Sasidharakurup Hemalatha, Melethadathil Nidheesh, Nair Bipin, Diwakar Shyam
Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham (Amrita University) , Kollam, India .
OMICS. 2017 Aug;21(8):454-464. doi: 10.1089/omi.2017.0056.
Parkinson's disease (PD), a neurodegenerative disorder, affects millions of people and has gained attention because of its clinical roles affecting behaviors related to motor and nonmotor symptoms. Although studies on PD from various aspects are becoming popular, few rely on predictive systems modeling approaches. Using Biochemical Systems Theory (BST), this article attempts to model and characterize dopaminergic cell death and understand pathophysiology of progression of PD. PD pathways were modeled using stochastic differential equations incorporating law of mass action, and initial concentrations for the modeled proteins were obtained from literature. Simulations suggest that dopamine levels were reduced significantly due to an increase in dopaminergic quinones and 3,4-dihydroxyphenylacetaldehyde (DOPAL) relating to imbalances compared to control during PD progression. Associating to clinically observed PD-related cell death, simulations show abnormal parkin and reactive oxygen species levels with an increase in neurofibrillary tangles. While relating molecular mechanistic roles, the BST modeling helps predicting dopaminergic cell death processes involved in the progression of PD and provides a predictive understanding of neuronal dysfunction for translational neuroscience.
帕金森病（PD）是一种神经退行性疾病，影响着数百万人，因其在影响运动和非运动症状相关行为方面的临床作用而受到关注。尽管从各个方面对帕金森病的研究日益普遍，但很少有研究依赖于预测系统建模方法。本文运用生化系统理论（BST），试图对多巴胺能细胞死亡进行建模和表征，并了解帕金森病进展的病理生理学。利用包含质量作用定律的随机微分方程对帕金森病通路进行建模，并从文献中获取建模蛋白质的初始浓度。模拟结果表明，在帕金森病进展过程中，与对照组相比，由于多巴胺能醌和3,4-二羟基苯乙醛（DOPAL）的增加导致失衡，多巴胺水平显著降低。与临床观察到的帕金森病相关细胞死亡相关，模拟显示帕金森蛋白和活性氧水平异常，神经原纤维缠结增加。在关联分子机制作用时，BST建模有助于预测帕金森病进展过程中涉及的多巴胺能细胞死亡过程，并为转化神经科学提供对神经元功能障碍的预测性理解。
