生物制药培养过程的物理信息神经网络:考虑不同的过程参数设置
Niklas Adebar1, Sabine Arnold2, Liliana M Herrera3
1Boehringer Ingelheim Pharma GmbH & Co. KG, Development NCE, Ingelheim (Rhein), Germany.
Biotechnology and bioengineering
|September 18, 2024
概括
这项研究引入了一种新的物理信息神经网络 (PINN) 模型,可以准确预测生物技术培养结果. 该方法整合了动态增长方程和泰勒数列,用于精确的过程参数变化分析,减少了实验需求.
科学领域:
- 生物技术和生物化学工程 生物技术和生物化学工程
- 计算机建模和模拟.
背景情况:
- 生物技术培养过程复杂,对参数变化敏感.
- 准确预测过程结果对于优化和效率至关重要.
- 现有的模型可能缺乏处理参数影响的连续,可微分预测的能力.
研究的目的:
- 开发一种新的建模方法,用于研究和预测生物技术培养过程的结果.
- 将多变量过程参数变化集成到动态生长模型中.
- 为了使流程结果的连续和可差异化的预测.
主要方法:
- 使用物理信息的神经网络 (PINNs).
- 将PINN与动态生长方程结合起来.
- 采用泰勒序列扩展,将外部过程参数变化 (例如温度,播种密度,养速率) 整合到动力速率和生长方程中.
主要成果:
- 实现了对关键过程变量如细胞密度和度的非常高的预测准确度.
- 提供了对单个和组合参数影响的详细见解.
- 证明了模型能够评估新的参数变化和组合的能力.
结论:
- 提出的基于PINN的方法与泰勒系列扩展为生物技术培养提供了更高的预测准确性.
- 这种方法促进了对参数效应的更深入的理解,并减少了实验要求.
- 允许以模型为导向的优化研究和高效的设计空间探索.
相关概念视频
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