  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.
