Automated modelling of signal transduction networks

[编者的话]

本文试图从二维电泳以及芯片数据中获得的蛋白与蛋白相互作用关系来还原细胞内信号传导网络，这个方法不需要任何先验知识。尽管这种方法有些过于简化，但是根据作者的验证，仍然有不错的效果。

Background

Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved.

Results

We have developed a computational approach for generating static models of signal transduction networks which utilizes proteininteraction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles.

Conclusion

We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.

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