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 \mbox{mm^2, the number of platelets and the ones activated per \mu\ell still in suspension. By comparing in-vitro experiments with simulations, the model parameterscan 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.
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. ^2, the number of platelets and the ones activated per \mu\ell still in suspension. By comparing in-vitro experiments with simulations, the model parameterscan be manually tuned (Chopard et al. (2015); Chopard et al. (2017)). Here} 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.
R
A numerical model that quantitatively describes how platelets in a shear flow adhere and aggregate on a deposition surface has been recently developed
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
R
al. (2015); Chopard et al. (
. al.
R
). Five
, by integrating fundamental understandings of how platelets interact in a numerical model, parameterized by five parameters. These
D
Experiments give observations, at five time intervals, on the average size of the aggregation clusters, their number per \mbox{mm
A
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
R
instead
a likelihood-free inference scheme
R
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
manner. As ABC requires the
R
have thus developed an
use a
R
ABC framework , taking into account accuracy and scalability.
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.
R
platelets
personalized platelet
R
patients, by combining in-vitro observation, mathematical modeling , Bayesian inference and high
CVD detection, using numerical modeling of platelet deposition, Bayesian uncertainty quantification and High