Seminari di gruppo IV

Andrea Esposito, "Unfolding the genome: hybrid Polymer Physics and Machine Learning approach to investigate the 3D architecture of chromosomes"




In the nucleus of eukaryotic cells, chromosomes exhibit complex, far from random three-dimensional (3D) conformations that are crucially linked to gene regulation and cellular functionality, with misfolding or aberrant organization possibly leading to severe pathological conditions. Thanks to the wealth of data generated from recent biochemical experiments, principled approaches from statistical physics can be effectively applied to investigate the mechanisms underlying such a complex architecture of the genome. In this talk, I will show that a simple polymer model, based on the process of micro phase separation, combined with machine learning and molecular dynamics, allows us to make sense of recent experimental observations across entire human and mouse genomes, providing new insights into the genomic structure-function relationship in both healthy and disease states. I will also highlight how these interdisciplinary approaches provide a robust framework for predicting the impacts of genetic variations on the 3D genome architecture, offering potential pathways for therapeutic interventions.