Dmitrii Makarov

Randomness, Complexity, and Information with Applications to Single-Molecule Science

The mathematical analogy between information and thermodynamical entropy has recently led to promising developments in chemistry and physics, and information theory tools are increasingly important in chemical and biological data analysis. In this talk I will describe a few of our ideas at the intersection of physical chemistry, information theory, and computer science, with the focus on single-molecule data analysis. Single-molecule experimental studies have opened a new window into the elementary biochemical steps, function of molecular machines, and cellular phenomena. The information contained in single-molecule trajectories is however often underutilized in that oversimplified models such as one-dimensional diffusion or one-dimensional random walk are used to interpret experimental data. I will show that much finer details of single-molecule dynamics, such as conformational memory and static disorder, can be deduced from an analysis that is similar to Shannon’s analysis of the printed English; in particular, this method relates conformational memory to the information-theoretical compressibility of single-molecule signals.