University of California, Berkeley - Department of Statistics
Single particle electron microscopy is a powerful method that biophysicists employ to learn about the structure of biological macromolecules. In contrast to the more traditional crystallographic methods, this method images â€œunconstrainedâ€ particles, thus posing a variety of statistical problems. We formulate and study such a problem, one that is essentially of a random tomographic nature, where a structural model for a biological particle is to be constructed given random projections of its Coulomb potential density, observed through the electron microscope. Although unidentifiable (ill-posed), this problem can be seen to be amenable to a statistical solution, once parametric assumptions are imposed. It can also be seen to present challenges both from a data analysis point of view (e.g. uncertainty estimation and presentation) as well as computationally. The proposed methodology will be illustrated on simulated data, and practical issues will be discussed.