Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany.
Department of Cardiology, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany.
PLoS One. 2022 Aug 3;17(8):e0272079. doi: 10.1371/journal.pone.0272079. eCollection 2022.
Atherosclerosis is one of the leading causes of death worldwide. Biomathematical modelling of the underlying disease and therapy processes might be a useful aid to develop and improve preventive and treatment concepts of atherosclerosis. We here propose a biomathematical model of murine atherosclerosis under different diet and treatment conditions including lipid modulating compound and antibiotics. The model is derived by translating known biological mechanisms into ordinary differential equations and by assuming appropriate response kinetics to the applied interventions. We explicitly describe the dynamics of relevant immune cells and lipid species in atherosclerotic lesions including the degree of blood vessel occlusion due to growing plaques. Unknown model parameters were determined by fitting the predictions of model simulations to time series data derived from mice experiments. Parameter fittings resulted in a good agreement of model and data for all 13 experimental scenarios considered. The model can be used to predict the outcome of alternative treatment schedules of combined antibiotic, immune modulating, and lipid lowering agents under high fat or normal diet. We conclude that we established a comprehensive biomathematical model of atherosclerosis in mice. We aim to validate the model on the basis of further experimental data.
动脉粥样硬化是全球范围内主要的死亡原因之一。对潜在疾病和治疗过程进行生物数学建模可能有助于开发和改进动脉粥样硬化的预防和治疗概念。在这里，我们提出了一种在不同饮食和治疗条件下的小鼠动脉粥样硬化的生物数学模型，包括脂质调节化合物和抗生素。该模型通过将已知的生物学机制转化为常微分方程，并假设对应用干预的适当反应动力学来推导。我们明确描述了动脉粥样硬化病变中相关免疫细胞和脂质种类的动态，包括由于斑块生长导致的血管阻塞程度。通过将模型模拟的预测拟合到来自小鼠实验的数据的时间序列中，确定了未知的模型参数。对于考虑的所有 13 个实验场景，参数拟合都使模型和数据具有很好的一致性。该模型可用于预测高脂肪或正常饮食下联合使用抗生素、免疫调节和降血脂药物的替代治疗方案的结果。我们得出结论，我们在小鼠中建立了一个全面的动脉粥样硬化生物数学模型。我们旨在根据进一步的实验数据验证该模型。
