Machine learning modelling for the ultrasonication-mediated disruption of recombinant E. coli for the efficient release of nitrilase

Abstract The ultrasonication-mediated cell disruption of recombinant E. coli was modeled using three machine learning techniques namely Multiple linear regression (MLR), Multi-layer perceptron (MLP) and Sequential minimal optimization (SMO). The four attributes were cellmass concentration (g/L), acoustic power (A), duty cycle (%) and treatment time of sonication (min). For the three responses (nitrilase, total protein release and cell disruption) MLP model was found to be at par with RSM model in terms of generalization as well as prediction capability. Nitrilase release was significantly influenced by the cellmass concentration so was in case of total protein release. Fraction of cells disrupted was heavily influenced by acoustic power and sonication time. Almost 32 U/mL nitrilase could be released for 300 g/L cellmass concentration when sonicated at 225 W for 1 min with 20% duty cycle.