集成组合NSGA-II用于多目标过程优化:重新折叠胰岛素作为一个案例研究
Rashmi Sharma1, Naveen G Jesubalan1, Anurag S Rathore1,2
1School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India.
Biotechnology and bioengineering
|December 3, 2025
概括
本研究引入了基于知识的生物治疗再折叠优化方法,显著提高蛋白质产量,减少与传统方法相比的实验力度.
科学领域:
- 生物技术是生物技术.
- 蛋白质工程是指蛋白质工程.
- 生物过程优化 生物过程优化
背景情况:
- 生物治疗性纳入体的体外再折叠是蛋白质生产中的一个关键瓶.
- 目前的重新折叠优化方法,如实验设计 (DoE),往往是低效的,昂贵的,劳动密集型.
- 线下分析和大量采样要求阻碍了快速发展.
研究的目的:
- 开发和验证一种基于知识的新型再折叠优化框架,用于益胰岛素.
- 将这种新方法的效率和有效性与传统的基于DoE的方法进行对比.
- 提高生物治疗生产产量,减少实验工作量.
主要方法:
- 使用富里埃变换红外线 (FTIR),氧化减少潜力 (ORP) 和逆相高性能液态染色学 (RP-HPLC) 实时监测重新折叠.
- 根据监测数据,将反应划分为两个不同的阶段 (0-2h和2-6h).
- 应用一个多目标优化 (MOO) 方法,结合XGBoost和一个非主导排序遗传算法II (NSGA-II) 优化器.
主要成果:
- 基于知识的优化实现了65%±1.78%的收益率,相对于传统的DoE方法 (54%±2.62%),相对增加了20%.
- 在分段反应阶段内确定参数之间的线性相关性.
- 证明了综合选和优化能力,减少了大约50%的实验力度.
结论:
- 拟议的基于知识的优化框架为生物治疗重新折叠提供了更高效和有效的方法.
- 这种方法显著提高了蛋白质产量,并减少了优化所需的时间和资源.
- 综合方法对于优化复杂的生物过程具有广泛的适用性.
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