On the Potential for Integrating Gene Expression and Metabolic Flux Data
Computational strategies, that integrate genomic-level information and metabolic flux data, have improved both prediction of metabolic fluxes and metabolic network identification. Due to the tight interplay between hierarchical (transcriptional) and metabolic control it is not clear how changes in gene expression drive changes in cellular phenotypes manifested through changes in metabolic fluxes. This raises the questions to what extent a change in a metabolic flux should be attributed to changes in gene expression and/or changes in metabolite concentrations and what kind of conclusions can be drawn by comparing gene expression profiles with the associated metabolic fluxes. This review addresses issues related to modeling approaches that attempt to integrate gene expression and metabolic flux data.
Keywords: Systems biology; gene expression; metabolic flux; modeling
Document Type: Research Article
Publication date: 01 September 2008
- Current Bioinformatics aims to publish all the latest and outstanding developments in bioinformatics. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a wide range of the integration of biology with computer and information science.
The journal focuses on reviews on advances in computational molecular/structural biology, encompassing areas such as computing in biomedicine and genomics, computational proteomics and systems biology, and metabolic pathway engineering. Developments in these fields have direct implications on key issues related to health care, medicine, genetic disorders, development of agricultural products, renewable energy, environmental protection, etc.
Current Bioinformatics is an essential journal for all academic and industrial researchers who want expert knowledge on all major advances in bioinformatics. - Editorial Board
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