生物系统的数据驱动识别使用多尺度分析
Ismaila Muhammed1, Dimitris M Manias1, Dimitris A Goussis2
1Department of Mathematics, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.
PLoS computational biology
|November 6, 2025
概括
本研究引入了一种新的数据驱动框架,将非线性动力学 (SINDy) 的稀疏识别,计算单一扰动 (CSP) 和神经网络 (NN) 结合起来,用于生物系统识别. 该方法有效地从复杂的多尺度数据中识别了缩小模型,即使有噪声.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 非线性动力学是一种非线性动力学.
背景情况:
- 生物系统表现出复杂的多层次动态,对传统的系统识别方法构成挑战.
- 现有的技术往往需要明确的方程,限制其使用纯粹的观测数据.
- 准确的系统识别对于理解和建模生物过程至关重要.
研究的目的:
- 开发一个数据驱动的框架,用于准确地识别具有多尺度动态的生物系统.
- 通过整合非线性动态的稀疏识别 (SINDy),计算单一扰动 (CSP) 和神经网络 (NN) 来克服传统方法的局限性.
- 为了使系统识别从观测数据,而不需要明确的方程.
主要方法:
- 开发了一个整合SINDy,CSP和NNs的新框架.
- 神经网络估计了CSP向量场的梯度.
- CSP将数据分成具有类似动态的子集,以方便SINDy模型的识别.
主要成果:
- 该框架成功地确定了迈凯利斯-门模型的缩小模型,在完整的数据集上表现优于SINDy.
- 该方法证明了与随机数据的稳定性,准确地识别了来自杂数据集的模型.
- 该框架的算法性质确保了系统识别独立于数据集维度.
结论:
- 拟议的数据驱动框架有效地解决了识别多层次生物动态的挑战.
- 整合SINDy,CSP和NNs为数据驱动系统建模提供了一个强大的工具.
- 这种方法提高了系统识别对复杂生物数据的适用性.
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