The systematic study of the genetic fingerprint (genomics) and the biochemistry (metabolites) that goes with a specific cellular process requires the characterization of all the small molecules that form the profile of metabolites and the associated genes. The metabolome represents the collection of all the metabolites during certain process in an organism. The transcriptome represents the gene expression profile, all the messengers RNA in a defined condition. Then to understand the whole process, the studies of metabolites must be accompanied with studies of the gene expression, hence the metabolome must be accompanied by the transcriptome, so we can identify genes and metabolites whose synthesis is induced by a specific process, an infection or stress. Studies of metabolomics generate an enormous amount of data, then they need mathematical and computational tools to establish the correlations between the biochemical and genetic data, and to build up networks that represent the complex metabolic interactions that occur in each case, using tools like Graph and Networks Theory to elucidate the emergent properties inherent to the complex interactions of the metabolic maps. This paper describes the major mathematical tools that can be used for these studies, with emphasis on a semi-qualitative proposal known as the kinetic structural model.