The development of kinetic models is still facing the challenges such as large uncertainties in available data. Uncertainty originating from various sources including metabolite concentration levels, flux values, thermodynamic and kinetic data propagates to the kinetic parameter space and prevents an accurate identification of parameters. The uncertainty in parameters propagates further to the outputs of the corresponding kinetic models, such as control coefficients and time evolutions of metabolic fluxes and concentrations, and it can have deleterious effects on metabolic engineering and synthetic biology decisions. We have introduced a framework recently, iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which allows us to characterize uncertainty in the parameters of studied kinetic models and integrate this information into a new population of kinetic models with reduced uncertainty. To this end, iSCHRUNK combines the ORACLE framework with parameter classification techniques. With ORACLE, we first construct a set of large-scale and genome-scale kinetic models that are consistent with the integrated datasets and physicochemical laws. We then employ parameter classification techniques to data-mine the integrated datasets together with the outputs of ORACLE. This way, iSCHRUNK allows us to uncover complex relationships between the integrated data and parameters of kinetic models, and to use the obtained information for constructing an improved set of kinetic models with reduced uncertainty in ORACLE. In this work, we analyzed possible improvements of xylose uptake rate (XTR) in a glucose-xylose co-utilizing S. cerevisiae strain. For this purpose, we computed a population of 200’000 large-scale kinetic models, and we determined the key enzymes controlling XTR. However, large uncertainties due to a limited number of measured flux and metabolite concentration values and lack of data about kinetic parameters led to widespread distributions around zero values for control coefficients of some enzymes such as hexokinase (HXK). We used iSCHRUNK to identify the enzymes and their kinetic parameters that determine negative and positive control of HXK over XTR. Our study showed that by engineering only three saturation states related to two enzymes we could enforce either negative or positive control of HXK over XTR. This result implies that for kinetic modeling of metabolism only a small number of kinetic parameters should be accurately determined and that we can disregard the remaining parameters.