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Inference-based assessment of parameter identifiability in nonlinear biological models.

Aidan C Daly1, David Gavaghan2, Jonathan Cooper3

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Inferring parameter values in biological models is challenging. This study reveals parameter compensation as a cause of unidentifiability and offers methods for experimental design.

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Area of Science:

  • Systems biology
  • Computational biology
  • Mathematical modeling

Background:

  • Inferring parameter values for biological models from experimental data is crucial for model development.
  • Nonlinear models often present challenges in parameter fitting, leading to unidentifiable parameters that cannot be constrained within plausible physiological ranges.

Purpose of the Study:

  • To investigate the causes of parameter unidentifiability in biological models.
  • To compare different inference-based methods for analyzing parameter uncertainty.
  • To provide insights for designing informative experiments.

Main Methods:

  • Utilized inference-based methods to analyze parameter unidentifiability.
  • Compared a measure-theoretic approach to inverse sensitivity analysis with Markov chain Monte Carlo (MCMC) and approximate Bayesian computation (ABC) for Bayesian inference.
  • Mapped output space uncertainty to parameter space probability distributions.

Main Results:

  • Identified parameter compensation as a key cause of unidentifiability.
  • Demonstrated how the geometry of parameter probability sets reveals sources of unidentifiability.
  • Showcased the utility of these methods in guiding experimental design.

Conclusions:

  • Parameter compensation significantly contributes to unidentifiability in biological models.
  • Inference-based methods, including measure-theoretic approaches, effectively diagnose and visualize parameter unidentifiability.
  • The insights gained can inform more efficient and effective experimental design for parameter estimation.