基于时刻的参数推断,对随机反应网络提供错误保证
Zekai Li1, Mauricio Barahona1, Philipp Thomas1
1Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom.
The Journal of chemical physics
|April 4, 2025
概括
这项研究引入了一种新的方法,用于用凸式优化在生物化学模型中推断参数,为无需复杂模拟的随机反应网络提供可靠的不确定性量化.
科学领域:
- 系统生物学 系统生物学
- 计算生物学 计算生物学
- 生物化学运动学 生物化学运动学
背景情况:
- 随机反应网络的参数推断受到当前错误量化方法中难以处理的概率函数和局限性的阻碍.
- 当前的方法在需要概率近似时缺乏理论保证,特别是对于非线性模型和单细胞数据.
研究的目的:
- 开发一种可靠的方法,从单细胞数据中推断生化动力学模型的参数,解决不确定性量化挑战.
- 为随机反应网络中的参数极限提供理论保证,适用于稳定状态和时间解析数据.
主要方法:
- 提出了一种凸优化方法,利用从观测数据中获得的动量方程和动量矩阵.
- 从观测中构建时刻间隔来约束凸集中的参数,确保边界包含真参数.
- 避免直接计算概率或模拟前问题,绕过计算难以解决的问题.
主要成果:
- 在随机反应网络中成功提供了对参数推理的不确定性量化和错误保证.
- 已证明适用于非线性和理性倾向网络,包括施洛格尔模型和切换开关模型.
- 用合成数据验证了该方法在数据整合和预测潜在物种统计数据方面的有效性.
结论:
- 拟议的方法为生物化学模型中的参数推理和不确定性量化提供了一个计算效率高,理论上健全的框架.
- 这种方法克服了传统方法的局限性,直接使用数据时刻,而不需要概率近似或模拟.
- 该技术是多功能,适用于各种生化模型和数据类型,推进复杂生物系统的分析.
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