A Data Integration Methodology for Systems Biology: Experimental Verification

The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise cha...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2005-11, Vol.102 (48), p.17302-17307
Main Authors: Daehee Hwang, Smith, Jennifer J., Deena M. Leslie, Weston, Andrea D., Rust, Alistair G., Stephen Ramsey, Pedro de Atauri, Siegel, Andrew F., Bolouri, Hamid, Aitchison, John D., Hood, Leroy
Format: Article
Language:English
Subjects:
Biological Sciences
Cellular biology
Chromatin
Chromatin Immunoprecipitation
Data analysis
Data integration
Datasets
Galactose - genetics
Galactose - metabolism
Genes
Informatics - methods
Information Systems
Metabolism
Microarray Analysis
Monosaccharide Transport Proteins - metabolism
P values
Protein metabolism
Research methodology
Saccharomyces cerevisiae Proteins - metabolism
Software
Systems Biology - methods
Yeast
Yeasts
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Summary:The integration of data from multiple global assays is essential to understanding dynamic spatiotemporal interactions within cells. In a companion paper, we reported a data integration methodology, designated Pointillist, that can handle multiple data types from technologies with different noise characteristics. Here we demonstrate its application to the integration of 18 data sets relating to galactose utilization in yeast. These data include global changes in mRNA and protein abundance, genome-wide protein-DNA interaction data, database information, and computational predictions of protein-DNA and protein-protein interactions. We divided the integration task to determine three network components: key system elements (genes and proteins), protein-protein interactions, and protein-DNA interactions. Results indicate that the reconstructed network efficiently focuses on and recapitulates the known biology of galactose utilization. It also provided new insights, some of which were verified experimentally. The methodology described here, addresses a critical need across all domains of molecular and cell biology, to effectively integrate large and disparate data sets.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.0508649102