ABSTRACT
Metabolism is characterized by the activity of enzymes regulating the velocity of biochemical reactions through a metabolic network (metabolic fluxes). Metabolomics and proteomics provide quantitative measurements of metabolites and proteins, respectively. However, inferring metabolic fluxes from these “omics” may lead to erroneous conclusions because there is not a linear relation between protein levels and metabolic fluxes and because metabolite concentration may result from different combinations of metabolic fluxes (i.e., high metabolite concentration can be due to high production or low consumption). Typically, this information is incorporated by using computational models that permits to determine the metabolic reactions fluxes (fluxomics). This chapter summarizes the two most relevant model-driven approaches used to study metabolism: (i) Kinetic modeling and (ii) Constraint-based modeling. Briefly, kinetic modeling enables to study the dynamic behavior of relatively small-size metabolic networks and permits to incorporate stable isotopomer tracing. On the other hand, constraint-based stoichiometric modeling is well suited for omic data integration and permits to characterize the metabolic flux profile of large-scale metabolic systems such as Genome-scale metabolic networks. Finally, some of the most relevant applications of metabolic modeling and novel strategies combining both model-driven methods will be exposed as well as the future challenges in this discipline.
