使用半定量数据对细胞过程的ODE模型进行高效参数估计
Domagoj Dorešić1,2, Stephan Grein1, Jan Hasenauer1,2,3
1Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany.
Bioinformatics (Oxford, England)
|June 28, 2024
概括
本研究引入了一种基于spline的方法,将半定量生物数据集成到动态模型参数估计中. 该方法可靠地发现未知的测量转换,并提高参数推断的准确性.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物物理学的生物物理.
背景情况:
- 定量动态模型对于理解生物过程至关重要.
- 从实验数据中估计参数是关键,但数据往往是半定量的.
- 半定量数据涉及系统状态的非线性转换,使模型比较复杂化.
研究的目的:
- 开发一种多功能方法,将半定量数据集成到动态模型的参数估计中.
- 为了使模型模拟和半定量实验数据之间的比较,即使是未知的转换.
- 提高生物模型参数推理的准确性和效率.
主要方法:
- 一种基于spline的方法,用于整合各种各样的半定量数据.
- 对层次目标函数梯度的分析公式的推导.
- 在开源的Python参数估计工具箱 (pyPESTO) 中实现.
主要成果:
- 该方法大大提高了参数估计效率.
- 它可靠地发现未知的非线性测量转换.
- 与现有的半定量数据方法相比,它显著改善了参数推断.
结论:
- 拟议的基于spline的方法为在动态建模中利用半定量数据提供了强大的解决方案.
- 这种方法增强了对生物系统的理解和预测.
- 开源实现有助于建模人员广泛采用.
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