Effects of spatiotemporal variations on metabolic control: approximate analysis using (log)linear kinetic models

For many metabolic systems, available experimental data allow description of the system by elasticities and control coefficients. The availability of information of this kind motivated the development of a (log)linear kinetic model of metabolic systems that is completely and explicitly determined by this information. It is shown here that this model can accurately describe the dynamic responses of metabolic systems that exhibit strong nonlinearities. Based on the excellent approximation provided by the (log)linear model, a method is developed for the estimation of the performance of metabolic systems subject to spatiotemporal variations of the system parameters and the process operating conditions. The method suggests experiments that can quantify the effect of these variations. Study of a model glycolytic pathway illustrates the applicability and the usefulness of this framework. Time-average flux control coefficients are shown to vary strongly and not monotonically as the period of the external variations changes.