One of the central problems in metabolic engineering deals with the identification of the "rate limiting steps", i.e. enzymes whose activities one should manipulate towards the achievement of a desirable performance. However, uncertainty about the kinetic characteristics of the enzymes involved in the pathway of interest makes such identification almost impossible. On the other hand, extensive research within metabolic engineering has enabled the estn. of intracellular metabolic fluxes. Such information, while it provides significant understanding about the functioning of metabolic pathways and, some times, guidance for metabolic engineering, it does not allow a quant. prediction of the metabolic pathway responses to metabolic engineering actions, such as changes in enzyme activities. We have recently developed a method that overcomes these limitations and allows the identification of metabolic engineering targets for the manipulation of metabolic pathways based on information about the stoichiometry of the pathways and the assocd. values of the metabolic fluxes. The framework employs knowledge about the stoichiometry of biochem. networks and the estd. values of the assocd. metabolic fluxes, modeling concepts from metabolic control anal., computational methods, and nonparametric statistics. We will present and discuss the application of the method to the central carbon pathways in E. coli and S. cerevisiae for the identification of the metabolic engineering strategies (i.e., changes in enzyme activities) with the highest probability of success in optimizing conversion of glucose to ethanol. [on SciFinder (R)]