Effective induction of phytase in Pichia pastoris fed-batch culture using an ANN pattern recognition model-based on-line adaptive control strategy

Abstract

An on-line adaptive substrate feeding control system based on DO/pH measurements and artificial neural network pattern recognition (ANNPR) model for fed-batch cultivation processes proposed previously was successfully applied for the phytase production with recombinant Pichia pastoris. The control strategy could effectively improve the fermentation performance for both cultivation and induction phases. With the standard ANNPR model-based control strategy, the cultivation time before starting the methanol induction could be shortened for about 30%. By adequately increasing the feeding rate iterative step size of the ANNPR-based control during induction phase, methanol concentration could be automatically controlled within an optimal range, leading to an approximate three-fold stable increase in phytase activity compared with those obtained by the traditional DO-Stat method and the on-line methanol electrode-based on–off control strategy. The effectiveness, universal ability, as well as the operational simplicity of the proposed control system has been further verified in the recombinant P. pastoris fed-batch culture process.