Patel Hetvi, Solanki Nilay, Solanki Arpita, Patel Mehul, Patel Swayamprakash, Shah Umang
Ramanbhai Patel College of Pharmacy, Charotar University of Science and Technology (CHARUSAT), CHARUSAT Campus Changa 388421, Gujarat, India.
Parul Institute of Engineering and Technology, Department of Applied Sciences and Humanities (Mathematics), Parul University Vadodara 391760, Gujarat, India.
Am J Transl Res. 2024 Jul 15;16(7):2777-2792. doi: 10.62347/UJQF5204. eCollection 2024.
The kinetics of brain cell death in Alzheimer's disease (AD) is being studied using mathematical models. These mathematical models utilize techniques like differential equations, stochastic processes, and network theory to explore crucial signalling pathways and interactions between different cell types. One crucial area of research is the intentional cell death known as apoptosis, which is crucial for the nervous system. The main purpose behind the mathematical modelling of this is for identification of which biomarkers and pathways are most influential in the progression of AD. In addition, we can also predict the natural history of the disease, by which we can make early diagnosis. Current mathematical models include the Apolipoprotein E (APOE) Gene Model, the Tau Protein Kinetics Model, and the Amyloid Beta Peptide Kinetic Model. The Bcl-2 and Bax apoptosis theories postulate that the balance of pro- and anti-apoptotic proteins in cells determines whether a cell experiences apoptosis, where the Bcl-2 model, depicts the interaction of pro- and anti-apoptotic proteins, it is also being used in research on cell death in a range of cell types, including neurons and glial cells. How peptides are produced and eliminated in the brain is explained by the Amyloid beta Peptide (Aβ) Kinetics Model. The tau protein kinetics model focuses on production, aggregation, and clearance of tau protein processes, which are hypothesized to be involved in AD. The APOE gene model investigates the connection between the risk of Alzheimer's disease and the APOE gene. These models have been used to predict how Alzheimer's disease would develop and to evaluate how different inhibitors will affect the illness's course. These mathematical models reflect physiological meaningful characteristics and demonstrates robust fits to training data. Incorporating biomarkers like Aβ, Tau, APOE and markers of neuronal loss and cognitive impairment can generate sound predictions of biomarker trajectories over time in Alzheimer's disease.
目前正在使用数学模型研究阿尔茨海默病（AD）中脑细胞死亡的动力学。这些数学模型利用微分方程、随机过程和网络理论等技术，探索关键的信号通路以及不同细胞类型之间的相互作用。一个关键的研究领域是被称为凋亡的程序性细胞死亡，这对神经系统至关重要。对此进行数学建模的主要目的是确定哪些生物标志物和信号通路在AD进展中最具影响力。此外，我们还可以预测疾病的自然史，从而实现早期诊断。目前的数学模型包括载脂蛋白E（APOE）基因模型、tau蛋白动力学模型和淀粉样β肽动力学模型。Bcl-2和Bax凋亡理论假定细胞中促凋亡蛋白和抗凋亡蛋白的平衡决定细胞是否经历凋亡，其中Bcl-2模型描述了促凋亡蛋白和抗凋亡蛋白的相互作用，它也被用于包括神经元和神经胶质细胞在内的一系列细胞类型的细胞死亡研究。淀粉样β肽（Aβ）动力学模型解释了大脑中肽的产生和清除过程。tau蛋白动力学模型关注tau蛋白的产生、聚集和清除过程，这些过程被认为与AD有关。APOE基因模型研究阿尔茨海默病风险与APOE基因之间的联系。这些模型已被用于预测阿尔茨海默病的发展方式，并评估不同抑制剂将如何影响疾病进程。这些数学模型反映了生理上有意义的特征，并对训练数据表现出稳健的拟合。纳入Aβ、Tau、APOE等生物标志物以及神经元丢失和认知障碍的标志物，可以对阿尔茨海默病中生物标志物随时间的轨迹做出合理预测。
