一个寡核酸的逆相染色学净化混合建模:从可差异化的物理中学习一些镜头 循环中的解决者
Yu-Cheng Chen1,2, Ismaele Fioretti2, Dong-Qiang Lin1
1Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China.
Biotechnology and bioengineering
|May 9, 2025
概括
混合模型结合了生物制药过程的机械和数据驱动方法. 我们的新型DP-SOL框架显著改善了寡核酸净化建模,证明了其下游加工的潜力.
科学领域:
- 生物制药制造业 生物制药制造业
- 化学工程是化学工程的组成部分.
- 计算建模计算建模
背景情况:
- 生物制药过程建模面临的挑战是由于有限的理解和数据.
- 纯粹机械或数据驱动的模型具有固有的局限性.
- 混合模型通过整合两种方法提供解决方案.
研究的目的:
- 应用一种新的混合建模框架,即微分物理循环中溶解器 (DP-SOL),用于寡核酸净化.
- 在生物处理中克服传统机械和数据驱动模型的局限性.
- 为了证明DP-SOL在逆相色谱中的有效性.
主要方法:
- 开发了DP-SOL框架,通过可微分物理学将神经网络 (NN) 与机械模型连接起来.
- 收集了六个线性梯度化实验的数据,用于训练和测试.
- 利用了几次射击学习与超参数优化的网格搜索 (NN有2个隐藏层,14个节点).
主要成果:
- 混合型DP-SOL模型显著优于校准机械模型.
- 在培训和测试数据集上都取得了高绩效 (R2 > 0.97 在培训上).
- 由于集成的机械和NN组件,证明了DP-SOL的强大预测能力.
结论:
- DP-SOL是一种多功能混合建模范式,用于生物制药下游加工.
- 该框架有效地解决了有限数据和流程理解的挑战.
- DP-SOL显示出在生物制药领域推进建模的巨大潜力.
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