Analysis and design of metabolic reaction networks via mixed‐integer linear optimization

Abstract Improvements in bioprocess performance can be achieved by genetic modifications of metabolic control structures. A novel optimization problem helps quantitative understanding and rational metabolic engineering of metabolic reaction pathways. Maximizing the performance of a metabolic reaction pathway is treated as a mixed‐integer linear programming formulation to identify changes in regulatory structure and strength and in cellular content of pertinent enzymes which should be implemented to optimize a particular metabolic process. A regulatory superstructure proposed contains all alternative regulatory structures that can be considered for a given pathway. This approach is followed to find the optimal regulatory structure for maximization of phenylalanine selectivity in the microbial aromatic amino acid synthesis pathway. The solution suggests that from the eight feedback inhibitory loops in the original regulatory structure of this pathway, inactivation of at least three loops and overexpression of three enzymes will increase phenylalanine selectivity by 42%. Moreover, novel regulatory structures with only two loops, none of which exists in the original pathway, could result in a selectivity up to 95%.