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Báo cáo y học: "odel-independent fluxome profiling from 2H and 13C experiments for metabolic variant discriminationm"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: Model-independent fluxome profiling from 2H and 13C experiments for metabolic variant discriminationm. | Method Open Access Model-independent fluxome profiling from 2H and 13C experiments for metabolic variant discrimination Nicola Zamboni and Uwe Sauer Address Institute of Biotechnology ETH Zurich CH-8093 Zurich Switzerland. Correspondence Uwe Sauer. E-mail sauer@biotech.biol.ethz.ch Published 16 November 2004 Genome Biology 2004 5 R99 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2004A5 l2 R99 Received 28 August 2004 Revised 18 October 2004 Accepted 25 October 2004 2004 Zamboni and Sauer licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract We introduce a conceptually novel method for intracellular fluxome profiling from unsupervised statistical analysis of stable isotope labeling. Without a priori knowledge on the metabolic system we identified characteristic flux fingerprints in 10 Bacillus subtilis mutants from 132 2H and l3C tracer experiments. Beyond variant discrimination independent component analysis automatically mapped several fingerprints to their metabolic determinants. The approach is flexible and paves the way to large-scale fluxome profiling of any biological system and condition. Background Genome-wide analyses of cellular mRNA protein or metabolite complements have become workhorses in biological research that produce unprecedented amounts of data on cellular network composition. In contrast to such compositional information molecular fluxes through intact metabolic networks link genes and proteins to higher-level functions that result from biochemical and regulatory interactions between the components 1 . As such quantitative knowledge of in vivo molecular fluxes is highly relevant to functional genomics metabolic engineering and systems .