半自动化管理允许建立因果网络模型,用于ApoE−/−小鼠年龄依赖性斑块进展的量化

J. Szostak, F. Martin, M. Talikka, M. Peitsch, J. Hoeng
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引用次数: 6

摘要

动脉粥样硬化斑块失稳过程背后的细胞和分子机制是复杂的,主动脉斑块的分子数据很难解释。生物网络模型可以克服这些困难,并精确量化疾病进展过程中受到影响的分子机制。采用半自动管理管道BELIEF构建动脉粥样硬化斑块不稳定生物网络模型。促进斑块不稳定或破裂的细胞和分子机制在网络模型中被捕获。使用公共转录组数据集来证明网络模型的特异性,并捕获ApoE−/−小鼠主动脉在6周和32周时受到影响的不同机制。我们得出的结论是,网络模型结合网络摄动幅度算法提供了一种在分子水平上跟踪疾病进展的敏感、定量的方法。这种方法可用于研究和量化斑块进展过程中的分子机制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semi-Automated Curation Allows Causal Network Model Building for the Quantification of Age-Dependent Plaque Progression in ApoE−/− Mouse
The cellular and molecular mechanisms behind the process of atherosclerotic plaque destabilization are complex, and molecular data from aortic plaques are difficult to interpret. Biological network models may overcome these difficulties and precisely quantify the molecular mechanisms impacted during disease progression. The atherosclerosis plaque destabilization biological network model was constructed with the semiautomated curation pipeline, BELIEF. Cellular and molecular mechanisms promoting plaque destabilization or rupture were captured in the network model. Public transcriptomic data sets were used to demonstrate the specificity of the network model and to capture the different mechanisms that were impacted in ApoE−/− mouse aorta at 6 and 32 weeks. We concluded that network models combined with the network perturbation amplitude algorithm provide a sensitive, quantitative method to follow disease progression at the molecular level. This approach can be used to investigate and quantify molecular mechanisms during plaque progression.
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