Parameter estimation of platelets deposition: Approximate Bayesian computation with high performance computing
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A numerical model that quantitatively describes how platelets in a shear flow adhere and aggregate on a deposition surface has been recently developed in Chopard et al. (2015); Chopard et al. (2017). Five parameters specify the deposition process and are relevant for a biomedical understanding of the phenomena. Experiments give observations, at five 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. By comparing in-vitro experiments with simulations, the model parameters can be manually tuned (Chopard et al. (2015); Chopard et al. (2017)). Here, we use instead approximate Bayesian computation (ABC) to calibrate the parameters in a data-driven automatic manner. ABC requires a prior distribution for the parameters, which we take to be uniform over a known range of plausible parameter values. ABC requires the generation of many pseudo-data by expensive simulation runs, we have thus developed an high performance computing (HPC) ABC framework, taking into account accuracy and scalability. The present approach can be used to build a new generation of platelets functionality tests for patients, by combining in-vitro observation, mathematical modeling, Bayesian inference and high performance computing.
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