以生理学为基础的规律化使生物应用的普遍微分方程系统的训练成为可能
Max de Rooij1,2, Balázs Erdős3, Natal A W van Riel1,2,4
1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
PLoS computational biology
|January 23, 2025
概括
本研究引入了基于生理学的规范化,以改善系统生物学中的普遍微分方程 (UDE) 模型. 这种方法提高了模型的准确性,并减少了对更好地理解生物系统的数据需求.
科学领域:
- 系统生物学 系统生物学
- 计算生物学是一种计算生物学.
- 数学建模的数学建模
背景情况:
- 了解复杂的生物系统需要复杂的数学模型.
- 机械模型耗时,而机器学习模型需要大量的数据.
- 像普遍微分方程 (UDEs) 这样的混合模型整合了机械和数据驱动的方法,但面临着稀疏数据的参数估计挑战.
研究的目的:
- 开发一种新的方法,以生理学为基础的规范化,以提高UDE模型中的参数估计.
- 提高UDE模型在生物应用中的准确性和数据效率.
- 为了解决生物建模中稀疏数据和有限的采样持续时间的局限性.
主要方法:
- 建议以生理学为基础的规范化来惩罚生物学上不可思议的模型行为.
- 应用规范化指导UDE到生理学上可信的解决方案.
- 使用模拟研究和葡萄糖最小模型与人类食反应数据验证了该方法.
主要成果:
- 以生理学为基础的规范化证明了更准确的模型行为预测.
- 该方法显著减少了成功模型培训所需的数据量.
- 将其应用于葡萄糖最小模型,可以降低与不同的初始参数进行训练的UDE嵌入神经网络中的变异性.
结论:
- 基于生理学的规范化是改善系统生物学中UDE模型的有效技术.
- 这种方法提高了模型的准确性和稳定性,特别是在有限的生物数据下.
- 该方法有望通过更可靠的计算模型推进疾病机制发现和识别药物点.
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