Through molecular modeling and machine learning to epigenetic mechanisms
We study the associations between key components in the epigenome to understand how its perturbation can lead to cancer. Our team works to identify factors contributing to cancer mutation occurrence in DNA, to discover molecular mechanisms of how mutations and covalent modifications affect nucleosomes and chromatin, their interactions, stability and dynamics. We apply and design computational methods that integrate hypothesis- and data- driven approaches, including machine learning/deep learning, molecular modeling and molecular dynamics simulations. We work in close collaboration with experimental groups and develop hybrid integrative approaches that use experimental data - ranging from hydroxyl radical footprinting, chemical crosslinking to cryo-electron microscopy - to guide us in molecular modeling and simulations. This multi-faceted approach can offer experimental leads for identifying driver mutations and genes, and for designing drugs that affect chromatin-related pathways.