Fast Reconstruction of Compact Context-Specific Metabolic Network Models

Nikos Vlassis, Maria Pires Pacheco and Thomas Sauter

PLOS Computational Biology, 2014, vol. 10, issue 1, 1-10

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.Author Summary: Metabolism comprises all life-sustaining biochemical processes. It plays an essential role in various aspects of biology, including the development and progression of many diseases. As the metabolism of a living cell involves several thousands of small molecules and their conversion, a full analysis of such a metabolic network is only feasible using computational approaches. In addition, metabolism differs significantly from cell to cell and over different contexts. Therefore, the efficient generation of context-specific mathematical models is of high interest. We present fastcore, a fast algorithm for the reconstruction of compact context-specific metabolic network models. The algorithm takes as input a global metabolic model and a set of reactions that are known to be active in a given context, and it produces a context-specific model. fastcore is significantly faster than other algorithms, typically obtaining a genome-wide reconstruction in a few seconds. High-throughput model building will soon become a common procedure for the integration and analysis of omics data, and we foresee many future applications of fastcore in disease and patient specific metabolic modeling.

Date: 2014
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003424 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 03424&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1003424

DOI: 10.1371/journal.pcbi.1003424

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol ().