Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation With High Performance Computing.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Cardiovascular Research

Background:

• Cardio/cerebrovascular diseases (CVD) pose significant health challenges.
• Current CVD detection methods are limited, failing to account for platelet activation dynamics and individual variability.
• Platelet deposition modeling offers a potential avenue for improved diagnostics.

Purpose of the Study:

• To develop a Bayesian inferential scheme for estimating key parameters in a numerical platelet deposition model.
• To validate the model's ability to capture inter-individual variability in platelet interactions.
• To establish a data-driven methodology for personalized CVD detection.

Main Methods:

• Utilized a Bayesian inference framework combined with Approximate Bayesian Computation (ABC) for parameter estimation.
• Employed a High-Performance Computing (HPC) framework to manage computationally intensive simulations required by ABC.
• Calibrated the model using experimental data on platelet aggregation cluster size, number, and activation status from seven volunteers.

Main Results:

• The Bayesian inference scheme successfully estimated five critical parameters of the platelet deposition model.
• The model's posterior predictions closely matched experimental data, demonstrating its accuracy.
• A tight posterior prediction error margin validated the model's predictive power and the approach's efficacy.

Conclusions:

• The developed methodology provides a robust framework for inferring platelet deposition parameters from patient data.
• This approach can lead to the development of novel, personalized platelet function tests for early CVD detection.
• Integration of numerical modeling, Bayesian uncertainty quantification, and HPC offers a powerful toolkit for advancing cardiovascular diagnostics.