Improving Bioprocess Productivity Using Constraint-Based Models in a Dynamic Optimization Scheme

Modulating the expression of target genes is an effective metabolic engineering approach to increase bioprocess productivity. In this work, a bilevel optimization framework is applied for dynamic manipulation of gene expression based on constraint-based models. A dynamic model in the inner problem captures the network dynamics and enables temporal regulation of the metabolic network. Through linearization of nonlinear constraints, this framework is based on linear optimization which saves on computation time especially in a bilevel structure. A case study involving batch fermentation of Escherichia coli for ethanol production is considered to find an optimal manipulation strategy of metabolic networks for maximal productivity.