结合基于模型和数据的模型:合成生物学资源竞争的应用
Atefe Darabi1, Zheming An2, Muhammad Ali Al-Radhawi3
1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Mathematical biosciences
|March 6, 2026
概括
本研究介绍了嵌入式物理信息神经网络 (ePINNs),以集成机器学习 (ML) 与机械模型 (MM). 这种混合方法可以在合成生物学等复杂系统中提高预测和解释能力.
科学领域:
- 计算生物学 计算生物学
- 系统生物学 系统生物学
- 机器学习在科学中的应用
背景情况:
- 机械模型 (MM) 提供可解释性,但构建起来可能很复杂.
- 机器学习 (ML) 在数据驱动的建模方面表现出色,但可能缺乏可解释性.
- 整合ML和MM对于强大的预测和更深入的系统洞察是可取的.
研究的目的:
- 开发一个混合建模框架,将ML和MM结合起来.
- 确保ML组件遵守机械限制,避免过度装配并保持可解释性.
- 解决模拟复杂生物系统的挑战,例如合成遗传电路.
主要方法:
- 引入部分不确定模型结构 (PUMS) 来指导ML组件.
- 嵌入式物理信息神经网络 (ePINNs) 的开发,具有共享损失功能.
- 将ePINNs框架应用于具有资源竞争的基因网络模型.
主要成果:
- 在捕捉复杂的系统交互方面,ePINNs的有效性得到了证明.
- 展示了混合方法保持物理一致性的能力.
- 验证了框架在合成生物学应用中的实用性.
结论:
- ePINNs框架提供了一个强大的方法来整合ML和MM.
- 这种混合方法提高了模型的稳定性,可解释性和预测准确性.
- 对于具有固有的机械原理的系统建模,例如工程生物系统,ePINN特别有价值.
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