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- Title
Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model.
- Authors
Yizhak, Keren; Benyamini, Tomer; Liebermeister, Wolfram; Ruppin, Eytan; Shlomi, Tomer
- Abstract
Motivation: The availability of modern sequencing techniques has led to a rapid increase in the amount of reconstructed metabolic networks. Using these models as a platform for the analysis of high throughput transcriptomic, proteomic and metabolomic data can provide valuable insight into conditional changes in the metabolic activity of an organism. While transcriptomics and proteomics provide important insights into the hierarchical regulation of metabolic flux, metabolomics shed light on the actual enzyme activity through metabolic regulation and mass action effects. Here we introduce a new method, termed integrative omics-metabolic analysis (IOMA) that quantitatively integrates proteomic and metabolomic data with genome-scale metabolic models, to more accurately predict metabolic flux distributions. The method is formulated as a quadratic programming (QP) problem that seeks a steady-state flux distribution in which flux through reactions with measured proteomic and metabolomic data, is as consistent as possible with kinetically derived flux estimations.
- Subjects
PROTEOMICS; QUADRATIC programming; METABOLIC regulation; MOLECULAR biology; BIOINFORMATICS
- Publication
Bioinformatics, 2010, Vol 26, Issue 12, pi255
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btq183