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Published version

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2011-05-19

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cc-by, (c) Serrano et al., 2011
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/21445

Network-based scoring system for genome-scale metabolic reconstructions

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http://dx.doi.org/10.1186/1752-0509-5-76

Abstract

Background: Network reconstructions at the cell level are a major development in Systems Biology. However, we are far from fully exploiting its potentialities. Often, the incremental complexity of the pursued systems overrides experimental capabilities, or increasingly sophisticated protocols are underutilized to merely refine confidence levels of already established interactions. For metabolic networks, the currently employed confidence scoring system rates reactions discretely according to nested categories of experimental evidence or model-based likelihood. Results: Here, we propose a complementary network-based scoring system that exploits the statistical regularities of a metabolic network as a bipartite graph. As an illustration, we apply it to the metabolism of Escherichia coli. The model is adjusted to the observations to derive connection probabilities between individual metabolite-reaction pairs and, after validation, to assess the reliability of each reaction in probabilistic terms. This network-based scoring system uncovers very specific reactions that could be functionally or evolutionary important, identifies prominent experimental targets, and enables further confirmation of modeling results. Conclusions: We foresee a wide range of potential applications at different sub-cellular or supra-cellular levels of biological interactions given the natural bipartivity of many biological networks.

Subject

Biologia de sistemes, Química física

Subject (English)

Systems biology, Physical and theoretical chemistry

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Articles publicats en revistes (Ciència dels Materials i Química Física)

Citation

SERRANO MORAL, Ma. Ángeles (María Ángeles) and SAGUÉS I MESTRE, Francesc. Network-based scoring system for genome-scale metabolic reconstructions. BMC Systems Biology 2011. 5:76. ISSN 1752-0509. [consulted: 9 of August of 2026]. Available at: https://hdl.handle.net/2445/21445

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