Computational Cell Fate

Research focus

In the contemporary scientific landscape, novel computational tools are key to extract deeper insight from increasingly complex experimental datasets. Our research group addresses this challenge by integrating physics/mathematics with deep learning to extract interpretable causal mechanisms from high dimensional single cell data. We use these models to investigate fundamental questions about cell behavior including 1) How do cells convert noisy signals from their surrounding environment into robust decisions? and 2) How is information relayed across spatial scales from genes to cells and to tissues through multilayered regulatory networks? We collaborate with other group at RIMLS and internationally to validate and apply these predictions to multiple biological contexts including developmental trajectories and cancer progression. 

To know more about the group or inquire about internships/vacancies, you can contact Federico @ federico.bocci at ru.nl