Development of a hybrid neural network model to predict feeding method in fed-batch cultivation for enhanced recombinant streptokinase productivity in Escherichia coli

BACKGROUND: A simple and efficient model for enhancing production of recombinant proteins is essential for cost effective development of processes at industrial scale. A hybrid neural network (HNN) model is proposed combining an unstructured model and neural network to predict the feeding method for the post-induction phase of fed-batch cultivation for increased recombinant streptokinase activity in Escherichia coli. RESULTS: The parameters of the unstructured model were estimated from experiments conducted with various feeding methods. The simulated model described the dynamics of the process satisfactorily, however, its predictive capability of the process for different feeding methods is limited due to wide disparity in process parameters. In contrast, a neural network model trained to map the variations in process parameters to state variables complements the 'first principle' model in predicting the state variables effectively. CONCLUSIONS: The HNN model is able to predict the product profile for different substrate feed rates. Further, the average volumetric streptokinase activity predicted by the HNN model matches closely the experimental values for fed-batches having high as well as low streptokinase activity. The HNN model developed in this study could facilitate development of a process for recombinant protein production with minimum number of experiments. (C) 2011 Society of Chemical Industry