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利用细胞特异性的基于逻辑的通路模型揭示原代和转化后的肝细胞之间的信号差异
Uncovering signalling differences between primary and transformed hepatocytes using cell-specific logic-based pathway models |
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| 课程网址: |
http://videolectures.net/cancerbioinformatics2010_rodriguez_usdb/
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| 主讲教师: |
Julio Saez Rodriguez
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| 开课单位: |
欧洲生物信息研究所
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| 开课时间: |
2010-11-29
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| 课程语种: |
英语
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| 中文简介: |
在过去的几年中，在致癌基因和它们所运行的信号网络的识别方面取得了显著的进展。总结文献知识的路径图是广泛而有用的，但它不允许预测路径的运行，通常也不包含细胞类型的具体信息。这是一个关键的局限性，因为治疗干预的目标正是正常细胞和病变细胞之间的这些差异。我们开发了一种基于通用路径图和高通量磷蛋白组学数据构建信号网络细胞特异性逻辑模型的有效方法。这些方法被嵌入到MATLAB工具箱CellNetOptimizer中，它与DataRail协同工作，后者是管理和转换各种数据的补充工具箱。我们为不同的细胞类型开发了模型，使我们能够阐明信号网络接线的显著差异。我们的研究结果表明，癌细胞在信号处理过程中存在异质性，但它们具有共同的通路异常，这些通路异常赋予了它们致癌特性。
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| 课程简介: |
The last years have witnessed remarkable progress in the identification of oncogenes and the signaling networks in which they operate. Pathway maps summarizing literature knowledge are widespread and useful, but they do not allow prediction of pathway operation and do not usually include cell-type specific information. This is a critical limitation because it is precisely these differences between normal and diseased cells that are targeted for therapeutic intervention. We have developed an efficient method to construct cell-specific logic models of signaling networks based on generic pathway maps and high-throughput phosphoproteomics data. These methods are embedded in the MATLAB toolbox CellNetOptimizer, which works in concert with DataRail a complementary toolbox for managing and transforming varied data. We developed models for different cell types that allow us to elucidate significant differences in the wiring of signaling networks. Our results suggest that cancer cell lines are heterogeneous in their signal processing, yet share pathway abnormalities that confer oncogenic properties on them.
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| 关 键 词: |
致癌基因; 信号网络的识别; 路径图; 细胞特异性; 逻辑模型; 癌细胞
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-10-22：cwx
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| 阅读次数: |
22
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