Title |
Integration of expression data in genome-scale metabolic network reconstructions
|
---|---|
Published in |
Frontiers in Physiology, January 2012
|
DOI | 10.3389/fphys.2012.00299 |
Pubmed ID | |
Authors |
Anna S. Blazier, Jason A. Papin |
Abstract |
With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of "omics" data, quantifying thousands of cellular components across a variety of scales, ranging from mRNA transcript levels to metabolite quantities. Methods are needed to not only integrate this omics data but to also use this data to heighten the predictive capabilities of computational models. Several recent studies have successfully demonstrated how flux balance analysis (FBA), a constraint-based modeling approach, can be used to integrate transcriptomic data into genome-scale metabolic network reconstructions to generate predictive computational models. In this review, we summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations. |
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Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Science communicators (journalists, bloggers, editors) | 1 | 50% |
Scientists | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 14 | 3% |
Colombia | 2 | <1% |
Germany | 2 | <1% |
Sweden | 2 | <1% |
Luxembourg | 2 | <1% |
Iran, Islamic Republic of | 2 | <1% |
Latvia | 1 | <1% |
Australia | 1 | <1% |
Netherlands | 1 | <1% |
Other | 8 | 2% |
Unknown | 423 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 136 | 30% |
Student > Master | 78 | 17% |
Researcher | 73 | 16% |
Student > Doctoral Student | 24 | 5% |
Professor > Associate Professor | 22 | 5% |
Other | 68 | 15% |
Unknown | 57 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 162 | 35% |
Biochemistry, Genetics and Molecular Biology | 83 | 18% |
Engineering | 52 | 11% |
Computer Science | 41 | 9% |
Chemical Engineering | 15 | 3% |
Other | 41 | 9% |
Unknown | 64 | 14% |