 My name is Anna Castagnal. I'm Professor Andresso Toriva. My name is Fernanda Piñero. Hi everybody, I'm Aurora Savino. I am a group leader at the Structural Biology Research Center at Human Technopole. I'm the head of the Center for Computational Biology at Human Technopole. And I work in Francesco Yaris Group. And I am a group leader at the Computational Biology Center at Human Technopole. In my laboratory we study a big problem in cancer medicine, which is the emergence of treatment resistance, which is the fact that therapies work very well in the first instance in many patients. However, some patients stop responding to treatment and the tumor comes back. In my laboratory we combine computer algorithms with artificial intelligence and mathematical modeling with genetic data from cancer patients in order to understand why treatment resistance occur and how to prevent it. In our group we are trying to identify the best treatment for each patient in a personalized medicine approach. For this we use huge genomic data from patients and in vitro systems that we analyze through advanced complex tools that we computationally develop in our lab. With this we can select the best drug, which has the highest benefit and lowest amount of side effects for each specific context. Also we can select drugs and apply them to new contexts in a drug-purposing approach. In my lab we use a combination of microscopy, biochemistry and biophysics to understand how multi-protein complexes add biochemical marks on RNA. When this process doesn't work properly in the cell, this can lead to disease and one example is leukemia. If we get to understand deeply how these marks are added and removed on RNA, then we can use this as a target for potential treatments. I work on the problem of antibiotic resistance which is a growing public health concern. Resistant bacteria compromise antibiotic treatment of millions of people worldwide every year so in my research group we combine physics, mathematics, computational methods and experiments to understand and predict the behavior of bacteria under antibiotic challenge. We hope that with the methods we develop we will be able to have informed protocols for sustainable antibiotic use that are going to maximize the desired effect of antibiotics while minimizing collateral effects like for example the emergence of resistance.