very promising result, taking into account the limited dataset compared to the system complexity.

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Journal Article

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van Riel NA, Giuseppin ML, TerSchure EG, Verrips CT

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