Aug 8 – 10, 2019
Flatiron Institute
America/New_York timezone

Computing and Understanding Statistical Models for Heterogeneous Biological Nano-machines

Aug 8, 2019, 5:15 PM
2h
Ingrid Daubechies Auditorium (Flatiron Institute)

Ingrid Daubechies Auditorium

Flatiron Institute

162 Fifth Ave, 2nd fl. New York, NY 10010

Speakers

Yunye Gong (Cornell University)Prof. Peter C. Doerschuk (Cornell University)

Description

The goal of this research is to understand the dynamical motion of nanoscale biological machines such as viruses directly from large sets of data. The ideal data would be 4-D measurements (3 spatial and 1 temporal) on each instance of the machine. The most informative available data are 2-D cryo-electron microscopy projection images of flash-frozen instances, one image for each instance. Because the instances are flash frozen, each image represents a sample from the statistical mechanical ensemble of the machine and the fact that many instances are available is the key to learning the 4-D behavior of the machine. Our proven learning methods based on maximum likelihood estimators can provide the 6-D spatial covariance function of the electron scattering intensity of the machine and the covariance function includes dynamical motion information. But 6-D information is challenging to interpret. Our goal is to understand the 4-D behavior of the machine by learning a generative mathematical mechanical model of the machine based on the connection between the model and the experiment that is provided by statistical mechanics. From such a mathematical model we can bring the machine back to life in 4-D and compute all of the machine’s dynamical behaviors.

Primary authors

Yunye Gong (Cornell University) Prof. Peter C. Doerschuk (Cornell University)

Presentation materials

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