OPTIMIZATION OF REGULATORY ARCHITECTURES IN METABOLIC REACTION NETWORKS

Successful biotechnological applications, such as amino acid productio n, have demonstrated significant improvement in bioprocess performance by genetic modifications of metabolic control architectures and enzym e expression levels. However, the stoichiometric complexity of metabol ic pathways, along with their strongly nonlinear nature and regulatory coupling, necessitates the use of structured kinetic models to direct experimental applications and aid in quantitative understanding of ce llular bioprocesses. A novel optimization problem is introduced here, the objective of which is to identify changes in the regulatory charac teristics of pertinent enzymes and in their cellular content which sho uld be implemented to optimize a particular metabolic process. The mat hematical representation of the metabolic reaction networks used is th e S-system representation, which at steady state is characterized by l inear equations. Exploiting the linearity of the representation, we fo rmulated the optimization problem as a mixed-integer linear programmin g (MILP) problem. This formulation allows the consideration of a regul atory superstructure that contains all alternative regulatory structur es that can be considered for a given pathway. The proposed approach i s developed and illustrated using a simple linear pathway. Application of the framework on a complicated pathway-namely, the xanthine monoph osphate (XMP) acid guanosine monophosphate (GMP) synthesis pathway-ide ntified the modification of the regulatory architecture that, along wi th changes in enzyme expression levels, can increase the XMP and GMP c oncentration by over 114 times the reference value, which is 50 times more than could be achieved by changes in enzyme expression levels onl y. (C) 1996 John Wiley & Sons, Inc.