Optimization of a multi-product microbial cell factory for multiple objectives - A paradigm for metabolic pathway recipe

Genetic algorithms, which have been successfully employed in various areas of chemical engineering in recent years, enable us to probe deeper into the influences of alternate metabolic pathways on multi-product synthesis. In this chapter, optimization of enzyme activities in Escherichia coli for multiple objectives is proposed and explored using a non-dominated sorting genetic algorithm for multi-objective optimization (MOO) and a detailed, non-linear dynamic model for central carbon metabolism in Escherichia coli. A wide range of optimal enzyme activities regulating the amino acid synthesis is successfully obtained for selected, integrated pathway scenarios. Identical results are obtained for gene knockouts via exhaustive search and interactive branch and bound driven by genetic algorithm. The predicted potential improvements of the optimized metabolic pathway recipe using the MOO strategy are highlighted and discussed in detail.