机器学习辅助的媒体优化,用于在Pseudomonas fluorescens细胞工厂和扩展研究中增强胰岛素生产
Ansuman Sahoo1, Prabir Kumar Das1, Venkata Dasu Veeranki1
1Biochemical Engineering Laboratory, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, 781039, Assam, India.
概括
优化 Pseudomonas 光体表达系统显著增加了 GST-proinsulin 融合蛋白质的产生. 机器学习辅助的媒介优化实现了19倍高的标位,这对于满足不断增长的胰岛素需求至关重要.
科学领域:
- 生物技术是生物技术.
- 生物工艺工程 生物工艺工程
- 分子生物学分子生物学
背景情况:
- 糖尿病需要胰岛素治疗,全球需求增加.
- 重组胰岛素的产生至关重要,但表达系统需要优化.
- 光虫是重组蛋白质生产的潜在宿主,但其介质优化尚未得到充分探索.
研究的目的:
- 优化培养基,以增强 Pseudomonas fluorescens 中的 GST-proinsulin (GPI) 融合蛋白表达.
- 将机器学习辅助优化与生物过程开发的传统统计方法进行比较.
- 评估优化表达系统的扩展潜力.
主要方法:
- 使用Plackett-Burman设计 (PBD) 对介质组件的选.
- 使用盒子-Behnken设计 (BBD) 和人工神经网络-遗传算法 (ANN-GA) 的重要组件的优化.
- 验证和扩大规模的研究在摇瓶,批量,料批量和连续生物反应器.
主要成果:
- 确定了葡萄糖,NaCl,NH4Cl和MgSO4作为影响GPI表达的关键成分.
- 实现了亲胰岛素融合蛋白位的19倍增加,在食批次培养中达到1145 mg/L.
- 证明了ANN-GA在优化生物过程参数方面的有效性.
结论:
- Pseudomonas fluorescens 的表达系统适用于高滴度重组蛋白质的生产.
- 机器学习辅助的优化显著优于生物过程开发的传统方法.
- 优化的介质和生物处理策略对于满足日益增长的对胰岛素等治疗蛋白质的需求至关重要.
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