智能培养基优化用于重组蛋白质生产:实验,建模和AI/ML驱动的战略
Galib Khan1, Carrie Sanford2, Cong T Trinh3
1Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, TN, United States of America; Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States of America.
Biotechnology advances
|October 17, 2025
概括
优化培养基对于重组蛋白生产 (RPP) 至关重要. 本综述概述了智能媒介设计的框架,整合AI/ML以提高蛋白质产量和降低成本.
科学领域:
- 生物技术和生物工艺工程
- 代谢工程和合成生物学
背景情况:
- 重组蛋白质生产 (RPP) 在生物技术中至关重要,培养基显著影响蛋白质产量和质量.
- 中等优化是RPP的主要成本驱动因素,需要高效和可预测的战略.
研究的目的:
- 为RPP提供"智能"培养基优化阶段的全面审查.
- 探索机械和AI/ML模型的集成,以进行预测性生物过程优化.
主要方法:
- 审查中介设计的规划,选 (实验设计),建模,优化和验证阶段.
- 对机械和AI/ML驱动模型的调查,用于预测生物过程条件,营养素可用性,新陈代谢和蛋白质质量.
主要成果:
- 确定了介质成分的关键营养和能量作用及其对培养参数的影响.
- 突出了微金属含量变化的影响RPP.
- 展示了集成的AI/ML模型的潜力,用于预测媒介的制定和优化.
结论:
- 已经提出了一个统一的框架,用于推进RPP中的智能媒介设计.
- 人工智能/机器学习方法为克服中介设计中的瓶和加速RPP提供了变革性的潜力.
- 将实验室规模的优化转化为工业环境仍然是一个挑战,需要对AI/ML驱动的解决方案进行进一步的研究.
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