Precise information about kinetic properties of enzymes within a biochemical network is a necessary ingredient of any successful metabolic engineering design. Such information inevitably involves uncertainty due to changes in cellular and process parameters. In this contribution, we present a computational methodology that explores the kinetic data space, and accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints. Thus, the proposed framework allows us to characterize the meaningful kinetic responses of metabolic network in the presence of uncertainty. We integrate the kinetic models of the individual reactions with the thermodynamically feasible pairs of substrate and product concentrations to build a population of all biochemically and thermodynamically meaningful models of the metabolic network. The effectiveness of the employed sampling method allows implementing the proposed methodology for modeling of large-scale metabolic networks. We apply the proposed framework for the modeling of the central carbon metabolism of Escherichia Coli.