Productivity improvement of recombinant Escherichia coli fermentation via robust optimization

A nonlinear model of a recombinant Escherichia coli producing porcine growth hormone (pGH) fermentation was developed. The model was used to calculate a glucose feeding and temperature strategy to optimize the production of pGH. Simulations showed that the implementation of optimal feed and temperature profiles was sensitive to the maximum specific growth rate, and a mismatch could result in excessive acetate production and a significant reduction in pGH yield. An optimization algorithm was thus developed, using feedback control, to counter the effects of uncertainty in the specific growth rate and thus determine an optimal operating strategy for pGH production. This policy was experimentally implemented in a 10 L fermenter and resulted in a 125% increase in productivity over the previous best experimental result with this system—in spite of significant plant-model mismatch.