Background: Signal transduction is one of the most important biological processes by which cells convert an external signal into a response. Novel computational approaches to mapping proteins onto signaling pathways are needed to fully take advantage of the rapid accumulation of genomic and proteomics information. However, despite their importance, research on signaling pathways reconstruction utilizing large-scale genomics and proteomics information has been limited.

Results: We have developed an approach for predicting the order of signaling pathway components, assuming all the components on the pathways are known. Our method is built on a score function that integrates protein-protein interaction data and microarray gene expression data. Compared to the individual datasets, either protein interactions or gene transcript abundance measurements, the integrated approach leads to better identification of the order of the pathway components.

Conclusions: As demonstrated in our study on the yeast MAPK signaling pathways, the integration analysis of high-throughput genomics and proteomics data can be a powerful means to infer the order of pathway components, enabling the transformation from molecular data into knowledge of cellular mechanisms.

• PMID: 15504238
• DOI full text
• PMC full text
• PubMed
• PubTator

Reference Type

Comparative Study | Journal Article | Research Support, U.S. Gov't, Non-P.H.S. | Research Support, U.S. Gov't, P.H.S.

Authors

Liu Y, Zhao H

Primary Lit For

Additional Lit For

Review For