{"title":"A Framework for Parameter Estimation and Uncertainty Quantification in Systems Biology Using Quantile Regression and Physics-Informed Neural Networks.","authors":"Haoran Hu, Qianru Cheng, Shuli Guo, Huifang Wen, Jing Zhang, Yongqi Song, Kaiqun Wang, Di Huang, Hui Zhang, Chaofeng Zhang, Yanhu Shan","doi":"10.1007/s11538-025-01439-9","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":9372,"journal":{"name":"Bulletin of Mathematical Biology","volume":"87 5","pages":"60"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bulletin of Mathematical Biology","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.1007/s11538-025-01439-9","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
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.
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.