Using Approximate Bayesian Computation to Calibrate the Model Parameters Characterizing the Autoregulatory Behavior...

Area of Science:

• Physiology
• Biomedical Engineering
• Computational Biology

Background:

• Microvessel autoregulation involves myogenic and endothelial mechanisms influencing vessel caliber.
• A compliance feedback model previously described microvessel elastic and autoregulatory behavior.
• Experimental data on microvessel responses to pressure changes are available.

Purpose of the Study:

• To calibrate parameters and refine the functional form of the microvessel compliance feedback model.
• To integrate prior knowledge, model dynamics, and experimental data for parameter estimation.
• To gain mechanistic insights into microvessel autoregulatory responses.

Main Methods:

• Utilized experimental data on arteriolar vessel caliber changes in response to intraluminal pressure and pressure gradients.
• Employed a two-stage sequential Monte Carlo (MC) approximate Bayesian computation (ABC) scheme.
• Obtained posterior distributions of model parameters, integrating prior knowledge and experimental data.

Main Results:

• The ABC scheme revealed distinct time constants for myogenic-induced dilation and constriction.
• The calibrated compliance feedback model demonstrated excellent agreement with experimental measurements.
• The model achieved accurate characterization despite limited data availability.

Conclusions:

• The computationally efficient compliance feedback model provides a robust, physiologically grounded characterization of microvessel autoregulation.
• The refined model's simplicity and accuracy enhance its translatability for clinical hemodynamic applications.
• The study highlights the model's potential for future clinical use in understanding blood flow regulation.