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Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments.

Youngdeok Hwang1, Hang J Kim2, Won Chang3

  • 1Paul H. Chook Department of Information Systems and Statistics, Baruch College, City University of New York.

Technometrics : a Journal of Statistics for the Physical, Chemical, and Engineering Sciences
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian calibration framework to harmonize biological and computer simulations of oscillating biochemical models. It uses advanced Markov chain Monte Carlo (MCMC) methods for accurate parameter inference and sensitivity analysis in biological systems.

Keywords:
Circadian cycleDifferential equationGeneralized multiset samplerHarmonic basis representationIntervention posteriorSystematic biology

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Understanding biological oscillations requires integrating experimental and computational approaches.
  • Significant statistical challenges exist in harmonizing these experimental types, including identifiability issues and high-dimensional instability.
  • Oscillating biochemical models are crucial for studying biological processes like circadian rhythms.

Purpose of the Study:

  • To develop a novel Bayesian calibration framework for oscillating biochemical models.
  • To address statistical challenges in parameter inference and sensitivity analysis for these models.
  • To effectively link simulated and observed oscillatory biological data.

Main Methods:

  • A Bayesian calibration framework is proposed for oscillating biochemical models.
  • Advanced Markov chain Monte Carlo (MCMC) techniques are employed for parameter inference.
  • An intervention posterior approach is used for sensitivity analysis.

Main Results:

  • The framework efficiently infers parameter values matching simulated and observed oscillatory processes.
  • Sensitivity analysis quantifies the influence of individual parameters on biological processes.
  • The methodology is successfully illustrated using circadian oscillations in *Neurospora crassa*.

Conclusions:

  • The proposed Bayesian framework offers a robust method for calibrating oscillating biochemical models.
  • The MCMC technique and sensitivity analysis provide valuable tools for systems biology research.
  • This approach enhances the integration of computational and experimental data in understanding biological oscillations.