BME Seminar Series: Maria Gracheva, Ph.D.

Tuesday, January 20, 2015
8:30 a.m.

Goergen Hall 101 (Sloan Auditorium)

"Computer Simulations of Separation and Characterization of Biological Molecules with an Electrically Tunable Membrane" 

Maria Gracheva
Assistant Professor, Department of Physics
Clarkson University

Abstract: In this talk, I will introduce the computational models that we developed in our group to study biological and artificial molecules such as DNA, proteins and nanoparticles in general, that interact with a solid state membrane carrying a nanopore. We are interested in learning how an electrically tunable semiconductor membrane influences the dynamics of the translocating molecule. Ultimately, we are interested in the development of these membranes for sensing and filtering applications.

Our computational models describe both, the electrically tunable membrane and the translocating molecule. In this particular project, we study the dynamics of the protein insulin placed near a nanopore of an electrically tunable semiconductor membrane. Previously, we showed that the tunable local electric field arising inside the membrane can effectively control interaction of filtered objects with the nanopore to either block its passage or increase the translocation rate by modulating the electroosmotic flow direction and magnitude. Collecting statistical information while tracking the movement of a full atomic protein model is computationally expensive since number of atoms in a complex biomolecule ranges in the thousands. Using Brownian dynamics method we calculate the trajectory of the modeled protein in the electrolyte-membrane electric potential. The time spent by the protein before a successful translocation and the translocation times were both analyzed. Our results indicate that the localized electric field within the nanopore affects the movement of the protein. Also, by comparing the results of the full atomic protein model with a coarse grained model and a single bead model, we evaluate which model best approximates the full atomic protein model.