A Data Integration Methodology for Systems Biology: Experimental Verification

• 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
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.0508649102

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.