Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach

Area of Science:

• Biophysics
• Computational Biology
• Systems Biology

Background:

• Understanding biological molecules and systems relies on mechanistic models.
• Biophysical parameters are often estimated by fitting models to experimental data.
• Parameter non-identifiability occurs when multiple parameter sets explain data equally well.

Purpose of the Study:

• To demonstrate parameter non-identifiability in biophysical models.
• To investigate the causes of parameter non-identifiability.
• To present a reliable method for parameter estimation and confidence quantification.

Main Methods:

• Analysis of parameter identifiability in biophysical models.
• Investigation of causes for non-identifiability.
• Application of Bayesian inference for parameter reliability assessment.

Main Results:

• Parameter non-identifiability is a common issue, even in simple biophysical models.
• Straightforward methods exist for simple models, but more complex models require advanced tools.
• A Bayesian inference method reliably quantifies parameter confidence.

Conclusions:

• Parameter non-identifiability poses a significant challenge in biophysics.
• Careful model design and advanced inference methods are crucial for accurate parameter estimation.
• Bayesian inference offers a robust approach to establish the reliability of biophysical model parameters.