Our goal

We study the causes of disease progression, with a specific focus on cancer, in order to identify which extrinsic and intrinsic factors can contribute to disease mutation occurrence in DNA, as well as to discover molecular mechanisms of how these mutations can affect proteins, protein interactions, and dynamical behavior of chromatin. We apply and design computational methods that integrate hypothesis- and data- driven approaches, including machine learning, molecular modeling and molecular dynamics simulations. We work in close collaboration with many experimental groups and use experimental data ranging from hydroxyl radical footprinting, chemical crosslinking to cryo- electron microscopy to guide us in designing new hybrid approaches. From a practical point of view, this can provide predictive power for the behavior of the system in response to disease and offer experimental leads for identifying driver mutations and genes, and for designing drugs that affect protein interactions and pathways.

Graduate student positions are available