Research

Observing metabolic functions at the genome scale

Jean-Marc Schwartz^1,2* , Claire Gaugain^3* , Jose C Nacher^1,4 , Antoine de Daruvar³ and Minoru Kanehisa¹

¹  Bioinformatics Center, Kyoto University, Uji, Kyoto 611-0011, Japan

²  Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK

³  Centre de Bioinformatique de Bordeaux, Université Bordeaux 2, 33076 Bordeaux, France

⁴  Department of Complex Systems, Future University, Hakodate, Hokkaido 041-8655, Japan

author email corresponding author email* Contributed equally

Genome Biology 2007, 8:R123doi:10.1186/gb-2007-8-6-r123

Published:	26 June 2007

Subject areas: Bioinformatics, Genome studies, Model organisms

Abstract

Background

High-throughput techniques have multiplied the amount and the types of available biological data, and for the first time achieving a global comprehension of the physiology of biological cells has become an achievable goal. This aim requires the integration of large amounts of heterogeneous data at different scales. It is notably necessary to extend the traditional focus on genomic data towards a truly functional focus, where the activity of cells is described in terms of actual metabolic processes performing the functions necessary for cells to live.

Results

In this work, we present a new approach for metabolic analysis that allows us to observe the transcriptional activity of metabolic functions at the genome scale. These functions are described in terms of elementary modes, which can be computed in a genome-scale model thanks to a modular approach. We exemplify this new perspective by presenting a detailed analysis of the transcriptional metabolic response of yeast cells to stress. The integration of elementary mode analysis with gene expression data allows us to identify a number of functionally induced or repressed metabolic processes in different stress conditions. The assembly of these elementary modes leads to the identification of specific metabolic backbones.

Conclusion

This study opens a new framework for the cell-scale analysis of metabolism, where transcriptional activity can be analyzed in terms of whole processes instead of individual genes. We furthermore show that the set of active elementary modes exhibits a highly uneven organization, where most of them conduct specialized tasks while a smaller proportion performs multi-task functions and dominates the general stress response.