Prediction of porcine interferon α antiviral activity in fermentation by Pichia pastoris based on multivariable regression and artificial neural network

One of the most important methods to produce porcine interferon α is microbial fermentation. In the present study, recombinant Pichia pastoriswas used. Broth’s antiviral activity is the key index of the expression level of porcine interferon α. Measurement of antiviral activity is a time-consuming and difficult task, which makes the research and production work inconvenient and uncertain. To solve this problem, multivariable regression and artificial neural network were applied to predict the antiviral activity based on five on-line variables (induction time, temperature, dissolve doxygen, O2uptake rate and CO2 evolution rate) and two off-line variables (methanol consumption rate and total protein concentration).Parameters of the multivariable quadratic polynomial regression equation were estimate dusing least square methods. Optimization of artificial neural network(ANN)was achieved by back-propagation and genetic algorithm. Verified by test set, the ANN optimized by genetic algorithm had the best predictive performance and generalization. The sensitivity analysis showed that CO2evolution rate, O2 uptake rate and methanol consumption rate were the most relevant factors for model’s output, except for the antiviral activity’s own previous value