Title |
Fast Reconstruction of Compact Context-Specific Metabolic Network Models
|
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Published in |
PLoS Computational Biology, January 2014
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DOI | 10.1371/journal.pcbi.1003424 |
Pubmed ID | |
Authors |
Nikos Vlassis, Maria Pires Pacheco, Thomas Sauter |
Abstract |
Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 18% |
Germany | 1 | 9% |
Norway | 1 | 9% |
China | 1 | 9% |
Unknown | 6 | 55% |
Demographic breakdown
Type | Count | As % |
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Scientists | 5 | 45% |
Members of the public | 5 | 45% |
Science communicators (journalists, bloggers, editors) | 1 | 9% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 7 | 2% |
Iran, Islamic Republic of | 2 | <1% |
Luxembourg | 2 | <1% |
Colombia | 1 | <1% |
Brazil | 1 | <1% |
United Kingdom | 1 | <1% |
Singapore | 1 | <1% |
Germany | 1 | <1% |
Belgium | 1 | <1% |
Other | 3 | <1% |
Unknown | 339 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 101 | 28% |
Student > Master | 59 | 16% |
Researcher | 54 | 15% |
Student > Bachelor | 27 | 8% |
Student > Doctoral Student | 14 | 4% |
Other | 45 | 13% |
Unknown | 59 | 16% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 97 | 27% |
Biochemistry, Genetics and Molecular Biology | 79 | 22% |
Computer Science | 36 | 10% |
Engineering | 29 | 8% |
Chemical Engineering | 14 | 4% |
Other | 35 | 10% |
Unknown | 69 | 19% |