Federica Eduati is an assistant professor at the department Biomedical Engineering (research group Computational Biology), working on systems biology for oncology. She investigates why patients differentially respond to cancer therapy, and how personalized therapy based on biomarkers can be developed. In particular, she approaches the complex problem of precision medicine by applying dynamic mathematical modeling approaches and machine learning techniques to investigate signaling pathways, their deregulation in cancer, and the specific effect of targeted therapy. Since precision medicine would largely benefit from the possibility to perform phenotypic drug screening directly on live patient tumor cells, she has also been collaborating on the development of a microfluidics platform for this purpose. She is now expanding her research to study the interaction between tumor and immune cells, using a systems biology approach to tackle the multiple open questions of immuno-oncology.
Federica Eduati received a BSc in Biomedical Engineering (2006) and a MSc in Bioengineering (2008) from the University of Padova, Italy (both cum laude). During her PhD research she worked in the field of systems biology using different computational modeling approaches in order to gain new insights into biological systems. Following her graduation in 2013, she became a postdoc (EMBL EIPOD, co-funded Marie Curie fellowship) at the European Molecular Biology Laboratory (EMBL), shared between the Systems Biomedicine group of Julio Saez-Rodriguez at EMBL-EBI (Cambridge, UK) and the microfluidics group of Christoph Merten at EMBL-HD (Heidelberg, Germany). Since 2018 she is assistant professor in the Computational Biology group at Eindhoven University of Technology (TU/e, The Netherlands), working on systems biology for (immuno-)oncology. In 2013, Federica was co-organizer of the NIEHS-NCATS-UNC DREAM Toxicogenetics Challenge (http://dreamchallenges.org/).
Multi-omics profiling of the tumor microenvironmentFrontiers in Oncology (2018)
A microfluidics platform for combinatorial drug screening on cancer biopsiesNature Communications (2018)
Logic modeling in quantitative systems pharmacologyCPT: Pharmacometrics & Systems Pharmacology (2017)
Drug resistance mechanisms in colorectal cancer dissected with cell type-specific dynamic logic modelsCancer Research (2017)
Rapid identification of optimal drug combinations for personalized cancer therapy using microfluidicsbioRxiv (2016)