从生物数据中提取可识别和可解释的动态模型
Gemma Massonis1, Alejandro F Villaverde2,3, Julio R Banga1
1Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Galicia, Spain.
PLoS computational biology
|October 18, 2023
概括
这项研究引入了一种新方法,用于自动发现生物系统可解释和可靠的机械动态模型. 它增强了SINDy-PI算法,以确保模型在结构上是可识别和可观察的,克服了当前方法的局限性.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 数学建模的数学建模
背景情况:
- 机械动态模型对于对复杂的生物系统进行定量理解至关重要.
- 从数据中自动开发可解释模型是计算生物学中的一个关键挑战.
- 稀疏回归,特别是非线性动力学稀疏识别 (SINDy) 算法,是模型发现的一个成功框架.
研究的目的:
- 提出一种用于自动发现结构上可识别和可观测的机械生物学模型的方法.
- 扩展SINDy-PI算法,以确保模型的可解释性和可靠性.
- 解决当前模型发现技术的局限性,可能导致无法识别的模型.
主要方法:
- 使用SINDy-PI算法在普通微分方程中发现理性非线性项.
- 开发一种方法,以确保发现模型的结构识别和可观察性.
- 将组合方法应用于六个生物学案例研究.
主要成果:
- 提出的方法成功地发现了结构上可识别和可观察的机械模型.
- 发现SINDy-PI有时可以产生无法识别的模型,新方法可以转换这些模型.
- 该方法确保模型在保持稀疏性的情况下,可以从机械上解释.
结论:
- 开发的方法学增强了对生物系统的自动化模型发现.
- 它通过解决结构识别和可观察性来确保机械动态模型的可靠性和可解释性.
- 这项工作为计算生物学和系统生物学研究提供了重大进展.
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