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A Framework for Parameter Estimation and Uncertainty Quantification in Systems Biology Using Quantile Regression and

Haoran Hu1, Qianru Cheng1, Shuli Guo1

  • 1Department of Biomedical Engineering, Research Center for Nano-Biomaterials and Regenerative Medicine, College of Artificial Intelligence, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, People's Republic of China.

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This study introduces a novel method combining the quantile method with Physics-Informed Neural Networks (PINNs) for accurate biological system modeling. The approach enhances parameter estimation and uncertainty quantification efficiently, outperforming existing techniques.

Keywords:
NoiseOrdinary differential equation models (ODEs)PINNsParameter estimationSystems biology modelsUncertainty quantification

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Machine Learning

Background:

  • Accurate parameter estimation and uncertainty quantification are vital for understanding complex biological systems.
  • Current methods face challenges in achieving high accuracy at reasonable computational costs.

Purpose of the Study:

  • To develop a novel, efficient framework for parameter estimation and uncertainty quantification in systems biology.
  • To integrate the quantile method with Physics-Informed Neural Networks (PINNs) for improved biological modeling.

Main Methods:

  • Developed a novel approach integrating the quantile method with Physics-Informed Neural Networks (PINNs).
  • Utilized a multi-output neural network architecture to characterize parameter estimation and uncertainty.
  • Validated the approach across three case studies and a larger-scale model, comparing it with Monte Carlo dropout (MCD) and Bayesian methods.

Main Results:

  • The proposed approach demonstrated significantly superior efficacy in parameter estimation and uncertainty quantification compared to MCD and Bayesian methods.
  • Achieved high accuracy in characterizing parameter estimation and associated uncertainty.
  • Showcased excellent performance on a larger-scale biological model.

Conclusions:

  • The novel quantile method and PINNs integration offers a powerful tool for systems biology modeling.
  • This approach promises to expand the applications of computational modeling in biological research.
  • Provides a more accurate and computationally efficient solution for parameter estimation and uncertainty quantification.