PDF(Pubmed)
Abstract：
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson\'s disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
摘要：
计算模型已成为研究涉及生物系统和疾病的复杂分子过程的关键工具。在这项研究中,我们应用布尔建模来揭示帕金森病(PD)的分子机制，最常见的神经退行性疾病之一。我们的方法是基于PD地图，一个全面的分子相互作用图，捕捉参与PD的启动和进展的关键机制。使用布尔建模，我们的目标是更深入地了解疾病的动态，确定潜在的药物靶标，并模拟对治疗的反应。我们的分析证明了这种方法在揭示PD复杂性方面的有效性。我们的结果证实了对该疾病的现有知识，并提供了对潜在机制的有价值的见解，最终提出治疗干预的潜在目标。此外，我们的方法允许我们根据组学数据对模型进行参数化,以便进一步进行疾病分层.我们的研究强调了计算建模在促进我们对复杂生物系统和疾病的理解方面的价值，强调在这一领域继续研究的重要性。此外，我们的发现对PD新疗法的开发具有潜在的意义，这是一个紧迫的公共卫生问题。总的来说,这项研究代表了将计算模型应用于神经退行性疾病研究的重要一步，并强调了跨学科方法在解决具有挑战性的生物医学问题方面的力量。
