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Publicaciones científicas más recientes (desde 2010)

Autores: Rezola, Alberto; de Figueiredo, L.F.; et al.
Revista: BIOINFORMATICS
ISSN 1367-4803  Vol. 29  Nº 16  2013  págs. 2009 - 2016
Motivation: The analysis of high-throughput molecular data in the context of metabolic pathways is essential to uncover their underlying functional structure. Among different metabolic pathway concepts in systems biology, elementary flux modes (EFMs) hold a predominant place, as they naturally capture the complexity and plasticity of cellular metabolism and go beyond predefined metabolic maps. However, their use to interpret high-throughput data has been limited so far, mainly because their computation in genome-scale metabolic networks has been unfeasible. To face this issue, different optimization-based techniques have been recently introduced and their application to human metabolism is promising. Results: In this article, we exploit and generalize the K-shortest EFM algorithm to determine a subset of EFMs in a human genome-scale metabolic network. This subset of EFMs involves a wide number of reported human metabolic pathways, as well as potential novel routes, and constitutes a valuable database where high-throughput data can be mapped and contextualized from a metabolic perspective. To illustrate this, we took expression data of 10 healthy human tissues from a previous study and predicted their characteristic EFMs based on enrichment analysis. We used a multivariate hypergeometric test and showed that it leads to more biologically meaningful results than standard hypergeometric. Finally, a biological discussion on the characteristic EFMs obtained in liver is conducted, finding a high level of agreement when compared with the literature.
Autores: Gutiérrez-Gutiérrez, Jesús, (Autor de correspondencia); Iglesias, I.; Podhorski, Adam;
Revista: SIGNAL PROCESSING
ISSN 0165-1684  Vol. 91  Nº 9  2011  págs. 2237 - 2245
In the present paper we derive a formula for the geometric minimum mean square error (GMMSE) for one-sided and two-sided finite-length vector linear predictors. This formula is written only in terms of the autocorrelation matrix of the vector process being predicted. We also obtain a formula for the GMMSE for one-sided and two-sided infinite-length vector linear predictors of any wide sense stationary (WSS) vector process. This GMMSE expression for the infinite-length case is derived from the GMMSE expression obtained for the finite-length case.
Autores: Rezola, Alberto; de Figueiredo, L.F.; Brock, M.; et al.
Revista: BIOINFORMATICS
ISSN 1367-4803  Vol. 27  Nº 4  2011  págs. 534 - 540
Motivation: The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. Results: In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach.