生物过程动态中的混合建模:结构变化,实施策略和实际挑战
1Department of Biotechnology, Krunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India.
Biotechnology and bioengineering
|July 17, 2023
概括
混合建模结合了机械和数据驱动的方法来优化生物过程. 混合模型开发的挑战包括数据复杂性和缺乏共享资源,这阻碍了更广泛的采用.
科学领域:
- 生物工艺工程 生物工艺工程
- 计算建模 计算建模
- 系统生物学 系统生物学
背景情况:
- 混合建模,集成机械和数据驱动的方法,在生物处理中正在获得引力.
- 应用包括实验设计,参数识别和过程优化.
- 开发工作流是复杂的,涉及实验设计,数据处理,参数估计和验证.
研究的目的:
- 重新审视生物工艺系统中混合模型的发展.
- 讨论数据驱动组件的选择,参数识别和质量保证.
- 审查挑战,并为混合模型开发提出纠正措施.
主要方法:
- 对生物过程中混合模型开发的现有文献和方法的审查.
- 分析数据驱动的组件选择和状态映射策略.
- 检查参数识别技术和模型质量评估协议.
主要成果:
- 混合模型开发涉及各种模块的灵活整合,在确定最佳结构方面提出了挑战.
- 关键考虑因素包括基于数据的组件选择,参数估计和强大的模型验证.
- 库中缺乏数据和代码共享,阻碍了这些工具的探索和扩展.
结论:
- 混合建模为生物工艺进步提供了巨大的潜力,但面临着发展障碍.
- 解决数据集成,参数化和模型验证方面的挑战至关重要.
- 促进数据和代码共享对于加速生物处理中混合建模的采用和创新至关重要.
相关概念视频
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