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.

中文翻译：

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PI周期模型揭示了脂质结合蛋白的调控作用和利用镶嵌生物学数据的陷阱

磷脂酰肌醇（PI）周期是真核细胞信号转导的关键。由于反应的数量以及涉及的脂质和肌醇磷酸酯中间体的复杂性，使其难以进行实验分析。当不能仅通过实验方法来实现复杂的生物调节机制时，计算建模方法被视为一种阐明复杂生物调节机制的方法。尽管在通知生物系统方面已经建立了数学模型，但是许多模型通常会使用来自不同细胞类型的数据（马赛克数据）来告知模型参数。例如，动力学速率常数通常是从体外纯化的酶数据中确定的，或者是使用从多种不相关细胞类型中获得的实验浓度。因此，它们不代表任何特定的细胞类型，也不完全捕获细胞的特定行为。在这项工作中，我们开发了PI周期的模型，该模型以从单个细胞类型（即血小板）中获取的体内组学数据为基础。我们的模型概括了不同激动剂刺激前后的已知实验动力学，并证明了脂质和蛋白质结合蛋白在调节第二信使输出中的重要性。此外，我们能够对PI循环酶的调节以及成功进行GPCR信号转导所需的受体数目的重要性做出许多预测。然后，当我们充分了解HeLa细胞的数据时，我们会考虑途径行为的差异，并表明模型预测保持相对一致。然而，当由镶嵌实验数据告知时，模型的预测会大不相同。我们的工作说明了使用不相关细胞类型的镶嵌数据集的风险，这导致超过75％的输出与预期行为不符。