Theory of Single-molecule Chemical Dynamics

University Of Texas At Austin, Austin TX

Investigators

Abstract

Dmitrii E Makarov of the University of Texas at Austin is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop theoretical descriptions of chemical transformations involving molecules of life, as observed with single-molecule resolution. Molecular machines, powering nearly every process in a living organism, have efficiency that is unmatched by artificial mechanisms. As a result, the energy consumption by, for example, an adult human at rest is comparable to that of a single incandescent lightbulb. Single-molecule studies offer an unprecedented opportunity to explore the fundamental origins of this efficiency and to look under the hood of molecular machines by observing individual molecules and molecular machines in action, but their spatial and temporal resolution is severely limited by the fundamental physical laws such as those governing the light emitted by molecules. The Makarov group will tackle this challenge and develop theoretical models describing how biomolecules move and perform their biological functions through a synthesis of chemical theory and computational data analysis, and in collaboration with experimental groups performing measurements on individual molecules. These efforts will be integrated with education and outreach activities that will introduce middle and high school students to modern science, involve undergraduate students in research, and educate young researchers in the cross-disciplinary field of single-molecule science through organization of international summer schools. To tackle the challenge of deciphering molecular mechanisms from inherently incomplete single-molecule observations, Makarov will employ ideas and techniques from information theory. Two broad objectives will be pursued: The first one will focus on developing computational data analysis tools, such as compression-algorithm-based entropy estimators, enabling one to learn about hidden degrees of freedom from experimental observables, particularly with the focus on the emerging multidimensional single-molecule spectroscopies. The second objective will be to develop information-theoretical tools that will address the long-standing problem in stochastic thermodynamics: inferring directionality, entropy production, and energy dissipation from noisy trajectories and/or from partial single-molecule observations. Being at the intersection of chemistry, nonequilibrium statistical mechanics, mathematics, and computer science, these efforts will promote interactions and collaborators among graduate students, postdoctoral researchers and senior scientists across multiple disciplines. Further broader impacts of this project will include developing ties with Texas universities serving underrepresented groups, mentoring middle and high school students, and extensive involvement of undergraduate students in the Makarov group’s research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.