%A Dutta,Ritabrata
%A Chopard,Bastien
%A Lätt,Jonas
%A Dubois,Frank
%A Zouaoui Boudjeltia,Karim
%A Mira,Antonietta
%D 2018
%J Frontiers in Physiology
%C
%F
%G English
%K Platelet deposition,numerical model,Bayesian inference,Approximate Bayesian Computation,High performance computing
%Q
%R 10.3389/fphys.2018.01128
%W
%L
%N 1128
%M
%P
%7
%8 2018-August-20
%9 Original Research
%#
%! Parameter estimation of platelets deposition
%*
%<
%T Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation With High Performance Computing
%U https://www.frontiersin.org/article/10.3389/fphys.2018.01128
%V 9
%0 JOURNAL ARTICLE
%@ 1664-042X
%X Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. Recent studies show the existing clinical tests to detect 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, using experimental observations, at different time intervals, on the average size of the aggregation clusters, their number per mm^{2}, the number of platelets, and the ones activated per μℓ still in suspension. 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 consider a collective dataset of seven volunteers and use this inference scheme to get an approximate posterior distribution and the Bayes estimate of these five parameters. The mean posterior prediction of platelet deposition pattern matches the experimental dataset closely with a tight posterior prediction error margin, justifying our main intuition and providing a methodology to infer these parameters given patient data. 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.