Speakers
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.
