强大的参数估计和可识别性分析与混合神经常规微分方程在计算生物学中
Stefano Giampiccolo1,2, Federico Reali1, Anna Fochesato1,3,4
1Fondazione The Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), Rovereto, Trento, Italy.
NPJ systems biology and applications
|November 28, 2024
概括
本研究引入了一种新的计算生物学方法,用于在结构知识不完全的系统中进行参数估计和可识别性分析. 该方法使用混合神经普通微分方程,并将生物参数视为强大的全球搜索的超参数.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 生物技术是生物技术.
背景情况:
- 参数估计是计算生物学中的一个关键挑战.
- 准确的参数估计对于理解复杂的生物系统至关重要.
- 现有的方法经常与结构性知识不完整的系统作斗争.
研究的目的:
- 在计算机生物学中,当系统结构部分未知时,开发一种可靠的方法来估计模型参数.
- 评估这些部分已知的系统中的参数的识别能力.
- 在将神经网络集成到机械模型中时,应对全球探索和参数识别方面的挑战.
主要方法:
- 将部分已知的模型嵌入到混合神经普通微分方程 (h-NODE) 中.
- 使用神经网络来表示未知的系统组件.
- 在超参数调整过程中,将生物参数视为全球搜索的超参数.
- 进行后期可识别性分析,扩展机械模型的既定方法.
主要成果:
- 拟议的管道有效地估计了参数,并评估了部分结构信息的系统中的可识别性.
- 该方法成功地应对了与全球参数空间探索和潜在的识别能力丧失相关的挑战.
- 在三个测试案例的验证表明在现实条件下的性能,包括噪音数据和有限的系统可观测性.
结论:
- 开发的方法为在具有不完整信息的复杂生物系统中进行参数估计和可识别性分析提供了强大的工具.
- 将神经网络集成到机械模型中,加上超参数优化,为计算生物学挑战提供了可行的解决方案.
- 这种方法提高了生物研究中的计算模型的可靠性和可解释性.
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