A computational approach for ordering signal transduction pathway components from genomics and proteomics Data.
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.
Liu
Yin
Y
Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA. yin.liu@yale.edu <yin.liu@yale.edu>
Zhao
Hongyu
H
D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D023281:Genomics / Q000706:statistics & numerical data*; D020928:Mitogen-Activated Protein Kinases / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data; D020543:Proteome; D040901:Proteomics / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000201:enzymology / Q000235:genetics; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics*
D020543:Proteome; D029701:Saccharomyces cerevisiae Proteins; D020928:Mitogen-Activated Protein Kinases