Extended Modelling of Molecular Calcium Signalling in Platelets by Combined Recurrent Neural Network and Partial Least Squares Analyses.

Platelets play critical roles in haemostasis and thrombosis. The platelet activation process is driven by agonist-induced rises in cytosolic [Ca], where the patterns of Ca responses are still incompletely understood. In this study, we developed a number of techniques to model the [Ca] curves of platelets from a single blood donor. Fura-2-loaded platelets were quasi-simultaneously stimulated with various agonists, i.e., thrombin, collagen, or CRP, in the presence or absence of extracellular Ca entry, secondary mediator effects, or Ca reuptake into intracellular stores. To understand the calibrated time curves of [Ca] rises, we developed two non-linear models, a multilayer perceptron (MLP) network and an autoregressive network with exogenous inputs (NARX). The trained networks accurately predicted the [Ca] curves for combinations of agonists and inhibitors, with the NARX model achieving an R of 0.64 for the trend prediction of unforeseen data. In addition, we used the same dataset for the construction of a partial least square (PLS) linear regression model, which estimated the explained variance of each input. The NARX model demonstrated that good fits could be obtained for the nanomolar [Ca] curves modelled, whereas the PLS model gave useful interpretable information on the importance of each variable. These modelling results can be used for the development of novel platelet [Ca]-inhibiting drugs, such as the drug 2-aminomethyl diphenylborinate, blocking Ca entry in platelets, or for the evaluation of general platelet signalling defects in patients with a bleeding disorder.