Mathematical description of metabolic systems allows the calculation of the expected responses of metabolism to genetic modifications and the identification of the most promising targets for metabolic engineering. Metabolic control analysis (MCA) provides such a description in the form of quantitative indices (elasticities and control coefficients). These indices are determined by perturbation experiments around a reference steady state and, therefore, the predictive power of MCA is limited to small changes in the metabolic parameters. The modeling framework introduced here allows accurate description of the metabolic responses over wide range of changes in the metabolic parameters. The framework requires information about the MCA indices at the reference state and the corresponding values of the metabolic reaction rates, and employs simplifying assumptions about the reaction mechanisms. It is shown that knowledge of the intracellular metabolite concentrations is not necessary for the application of the framework. The performance of the methodology is illustrated using three elementary metabolic systems that display highly nonlinear responses to the modification in their parameters: an unbranched pathway, an interconvertible enzyme system, and a branched pathway subject to feedback inhibition.