Abundant in nature lignocellulosic plant biomass represent a promising, sustainable, source of energy as it can be converted into fuel ethanol through the fermentation of one of its prevalent sugar constituents – xylose. Xylose-utilizing recombinant Saccharomyces cerevisiae strains are engineered to overcome the potential bottlenecks of xylose uptake and cofactor imbalance, and to minimize the xylitol accumulation. In this work, we apply a novel computational framework to identify metabolic engineering targets for the optimization of ethanol production by Saccharomyces cerevisiae in the fed-batch culture. We employ a methodology that uses Monte Carlo sampling to explore the kinetic data space, and it accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints. We model the kinetic responses of the individual reactions, and we integrate this mechanistic information with the thermodynamically feasible pairs of substrate and product concentrations to build a population of all biochemically and thermodynamically meaningful models of the Xylose-utilizing recombinant Saccharomyces cerevisiae strains. We find that redistribution of the metabolic flows around pyruvate among carboxylation, decarboxylation, and transport has significant potential in improving total utilization of hexose and pentose sugar as well as its conversion into ethanol.