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Successful biotechnol. applications, such as amino acid prodn., have demonstrated significant improvement in bioprocess performance by genetic modifications of metabolic control architectures and enzyme expression levels. However, the stoichiometric complexity of metabolic pathways, along with their strongly nonlinear nature and regulatory coupling, necessitates the use of structured kinetic models to direct exptl. applications and aid in quant. understanding of cellular bioprocesses. A novel optimization problem is introduced here, the objective of which is to identify changes in the regulatory characteristics of pertinent enzymes and in their cellular content which should be implemented to optimize a particular metabolic process. The math. representation of the metabolic reaction networks used is the S-system representation, which at steady state is characterized by linear equations. Exploiting the linearity of the representation, the optimization problem was formulated as a mixed-integer linear programming (MILP) problem. This formulation allows the consideration of a regulatory superstructure that contains all alternative regulatory structures that can be considered for a given pathway. The proposed approach is developed and illustrated using a simple linear pathway. Application of the framework on a complicated pathway - namely, the xanthine monophosphate (XMP) and guanosine monophosphate (GMP) synthesis pathway - identified the modification of the regulatory architecture that, along with changes in enzyme expression levels, can increase the XMP and GMP concn. by >114-fold the ref. value, which is 50-fold more than could be achieved by changes in enzyme expression levels only. [on SciFinder (R)]