Single particle cryo-EM recently joined X-ray crystallography and nuclear magnetic resonance spectroscopy as a high-resolution structural method for biological macromolecules (the 2017 Nobel Prize in Chemistry). Furthermore, cryo-EM has the potential to analyze compositionally and conformationally heterogeneous mixtures and, consequently, can be used to determine the structures of complexes in different functional states. The 3D-structure and the possible structural variability need to be determined from many noisy two-dimensional tomographic projections, whose viewing directions and in-plane rotations are unknown. In this lecture, the speaker will give an overview of the computational challenges in cryo-EM analysis and how he and others are trying to face them, focusing on 3D ab-initio modelling and the heterogeneity problem of determining structural variability.
Bio:
Dr. Singer is a Professor in the Department of Mathematics at Princeton University. His current research is focused on developing algorithms for three-dimensional structuring of macromolecules using cryo-electron microscopy. His mathematical interests are linear and non-linear dimensionality reduction of high dimensional data, signal and image processing, spectral methods, convex optimization and semidefinite programming. He is also a Professor in the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML) at Princeton.