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利用基于细胞特异性逻辑的通路模型揭示原代和转化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: |
在过去的几年里，在癌基因的识别和它们运作的信号网络方面取得了显著的进展。总结文献知识的通路图是广泛和有用的，但它们不允许预测通路的运作，通常不包括细胞类型特异性信息。这是一个关键的限制，因为正是这些正常细胞和病变细胞之间的差异才是治疗干预的目标。我们已经开发了一种有效的方法来构建基于通用路径图和高通量磷酸蛋白质组学数据的细胞特异性信号网络逻辑模型。这些方法嵌入在MATLAB工具箱CellNetOptimizer中，该工具箱与DataRail（用于管理和转换各种数据的补充工具箱）协同工作。我们开发了不同细胞类型的模型，使我们能够阐明信号网络布线的显著差异。我们的结果表明，癌细胞系在信号处理上是异质的，但它们有共同的通路异常，从而赋予它们致癌特性。
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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: |
癌基因; 细胞特异性逻辑; 肝细胞
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| 课程来源: |
视频讲座网
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| 数据采集: |
2020-12-29：yxd
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| 最后编审: |
2020-12-29：yxd
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| 阅读次数: |
39
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