Prediction of Dynamic Plasmid Production by Recombinant Escherichia coli Fed-Batch Cultivations with a Generalized Regression Neural Network

T. Silva, P. Lima, M. Roxo-Rosa, S. Hageman, L. P. Fonseca and C. R. C. Calado

Abstract
A generalized regression neural network with external feedback was used to predict plasmid production in a fed-batch cultivation of recombinant Escherichia coli. The neural network was built out of the experimental data obtained on a few cultivations, of which the general strategy was based on an initial batch phase followed by an exponential feeding phase. The different cultivation conditions used resulted in significant differences in bacterial growth and plasmid production. The obtained model allows estimation of the experimental outputs (biomass, glucose, acetate and plasmid) based on the bioreactor starting conditions and the following on-line inputs: feeding rate, dissolved oxygen concentration and bioreactor stirring speed. Therefore, the proposed methodology presents a quick, simple and reliable way to perform on-line feedback prediction of the dynamic behaviour of the complex plasmid production process, based on simple on-line input data obtained directly from the bioreactor control unit and with few cultivation experiments for neural network learning.

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Keywords
neural network, fed-batch cultivation, plasmid production