ABSTRACT
This chapter discusses the benefits of interpreting metabolomics data in the context of genome-scale metabolic networks. These networks gather all the metabolic reactions occuring in an organism, a tissue or a cell, thus providing a biological background to decipher the role of metabolites of interest. This chapter first introduces how this list of biochemical reactions is built using genomic or metabolomic data. This knowledge can then be used in a targeted way, by mapping metabolomics data onto metabolic pathways, or, in a more global manner, using the entire network. Both approaches are described in this chapter, revealing how pathway analysis can be achieved using statistical methods and how global network analysis can be performed using graph representation. Turning the network into a metabolic graph consists of modeling metabolites and reactions by a set of nodes connected by edges. Various graph algorithms presented in this chapter can then be used to extract sub-networks of interest based on a list of identified metabolites. Finally, the benefits of using network visualization for data interpretation are discussed.
