Optimization of fermentation conditions for the production of human soluble catechol-O-methyltransferase by Escherichia coli using artificial neural network.

The aim of this work was to optimize the temperature, pH and stirring rate of the production of human soluble catechol-O-methyltransferase (hSCOMT) in a batch Escherichia coli culture process. A central composite design (CCD) was firstly employed to design the experimental assays used in the evaluation of these operational parameters on the hSCOMT activity for a semi-defined and complex medium. Predictive artificial neural network (ANN) models of the hSCOMT activity as function of the combined effects of these variables was proposed based on this exploratory experiments performed for the two culture media. The regression coefficients (R(2)) for the final models were 0.980 and 0.983 for the semi-defined and complex medium, respectively. The ANN models predicted a maximum hSCOMT activity of 183.73 nmol/h, at 40 °C, pH 6.5 and stirring rate of 351 rpm, and 132.90 nmol/h, at 35 °C, pH 6.2 and stirring rate of 351 rpm, for semi-defined and complex medium, respectively. These results represent a 4-fold increase in total hSCOMT activity by comparison to the standard operational conditions used for this bioprocess at slight scale.