10/12 – Bin Zhang, Massachusetts Institute of Technology

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Speaker: Bin Zhang

Pfizer-Laubach Career Development Assistant Professor, Massachusetts Institute of Technology

Title: Learning the mechanism of genome folding from big data

Abstract:

The three-dimensional genome organization plays an essential role in all DNA-templated processes, including gene transcription, gene regulation, DNA replication, etc. Computational modeling can be an effective way of building high-resolution genome structures and improving our understanding of these molecular processes. We are developing algorithms that integrate polymer simulations and experimental data to characterize the genome organization at a coarsened resolution. Our approach stands out from existing methodologies because of its emphasis on building a rigorous statistical mechanics framework. This framework makes it possible to address complex cell biology problems with theoretical chemistry approaches via the energy landscape concept. Our studies have led to the development of powerful tools for de novo prediction of genome organization, and have uncovered novel mechanisms that govern chromosome's arrangement inside the nucleus. I will also discuss a novel method that combines statistical mechanics and machine learning to analyze the fluctuation of chromatin conformation detected in microscopy experiments. This method can provide much needed dynamical information for chromatin folding that remains difficult to obtain with existing imaging techniques.