网络拓扑信息的来源Network topology as a source of information |
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课程网址: | http://videolectures.net/solomon_przulj_topology/ |
主讲教师: | Nataša Pržulj |
开课单位: | 伦敦帝国学院 |
开课时间: | 2012-12-03 |
课程语种: | 英语 |
中文简介: | 许多现实世界的现象可以表示为互连实体的网络。例如,单个基因只是达到目的的手段:它们产生的蛋白质以复杂的网络方式相互作用,使我们的细胞发挥作用。因此,使用蛋白质相互作用网络(PIN)来预测蛋白质功能和参与疾病在后基因组时代受到了很多关注。我们开发了新的网络拓扑测量方法来预测人类PIN中未注释蛋白质的功能。我们发现,参与关键生物过程和途径的人类基因,如衰老,癌症,传染病,信号传导和药物靶向途径,占据了与其“脊柱”相对应的网络区域。连接所有其他网络部分,因此可以在整个网络中快速传递蜂窝信号。我们设计了从网络拓扑中获取信息并获得新生物信息的方法,例如建议用于治疗干预的新药物靶标。例如,我们基于网络的预测参与人类细胞黑素生成的新蛋白质是表型验证的。 |
课程简介: | Many real-world phenomena can be represented as networks of interconnected entities. For example, individual genes are just a means to an end: they produce proteins that interact in complex networked ways and make our cells work. Hence, using protein interaction networks (PINs) to predict protein function and involvement in disease has received much attention in the post-genomic era. We develop novel measures of network topology to predict function of unannotated proteins in the human PIN. We find that human genes involved in key biological processes and pathways, such as aging, cancer, infectious diseases, signaling and drug-targeted pathways, occupy regions of the network that correspond to its “spine” that connects all other network parts and can thus pass cellular signals fast throughout the network. We design methods that harvest information from network topology and gain new biological information, such as suggest novel drug targets for therapeutic intervention. For example, our network-based predictions of novel proteins that participate in melanogenesis in human cells are phenotypically validated. |
关 键 词: | 细胞蛋白; 蛋白质; 网络拓扑 |
课程来源: | 视频讲座网 |
最后编审: | 2020-06-29:yumf |
阅读次数: | 74 |