| Summary: | Metabolic reprogramming has been defined to be a hallmark of cancer. One major question in cancer research is how to exploit these metabolic alterations for the identification of novel therapeutic strategies. With the outbreak of high throughput –omics data and the advances in genomics, novel holistic and integrative approaches are required to address this question. Systems Biology aims at responding to these needs and has provided the scientific community with a large variety of algorithms and approaches. Among different computational approaches in Systems Biology, Constraint-Based Modeling, based on genome-scale metabolic networks, has received much attention in the last years. They have provided different promising tools to predict metabolic targets in cancer, but, so far, with limited predictive power when compared to experimental data. In this doctoral thesis, we present a novel methodology to more accurately predict metabolic targets in cancer. Our approach is radically different to previous approaches in the literature and relies on a novel concept termed genetic Minimal Cut Sets. The relevance of our approach is shown in two different case studies. First, we applied it to explain the role that RRM1 plays in Multiple Myeloma. Second, we aimed at identifying selective therapeutic strategies in tamoxifen-resistant breast cancer.
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