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Area of Science:

  • Systems Biology
  • Biophysics
  • Computational Biology

Background:

  • Stochastic chemical kinetic models are increasingly vital for understanding biological phenomena.
  • High-resolution microscopy provides single-cell protein and mRNA data.
  • Parameter estimation is essential for these models given experimental data.

Purpose of the Study:

  • To develop a new, efficient, and reliable method for parameter estimation in stochastic chemical kinetic models.
  • To address the challenge of estimating model parameters from single-cell experimental data.
  • To provide accurate confidence regions for parameter estimates.

Main Methods:

  • A novel likelihood expression for experimental data.
  • Sample path optimization combined with UOBYQA-Fit (a Powell's unconstrained optimization variant).
  • Efron's percentile bootstrapping for confidence region estimation.

Main Results:

  • The proposed method was applied to an RNA dynamics model in E. coli.
  • Parameter estimates and their confidence regions were obtained and tested.
  • The method demonstrated efficiency, reliability, and accuracy in testing.

Conclusions:

  • The developed parameter estimation method is effective for stochastic chemical kinetic models.
  • The approach provides accurate and reliable parameter estimates with confidence intervals.
  • This method advances the analysis of single-cell biological data governed by stochastic processes.