混合深度建模CHO-K1料批处理过程:将第一原则与深度神经网络结合起来
José Pinto1, João R C Ramos1, Rafael S Costa1
1LAQV-REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Caparica, Portugal.
深度混合模型显著改善了中国子卵巢 (CHO) 细胞过程的发展,提高了对糖蛋白生产的预测精度. 这种先进的方法可以更好地了解细胞代谢,并支持生物制药4.0倡议.
科学领域:
- 生物技术是生物技术.
- 工艺工程是过程工程.
- 计算生物学 计算生物学
背景情况:
- 混合建模,整合第一原则和机器学习,对于BioPharma 4.0.0至关重要.
- 中国仓鼠卵巢 (CHO) 细胞对于工业糖蛋白生产至关重要.
- 以前的混合模型经常使用浅层的前神经网络 (FFNNs).
研究的目的:
- 为了比较CHO细胞过程开发的深度与浅度混合建模.
- 为了评估FFNN深度对模型性能的影响.
- 评估糖蛋白生产过程的预测能力.
主要方法:
- 利用了来自24个料批培养的CHO-K1细胞系的数据.
- 混合型号与不同深度 (3-5层) 的FFNNs进行了比较.
- 雇佣的古典培训 (Levenberg-Marquardt) 和深度学习培训 (ADAM).
主要成果:
- 深度混合模型显示,系统的概括性比浅层模型更好.
- 培训和测试错误分别减少了14.0%和23.6%.
- 深度模型准确预测了30个状态变量和关键的代谢转变.
结论:
- 深度混合模型为CHO细胞生物过程开发提供了卓越的性能.
- 深度模型的计算时间增加被增强的精度所抵消.
- 预计深度混合模型将加速生物制药领域数字双胞胎的发展.
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