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在动态和稳定状态的普通微分方程模型中计算局部灵敏度的基准测试方法
Polina Lakrisenko1,2, Dilan Pathirana3, Daniel Weindl1,3
1Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany.
PloS one
|October 23, 2024
概括
选择正确的方法来计算稳定状态灵敏度对于动态模型至关重要. 将稳定状态的数值集成与针对灵敏度的量身定制方法相结合,提供了最强大和最有效的方法.
科学领域:
- 计算建模计算建模
- 系统生物学 系统生物学
- 参数估计的参数估计.
背景情况:
- 估计动态模型参数是计算密集的.
- 稳态计算通常需要用于模型模拟.
- 选择稳态和梯度计算的高效方法是不清楚的.
研究的目的:
- 评估计算稳态和灵敏度的方法对.
- 识别可靠和计算效率高的方法.
- 为了指导模型设计者在方法选择中.
主要方法:
- 探索了稳定状态和灵敏度计算的六种方法对.
- 使用数值积分和牛顿稳定状态的方法.
- 采用了数值集成和针对敏感性分析的定制方法.
主要成果:
- 所有的方法对都产生了准确的稳定状态和梯度值.
- 结合数值集成 (平稳状态) 和量身定制方法 (敏感度) 的方法对是最强大和最有效的.
- 牛顿的稳定状态方法提供了加速度,但冒着模拟失败的风险.
结论:
- 没有一个单一的方法对是计算稳定状态灵敏度的普遍最佳方法.
- 为选择基于特定建模问题的合适方法提供了指导.
- 在动态模型分析中优化计算效率和稳定性是可以实现的.
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