A Framework for Parameter Estimation and Uncertainty Quantification in Systems Biology Using Quantile Regression and Physics-Informed Neural Networks.

IF 2 4区 数学 Q2 BIOLOGY
Haoran Hu, Qianru Cheng, Shuli Guo, Huifang Wen, Jing Zhang, Yongqi Song, Kaiqun Wang, Di Huang, Hui Zhang, Chaofeng Zhang, Yanhu Shan
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引用次数: 0

Abstract

A framework for parameter estimation and uncertainty quantification is crucial for understanding the mechanisms of biological interactions within complex systems and exploring their dynamic behaviors beyond what can be experimentally observed. Despite recent advances, challenges remain in achieving the high accuracy of parameter estimation and uncertainty quantification at moderate computational costs. To tackle these challenges, we developed a novel approach that integrates the quantile method with Physics-Informed Neural Networks (PINNs). This method utilizes a network architecture with multiple parallel outputs, each corresponding to a distinct quantile, facilitating a comprehensive characterization of parameter estimation and its associated uncertainty. The effectiveness of the proposed approach was validated across three study cases, where it was compared to the Monte Carlo dropout (MCD) and the Bayesian methods. Furthermore, a larger-scale model was employed to further demonstrate the excellent performance of the proposed approach. Our approach exhibited significantly superior efficacy in parameter estimation and uncertainty quantification. This highlights its great promise to broaden the scope of applications in system biology modeling.

基于分位数回归和物理信息神经网络的系统生物学参数估计和不确定性量化框架。
参数估计和不确定性量化的框架对于理解复杂系统中生物相互作用的机制和探索实验观察之外的动态行为至关重要。尽管最近取得了一些进展,但在以适度的计算成本实现高精度的参数估计和不确定性量化方面仍然存在挑战。为了应对这些挑战,我们开发了一种将分位数方法与物理信息神经网络(pinn)相结合的新方法。该方法利用具有多个并行输出的网络架构,每个并行输出对应于一个不同的分位数,便于对参数估计及其相关不确定性进行全面表征。通过三个研究案例验证了所提出方法的有效性,并将其与蒙特卡罗dropout (MCD)和贝叶斯方法进行了比较。最后,利用一个更大规模的模型进一步验证了该方法的优良性能。我们的方法在参数估计和不确定度量化方面表现出显著的优越性。这突出了它在系统生物学建模中扩大应用范围的巨大前景。
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来源期刊
CiteScore
3.90
自引率
8.60%
发文量
123
审稿时长
7.5 months
期刊介绍: The Bulletin of Mathematical Biology, the official journal of the Society for Mathematical Biology, disseminates original research findings and other information relevant to the interface of biology and the mathematical sciences. Contributions should have relevance to both fields. In order to accommodate the broad scope of new developments, the journal accepts a variety of contributions, including: Original research articles focused on new biological insights gained with the help of tools from the mathematical sciences or new mathematical tools and methods with demonstrated applicability to biological investigations Research in mathematical biology education Reviews Commentaries Perspectives, and contributions that discuss issues important to the profession All contributions are peer-reviewed.
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