WE'RE HIRING!The Computational Systems Oncology lab integrates algorithmic design, numerical modeling, and molecular biology approaches to address relevant questions in cancer biology and therapeutics.
Cancer emerges through the occurrence and selection of molecular alterations. A main goal in our group is to understand if and how alterations present in a cancer cell favor or veto the selection of specific new ones, thus generating non-random patterns of occurrence across tumor populations.Learn More
Selected genetic and epigenetic modifications in cancer represent implementations of oncogenic mechanisms and actionable targets for precision treatments. We dissect large-scale genomic datasets to nominate selected events and identify oncogenic signatures informing the design of combination therapies.Learn More
Tumor evolution is typically modeled according to Darwinian principles. By integrating human data with simple models of cancer evolutions, we aim at exploring which features associate and drive tumor intrinsic heterogeneity and to what extent this can be predicted.Learn More
DNA 3D structures have recently become accessible through Hi-chromatin conformation capture techniques (Hi-C). Our group has become interested in this approach to explore how cancer alterations and gene regulation depend and/or affect the chromatin structure.Learn More
Currently open positions: