Observing metabolic functions at the genome scale.
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.


CONCLUSIONS
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.
Schwartz
Jean-Marc
JM
Bioinformatics Center, Kyoto University, Uji, Kyoto 611-0011, Japan. jean-marc.schwartz@manchester.ac.uk
Gaugain
Claire
C
Nacher
Jose C
JC
de Daruvar
Antoine
A
Kanehisa
Minoru
M
D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D016681:Genome, Fungal; D012441:Saccharomyces cerevisiae / Q000235:genetics* / Q000378:metabolism*; D014158:Transcription, Genetic