Mechanics and High Performance Computing Group, Technical University of Munich, Parkring 35, 85748, Garching bei München, Germany.
School of Mathematics and Statistics F07, University of Sydney, Sydney, NSW, 2006, Australia.
Bull Math Biol. 2018 Jan;80(1):175-214. doi: 10.1007/s11538-017-0367-1. Epub 2017 Nov 27.
There are a growing number of studies that model immunological processes in the artery wall that lead to the development of atherosclerotic plaques. However, few of these models use parameters that are obtained from experimental data even though data-driven models are vital if mathematical models are to become clinically relevant. We present the development and analysis of a quantitative mathematical model for the coupled inflammatory, lipid and macrophage dynamics in early atherosclerotic plaques. Our modeling approach is similar to the biologists' experimental approach where the bigger picture of atherosclerosis is put together from many smaller observations and findings from in vitro experiments. We first develop a series of three simpler submodels which are least-squares fitted to various in vitro experimental results from the literature. Subsequently, we use these three submodels to construct a quantitative model of the development of early atherosclerotic plaques. We perform a local sensitivity analysis of the model with respect to its parameters that identifies critical parameters and processes. Further, we present a systematic analysis of the long-term outcome of the model which produces a characterization of the stability of model plaques based on the rates of recruitment of low-density lipoproteins, high-density lipoproteins and macrophages. The analysis of the model suggests that further experimental work quantifying the different fates of macrophages as a function of cholesterol load and the balance between free cholesterol and cholesterol ester inside macrophages may give valuable insight into long-term atherosclerotic plaque outcomes. This model is an important step toward models applicable in a clinical setting.
越来越多的研究对导致动脉粥样硬化斑块形成的动脉壁免疫过程进行建模。然而,尽管数据驱动模型对于数学模型具有至关重要的意义,但这些模型中很少使用从实验数据中获得的参数。我们提出了一种用于早期动脉粥样硬化斑块中炎症、脂质和巨噬细胞动力学的定量数学模型的开发和分析。我们的建模方法类似于生物学家的实验方法,即从许多体外实验的较小观察和发现中综合出动脉粥样硬化的全貌。我们首先开发了一系列三个更简单的子模型,将其最小二乘拟合到文献中的各种体外实验结果中。随后,我们使用这三个子模型构建了早期动脉粥样硬化斑块发展的定量模型。我们对模型的参数进行了局部敏感性分析,确定了关键参数和过程。此外,我们对模型的长期结果进行了系统分析,根据低密度脂蛋白、高密度脂蛋白和巨噬细胞的募集率,对模型斑块的稳定性进行了特征描述。模型分析表明,进一步的实验工作量化巨噬细胞的不同命运(作为胆固醇负荷和巨噬细胞内游离胆固醇与胆固醇酯之间平衡的函数)可能会深入了解长期动脉粥样硬化斑块的结果。该模型是朝着适用于临床环境的模型迈出的重要一步。