Simultaneous modeling of enzyme production and biomass growth in recombinant Escherichia coli using artificial neural networks

In this work, the biomass growth and the TaqI endonuclease production by recombinant Esherichia coli were studied using artificial neural networks. The effects of the medium components on biomass growth and enzyme yield were modeled by various networks. After the most successful networks were statistically determined, they were used to extract additional knowledge such as the possible correlations between the biomass growth and the enzyme yield, and the relative significance of the medium components. It was found that the change of the biomass growth and the enzyme yield with the change of KH2PO4 concentration was strongly correlated with an R-value of −0.954. Some mild correlations were also observed for the other components. It was also found that the relative significances of the medium components were in the same order for both outputs; (NH4)2HPO4 concentration was determined as the most important parameter followed by the glucose, KH2PO4 and MgSO4 concentrations.