Plant metabolism is highly complex, and often metabolic pathways contain cycles or various branch points that are difficult to follow by intuition. Measuring flux in a metabolic network thus affords complicated methods like puls-chase labelling with radioactive intermediates. An alternative approach is the dynamic modelling of turnover based on systems of differential equations. These mathematical methods allow calculation of kinetic parameters of enzymes based on the dynamics of metabolite concentrations, and thus allow turnover in an entire metabolic network to be assessed.
The goal of modelling is prediction of changes in metabolism that result from changes in enzyme activity. This would allow an a priori screening of breeding strategies that lead to higher yield, better performance or tolerance to stress. It would also enable in silico production of mutants in order to test the physiological importance of certain enzymes. Double or triple mutants could be created by in silico crossings, and these would reveal, whether it would be worthwhile to invest the effort of producing these mutants.
For example, we have used mathematical modelling to test the function of the dominant form of vacuolar invertase, an enzyme from sugar metabolism that splits up sucrose into glucose and fructose. Simulations showed that this enzyme stabilizes sugar concentrations in the cytosol, thus helping to maintain metabolic homeostasis.
These results also show the importance of subcellular compartmentation of metabolites for functionality of metabolism.
Vacuolar invertases not only contribute to metabolism of source organs: they are also involved in the regulation of sink strength and thus contribute to the regulation of sink/source interactions.
Even for the sudy of acclimation to adverse environmental conditions like cold, mathematical modelling is a very powerful method. We could show that different natural populations of Arabidopsis employ different strategies during cold acclimation, and these results can be employed to improve breeding for higher freezing tolerance.