title: Selection of human tissue-specific elementary flux modes using gene expression data.
abstract: BACKGROUND
  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.
authors.affiliation: Biomedical Engineering Department, CEIT and Tecnun, University of Navarra, San Sebastian, Spain.
authors.forename: Alberto
authors.initials: A
authors.lastname: Rezola
authors.forename: Jon
authors.initials: J
authors.lastname: Pey
authors.forename: Luis F
authors.initials: LF
authors.lastname: de Figueiredo
authors.forename: Adam
authors.initials: A
authors.lastname: Podhorski
authors.forename: Stefan
authors.initials: S
authors.lastname: Schuster
authors.forename: Angel
authors.initials: A
authors.lastname: Rubio
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Planes
mesh_terms: D000465:Algorithms; D015870:Gene Expression; D015894:Genome, Human; D006801:Humans; D008099:Liver / Q000378:metabolism; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D009928:Organ Specificity; D049490:Systems Biology / Q000379:methods