Reference : Fast reconstruction of compact context-specific metabolic network models
Scientific journals : Article
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/15107
Fast reconstruction of compact context-specific metabolic network models
English
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Pacheco, Maria Irene mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Sauter, Thomas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Life Science Research Unit >]
Jan-2014
PLoS Computational Biology
Public Library of Science
10
1
e1003424
Yes (verified by ORBilu)
International
1553-734X
1553-7358
San Francisco
CA
[en] 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
The research was funded by the Luxembourg Centre for Systems Biomedicine and the Life Sciences Research Unit, University of Luxembourg. MPP was supported by fellowships from the National Research Foundation of Luxembourg (FNR; http://www.fnr.lu) (AFR 6041230).
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/15107
10.1371/journal.pcbi.1003424
http://www.ploscompbiol.org/article/fetchObject.action?uri=info%3Adoi%2F10.1371%2Fjournal.pcbi.1003424&representation=PDF

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