化学反应网络的动力模型的参数估计从物种度的部分实验数据的化学反应网络
Manvel Gasparyan1,2,3, Shodhan Rao2,3
1School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.
Bioengineering (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了一种新的方法,用于估计化学反应模型中的参数,使用克朗减小和最小平方优化. 这种方法有效地解决了部分实验数据的错误问题.
科学领域:
- 化学动力学 化学动力学
- 系统生物学 系统生物学
- 数学建模的数学建模
背景情况:
- 动力模型的参数估计是具有挑战性的,因为问题不合理,实验数据有限.
- 准确的参数估计对于理解和预测化学反应网络的行为至关重要.
研究的目的:
- 开发和验证一种使用克朗减小和最小平方优化在动力模型中进行参数估计的新方法.
- 引入一个轨迹独立的测量方法来量化模型的减少精度.
- 解决动态系统中参数的识别问题.
主要方法:
- 应用克朗减值来简化运动模型.
- 使用加权和未加权最小平方优化进行参数估计.
- 采用轨迹独立的测量来指导参数估计.
- 使用交叉验证 (leave-one-out) 来比较估计技术.
主要成果:
- 克朗减小方法与最小平方相结合,有效地从部分时间序列数据中估计参数.
- 一个新的测量方法量化了原始模型和缩小模型之间的动态差异.
- 交叉验证 (leave-one-out) 确定了针对特定网络的首选技术.
- 在尼古丁酸乙胆受体和Trypanosoma brucei trypanothione合成酶上有明显的应用.
结论:
- 拟议的方法为复杂的化学反应网络中的参数估计提供了可靠的解决方案.
- 独立于轨迹的测量有助于选择适当的模型减少.
- 该研究强调了在动态系统分析中可识别性的重要性.
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