The work in the current thesis is focused on the metabolomics field. In general, studies in this area concentrate on obtaining information concerning the metabolites found in a biological sample. Acquiring and analyzing metabolome data provides a better understanding of the bionetwork's mechanisms. The first part of the thesis focalizes on analyzing metabolome data measured from plant systems. The proposed methodology is based on Principal Component Analysis. The primary goal is to reduce the dimensionality of a large dataset while retaining most of the associated information. The aforementioned methodology additionally provides the chance to obtain assay signatures by using metabolite contributions. Its importance relies on the fact that we are able to define the biomarkers in a biological sample and thus, providing a mean to further examine a desirable cellular function. The second part of the thesis introduces the development of a kinetic model, implemented to describe the anaerobic fermentation of glycerol in Escherichia coli. Setting up the overall model required obtaining the parameters from the literature, and determining the kinetic expressions for each enzyme. The results include the acquisition of the metabolite profiles over time, plus their steady-state concentrations. Additionally, Metabolic Control Analysis was applied to the steady-state conditions, in order to obtain an insight of which enzymes can be considered responsible for the control of flux. The accuracy of the outcome could be verified if the computational efforts were combined with experimental work. The results derived from the kinetic modeling of the anaerobic fermentation of glycerol underline the importance of utilizing methodologies that eliminate the need of kinetic data, like metabolic flux analysis, or the incorporation of non-mechanistic methods, like the log-linear approximation.