B.Sc. Chemistry, Koc University, Turkey
Ph.D. Physical Chemistry, University of Texas Austin
Dr. Kirmizialtin received his Ph.D. in Physical Chemistry from the University of Texas, Austin in 2007. Upon completion of his doctoral work Dr. Kirmizialtin joined Prof. Ron Elber’s group at the Institute for Computational Engineering and Sciences, UT-Austin where he continued his research, first, as a postdoctoral fellow and, later, as a research associate. At the Elber lab Dr. Kirmizialtin’s work focused on Molecular Dynamics simulations of long time scale dynamics of conformational transitions.
From 2013 to 2015 Dr. Kirmizialtin worked at the Los Alamos National Laboratories as an Associate Research Scientist in Theoretical Biology and Biophysics. In 2015 Dr. Kirmizialtin joined the NYU, Abu Dhabi faculty in the Chemistry Program.
Dr. Kirmizialtin’s research has a strong cross-disciplinary approach and draws upon insights from physical chemistry, computational science, soft matter, enzyme actions, and biological machinery of information processing. Using theory and computer simulations, Dr. Kirmizialtin’s studies investigate how physical and chemical principles can be applied to understand, predict, and manipulate the behavior of biological macromolecules.
Molecular thermal motions play an essential role in many functionally important biochemical processes such as allosteric transitions, ion permeation through channels, or folding of proteins and nucleic acids. Knowledge of functionally important molecular motions has important implications in medicine and nanotechnology, in addition to providing insights into understanding life.
To achieve this goal Kirmizialtin’s lab is using Molecular Dynamics (MD) Simulations. MD is one of the most powerful theoretical approaches to study the structure, molecular interactions and resulted dynamics in a unified framework. However, the timescale of the biological processes are orders of magnitude longer than the timescales accessible to straightforward MD Simulations. To overcome the time scale gap between simulations and experiments, Kirmizialtin’s lab focuses on developing novel computational methods to extend the timescales of MD simulations.
Another direction in Kirmizialtin lab is to integrate computer simulations and experiments to study the complex biomolecular machinery. Using the restraints coming from experiments such as Cryo-EM, SAXS, and NMR Kirmizialtin lab is developing computational tools and methods using path sampling, metadynamics, and machine learning to help to sample conformations more effectively and guide MD simulations toward the direction where it meets with the experimental signal, providing more accurate description of physical processes and allowing atomic level visualization of the experimental data.