Biology subjects

Gibbins, J.

Publications and source records attributed to Gibbins, J..

3 recordsLinked to original sources

bioRxiv2025

Extended Modelling of Platelet Calcium Signaling by Combined Recurrent Neural Network and Partial Least Squares Analyses

Platelets play critical roles in hemostasis and thrombosis. The platelet activation process is driven by agonist-induced rises in cytosolic [Ca2+]i, where the patterns of Ca2+ responses are still incompletely understood. In this study, we developed a number of techniques to model the [Ca2+]i curves of platelets from a single blood donor. Using a fluorescence ratio probe, the platelets were stimulated with a panel of agonists, i.e. thrombin, collagen, or CRP under various conditions, preventing extracellular Ca2+ entry, secondary mediator effects or Ca2+ reuptake into intracellular stores. To analyze the data, 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 platelet [Ca2+]i curves in the presence of combinations of agonists and inhibitors, with the NARX model achieving an R2 up to 0.64 for trend prediction of unforeseen data. In addition, we used the same dataset for 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 calcium 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 [Ca2+]i-inhibiting drugs.

biochemistry↗

bioRxiv2020

A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from different cell types (mosaic data), to inform model parameters. For instance, kinetic rate constants are often determined from purified enzyme data in vitro or use experimental concentrations obtained from multiple unrelated cell types. Thus they do not represent any specific cell type nor fully capture cell specific behaviours. In this work, we develop a model of the PI cycle informed by in-vivo omics data taken from a single cell type, namely platelets. Our model recapitulates the known experimental dynamics before and after stimulation with different agonists and demonstrates the importance of lipid- and protein-binding proteins in regulating second messenger outputs. Furthermore, we were able to make a number of predictions regarding the regulation of PI cycle enzymes and the importance of the number of receptors required for successful GPCR signaling. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain relatively consistent. However, when informed by mosaic experimental data model predictions greatly vary. Our work illustrates the risks of using mosaic datasets from unrelated cell types which leads to over 75% of outputs not fitting with expected behaviors. Authors summaryComputational models of cellular complexity offer much in terms of understanding cell behaviors and in informing experimental design, but their usefulness is limited in them being built with mosaic data not representing specific cell types and tested against limited experimental outputs. In this work we demonstrate an approach using quantitative proteomic datasets and temporal experimental data from a single cell type (platelets) to inform kinetic rate constants and protein concentrations for a mathematical model of a key signaling pathway - the phosphatidylinositol (PI) cycle; known for its central role in numerous cell functions and diseases. After using our model to make novel predictions regarding how aspects of the pathway are regulated, we demonstrate its versatile nature by utilising proteomic data from other cell types to generate similar predictions for those cells while highlighting the pitfalls of using mosaic data when constructing computational models.

cell biology↗

bioRxiv2019

Diffusional and chemical control in the tyrosine kinase network of platelet CLEC-2 signalling

C-type lectin-like receptor 2 (CLEC-2) is platelet membrane glycoprotein implicated in maintenance of blood vessel integrity and development of lymphatics. Organization and regulation of tyrosine kinase signalling network associated with CLEC-2 is poorly understood. Here we aimed to investigate CLEC-2 signal transduction using computational systems biology methods in combination with experimental approaches. We developed a 3D/stochastic multicompartmental computational model of CLEC-2 signalling, which was built around membrane-associated signalosome formation. The model predicted that both Syk and Src-family kinases phosphorylate CLEC-2, and that CLEC-2 ligation induces cytosolic calcium spiking. This was experimentally confirmed using flow cytometry and TIRF microscopy, respectively. Sensitivity analysis suggested that the CLEC-2 translocation to the signalosome region is one of the rate-limiting steps in the signal transduction process. In agreement with this prediction, CLEC-2-induced platelet activation was strongly temperature-dependent (unlike that mediated by G-protein coupled receptors) and was delayed by lipid raft disruption. Our results suggest a revised picture of the CLEC-2 signal transduction network functioning that emphasizes the crucial role of lipid raft structural rearrangement followed by tyrosine kinase feedback interplay.

systems biology↗