Parameter estimation of platelets deposition: Approximate Bayesian computation with high performance computing

TL;DR

This paper introduces a high-performance computing-enhanced approximate Bayesian computation method to estimate parameters in a complex numerical model of platelet deposition, aiming to improve personalized CVD diagnostics.

Contribution

It develops a likelihood-free Bayesian inference scheme using HPC for a complex stochastic model of platelet deposition, enabling personalized disease testing.

Findings

Posterior predictions closely match experimental data.

The approach captures inter-individual variability.

Efficient inference enabled by HPC framework.

Abstract

Recent studies show the existing clinical tests to detect Cardio/cerebrovascular diseases (CVD) are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions. Further they are also incapable to consider inter-individual variability. A physical description of platelets deposition was introduced recently in Chopard et. al. [2017], by integrating fundamental understandings of how platelets interact in a numerical model, parameterized by five parameters. These parameters specify the deposition process and are relevant for a biomedical understanding of the phenomena. One of the main intuition is that these parameters are precisely the information needed for a pathological test identifying CVD captured and that they capture the inter-individual variability. Following this intuition, here we devise a Bayesian inferential scheme for estimation of these parameters. As the likelihood function of the numerical model is intractable due to the complex stochastic nature of the model, we use a likelihood-free inference scheme approximate Bayesian computation (ABC) to calibrate the parameters in a data-driven manner. As ABC requires the generation of many pseudo-data by expensive simulation runs, we use a high performance computing (HPC) framework for ABC to make the inference possible for this model. We illustrate that our mean posterior prediction of platelet deposition pattern matches the experimental dataset closely with a tight posterior prediction error margin for a collective dataset of 7 volunteers. The present approach can be used to build a new generation of personalized platelet functionality tests for CVD detection, using numerical modeling of platelet deposition, Bayesian uncertainty quantification and High performance computing.