Kinetic modeling of plasmid bioproduction in Escherichia coli DH5α cultures over different carbon-source compositions

The need for the development of economic high plasmid production in Escherichia coli cultures is emerging, as a result of the latest advances in DNA vaccination and gene therapy. In order to contribute to achieve that, a model describing the kinetics involved in the bioproduction of plasmid by recombinant E. coli DH5α is presented, as an attempt to understand the complex and non-linear metabolic relationships and the plasmid production occurring in dynamic batch culture environments, run under different media compositions of glucose and glycerol, that result in distinct maximum biomass growths (between 8.2 and 12.8 g DCW/L) and specific plasmid productions (between 1.1 and 7.4 mg/g DCW). The model based on mass balance equations for biomass, glucose, glycerol, acetate and plasmid accurately described different culture behaviors, using either glucose or glycerol as carbon source, or mixtures of both. From the 17 parameters obtained after model simplification, the following 10 parameters were found to be independent of the carbon source composition: the substrate affinity constants, the inhibitory constants of biomass growth on glycerol by glucose, of biomass growth on acetate by glycerol and the global biomass growth by acetate, and the yields of biomass on acetate, acetate on glucose and glycerol, and plasmid on glucose. The parameters that depend on the culture composition, and that might explain the differences found between cultures, were: maximum specific growth rates on glucose, glycerol and acetate; biomass yield on glucose and glycerol; and plasmid yield on glycerol and acetate. Moreover, a crucial role of acetate in the plasmid production was revealed by the model, with most of plasmid production being associated to the acetate consumption. The model provides meaningful insight on the E. coli dynamic cell behavior concerning the plasmid bioproduction, which might lead to important guidelines for culture optimization and process scale-up and control.