文献类型：期刊文献

英文题名：A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations

作者：Xu, Zhixian[1];Zhu, Xiaofeng[1];Mohsin, Ali[1];Guo, Jianfei[1];Zhuang, Yingping[1];Chu, Ju[1];Guo, Meijin[1,2];Wang, Guan[1,2]

机构：[1]East China Univ Sci & Technol ECUST, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[2]East China Univ Sci & Technol, State Key Lab Bioreactor Engn, 130 Meilong Rd, Shanghai 200237, Peoples R China

年份：2024

卷号：19

期号：6

外文期刊名：BIOTECHNOLOGY JOURNAL

收录：;EI(收录号：20242516291619);WOS:【SCI-EXPANDED(收录号:WOS:001251699200001)】；

基金：This research was funded by the National Key R&D Program of China (Grant no. 2021YFC2101100) and Shanghai Rising-Star Program (Grant no. 21QA1402400).

语种：英文

外文关键词：Escherichia coli; heating rate; machine learning; multi-parameter correlation analysis; plasmid fermentation

摘要：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.