Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism

Simple item page
dc.creatorNemenman, Ilya
dc.creatorEscola, G. Sean
dc.creatorHlavacek, William S.
dc.creatorUnkefer, Pat J.
dc.creatorUnkefer, Clifford J.
dc.creatorWall, Michael E.
dc.date2007-06-13
dc.date.accessioned2026-07-07T08:43:15Z
dc.date.available2026-07-07T08:43:15Z
dc.descriptionWe investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For this, we generate synthetic metabolic profiles for benchmarking purposes based on a well-established model for red blood cell metabolism. A variety of data sets is generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We apply ARACNE, a mainstream transcriptional networks reverse engineering algorithm, to these data sets and observe performance comparable to that obtained in the transcriptional domain, for which the algorithm was originally designed.
dc.description14 pages, 3 figures. Presented at the DIMACS Workshop on Dialogue on Reverse Engineering Assessment and Methods (DREAM), Sep 2006
dc.identifierhttps://arxiv.org/abs/0706.2007
dc.identifierhttp://arxiv.org/abs/0706.2007
dc.identifierAnn. N.Y. Acad. Sci. 1115: 102â?"115 (2007)
dc.identifierdoi:10.1196/annals.1407.013
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/142252
dc.subjectMolecular Networks
dc.titleReconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism
dc.typetext

Files

Collections

arXiv