TOWARD THE IDENTIFICATION OF NEW CANCER THERAPY TARGETS USING METABOLIC MODELING IN A HUMAN GENOME SCALE

Cancer is a leading cause of death in the world, and the mechanisms that underlie this disease are still not completely understood. In the last decades, altered tumor metabolism has been recognized as one of the hallmarks of cancer. Modeling the different phenotypes of healthy and cancer cells will help to make predictions to create effective therapies to prevent, diagnose and treat cancer. We propose a pipeline to study cancer phenotypic traits using the human metabolic genome-scale model (GEM), Recon 2. To overcome the well-known challenges that arise due to the large size and complexity of GEMs, we apply methods that we developed to reduce the model. The purpose is to be able to focus on certain parts of metabolism that are of interest for the study. Furthermore, we compute control coefficients to analyze the fold change in concentrations and fluxes for a fold change in the kinetic parameters. This approach will allow the understanding, at the stoichiometric and kinetic level, of the main metabolic modifications that emerge in cancer development and progression.