Metabolism is primarily or secondarily affected in several human diseases. In the last years, in-silico modeling is gaining importance because the characterization of the metabolic changes occurring in health and disease states has a wide range of implications, from elucidation of pathogenic mechanisms to development of new biomarkers and drug discovery. Cancer cells have to reshape their metabolic networks to support the synthesis of biomass components and generate the energy required for cellular growth. However, a comprehensive understanding of the selective advantage of these alterations is still lacking due to the fact that metabolic reactions are interconnected in a complex network where dysfunction of a single enzyme may result in perturbation of multiple distant pathways. We will see that Differential Flux-balance Analysis, a tool that we have previously developed, represents an useful tool to decipher the complexity of the networks of metabolic reactions simultaneously deregulated in cancer cells. As a case-study, we will simulate metabolic reprogramming due to the mutations of TCA cycle enzymes that are known to drive oncogenesis.