In the quest for anti‐cancer drugs with high efficacy and low toxicity, cancer metabolism has increasingly been a focus of interest in clinical research. Enhanced glycolysis and robust production of lactate constitute characteristic traits that discriminate many cancerous cells from their normal counterparts. This, in principle, may provide researchers with a general handle on such a complex disease, regardless of the intrinsic genotypic heterogeneity of the single transformed cells. The work carried out during this project and presented in this thesis consists of developing and applying analytical approaches, mainly drawn from the field of metabolic control analysis (MCA), to the study of cancer metabolism. The ultimate goal is to assess whether, and to what extent, the metabolic features of cancer cells may be exploited in the attempt to attack the malignancy more specifically than through traditional clinical approaches. The underlying idea consists of identifying enzymes that represent points of fragility specifically characterising the cancerous metabolic phenotype. These enzymes are such that an alteration in their activity (due for example to the action of an anticancer drug) would elicit the desired response in cancer cells, without affecting their normal counterparts. The application of MCA relies on a mathematical representation of the system under study. Creating such a model is often hampered by the lack of data about the precise kinetic laws governing the different reaction steps and the value of their corresponding parameters. The most important result reached during this project shows that the metabolic quantities defining the normal and cancer phenotypes (such as fluxes and metabolite concentrations), together with heuristic assumptions about the properties of typical enzyme‐catalyzed reactions, already allow for a fast and efficient way to explore the effectiveness of putative drug targets with respect to criteria of high efficacy and low toxicity. The relevance of this result lies in the fact that the quantities defining a metabolic phenotype are experimentally more accessible than the kinetic parameters of the different enzymatic steps in the system.