|Title:||Mathematics for Cryo-electron Microscopy|
|Group/Series/Folder:||Record Group 8.15 - Institute for Advanced Study|
Series 3 - Audio-visual Materials
Part of workshop 'Mathematics for Cryo-electron Microscopy'.
Title from slide title.
Abstract: Single particle cryo-EM recently joined X-ray crystallography and nuclear magnetic resonance spectroscopy as a high-resolution structural method for biological macromolecules. 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 gives 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.
Prof Amit Singer is Professor of Mathematics at Princeton University, where he also serves as member of the Executive Committees of the Program in Applied and Computational Mathematics (PACM) and of for the Center for Statistics and Machine Learning (CSML). He joined Princeton as an Assistant Professor in 2008. From 2005 to 2008 he was the Gibbs Assistant Professor in Applied Mathematics at the Department of Mathematics, Yale University. Prof Singer received the PhD degree in Applied Mathematics from Tel Aviv University in 2005. He served in the Israeli Defense Forces during 1997-2003. He received numerous honors including the Simons Math+X Investigator Award (2017), the US National Finalist for Blavatnik Awards for Young Scientists (2016), the Moore Investigator in Data-Driven Discovery (2014), the Simons Investigator Award (2012) and the Alfred P. Sloan Research Fellowship (2010). His current research focuses on theoretical and computational aspects of data science, and on developing computational methods for structural biology.
Duration: 76 min.
|Appears in Series:||8.15:3 - Audio-visual Materials|
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