A machine learning‐based approach for improving plasmid DNA production in Escherichia coli fed‐batch fermentations

Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective process control leads to highly unstable plasmid yields. In this study, a multi‐parameter correlation analysis was first performed to discover a dynamic metabolic balance among the oxygen uptake rate, temperature, and plasmid yield, whilst revealing the heating rate and timing as the most important optimization factor for balanced cell growth and plasmid production. Then, based on the acquired on‐line parameters as well as outputs of kinetic models constructed for describing process dynamics of biomass concentration, plasmid yield, and substrate concentration, a machine learning (ML) model with Random Forest (RF) as the best machine learning algorithm was established to predict the optimal heating strategy. Finally, the highest plasmid yield and specific productivity of 1167.74 mg L−1 and 8.87 mg L−1/OD600 were achieved with the optimal heating strategy predicted by the RF model in the 50 L bioreactor, respectively, which was 71% and 21% higher than those obtained in the control cultures where a traditional one‐step temperature upshift strategy was applied. In addition, this study transformed empirical fermentation process optimization into a more efficient and rational self‐optimization method. The methodology employed in this study is equally applicable to predict the regulation of process dynamics for other products, thereby facilitating the potential for furthering the intelligent automation of fermentation processes. Graphical and Lay Summary A real‐time iterative machine learning (ML) model was developed to optimize the process control strategy for improving plasmid production in Escherichia coli fed‐batch fermentations. The optimized process control strategy was then successfully validated to prove the predictability of the established ML model.