文献类型：期刊文献

英文题名：Artificial intelligence-guided fermentation process regulation reveals metabolic rewiring in gentamicin C1a overproduction

作者：Xu, Feng[1,2,3];Su, Lihuan[1,2];Wang, Yuan[1,2];Gao, Hao[1,2];Hu, Kaihao[1,2];Ben, Rong[1,2];Guo, Yuanxin[1,2];Li, Xu[1];Chu, Ju[1,2,3];Tian, Xiwei[1,2,3]

机构：[1]East China Univ Sci & Technol, Qingdao Innovat Inst, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Natl Ctr Bioengn & Technol Shanghai, Shanghai 200237, Peoples R China;[3]East China Univ Sci & Technol, Shanghai Collaborat Innovat Ctr Biomfg Technol, Shanghai 200237, Peoples R China

年份：2025

卷号：436

外文期刊名：BIORESOURCE TECHNOLOGY

收录：;EI(收录号：20252818773645);WOS:【SCI-EXPANDED(收录号:WOS:001539103200001)】；

基金：This work was financially supported by the National Key Research and Development Program of China (2024YFA0917900) , the Taishan Scholars Program of Shandong Province (NO.tsqn202312316) , the Shanghai Pilot Program for Basic Research (22TQ1400100-14) , the Shanghai Science and Technology Innovation Action Plan (24HC2810100) , the Natural Science Foundation of Shanghai (23ZR1416500) , the Fundamental Research Funds for the Central Uni-versities (JKV01251708) . Thanks for the financial support from the Arawana Charity Foundation.

语种：英文

外文关键词：Fermentation optimization; Artificial neural network; Multi-sensor monitoring; Genetic algorithm; Metabolic flux analysis

摘要：In this study, an integrated optimization framework that combined artificial neural network-genetic algorithm (ANN-GA) modeling, multi-sensor online monitoring, and metabolic profiling was developed. The ANN-GA model outperformed the traditional methods in terms of prediction accuracy, exhibiting superior fitting capability and enhanced performance of gentamicin C1a production. Real-time fermentation control was achieved via integrated near-infrared and Raman spectroscopy, yielding a 54.6 % titer increase (385.3 mg/L) over control group (249.2 mg/L). Metabolomic and metabolic flux analyses revealed a 36.2 % and 18.4 % reduction in glycolysis and tricarboxylic acid cycle fluxes, respectively, with an 11.3 % increase in pentose phosphate pathway flux and enhanced NADPH availability. Carbon flux was redirected toward biosynthetic intermediates, with key precursor pools such as glucose-1-phosphate and gentamicin X2 increasing by 52.8 % and 61.7 %, respectively. Overall, the approach significantly enhanced gentamicin C1a titer and laid the foundation for a scalable paradigm and theoretical framework for the intelligent optimization of industrial bioprocesses.