A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data
bioRxiv, ISSN: 2692-8205
2020
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
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.
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