Fuzzy neural network for the control of high cell density cultivation of recombinant Escherichia coli

Abstract

A five-layer fuzzy neural network (FNN) was developed for the control of fed-batch cultivation of recombinant Escherichia coli JM103 harboring plasmid pUR 2921. The FNN was believed to represent the membership functions of the fuzzy subsets and to implement fuzzy inference using previous experimental data. This FNN was then used for compensating the exponential feeding rate determined by the feedforward control element. The control system is therefore a feedforward-feedback type. The change in pH of the culture broth and the specific growth rate were used as the inputs to FNN to calculate the glucose feeding rate. A cell density of 84 g DWC/ l in the fed-batch cultivation of the recombinant E. coli was obtained with this control strategy. Two different FNNs were then employed before and after induction to enhance plasmid-encoded β-galactosidase production. Before induction the specific growth rate was set as 0.31 h −1, while it was changed to 0.1 h −1 after induction. Compared to when only one FNN was used, the residual glucose concentration could be tightly controlled at an appropriate level by employing two FNNs, resulting in an increase in relative activity of β-galactosidase which was about four times greater. The present investigation demonstrates that a feedforward-feedback control strategy with FNN is a promising control strategy for the control of high cell density cultivation and high expression of a target gene in fed-batch cultivation of a recombinant strain.