AbstractThe 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.
On the extraction of kinetic rate constants from experimental data
