Developing predictive frameworks for the control of non-equilibrium cellular membrane dynamics

Abstract: The material properties of biological membranes control a vast array of molecular processes. Biological lipid membranes behave like fluids in plane and exhibit elastic fluctuations out of plane. While the basic driving forces for describing membrane biophysics are easy to formulate, their emergent properties and morphologies they can elicit remain important open questions. In this project, we seek to leverage modern advances in non-equilibrium statistical mechanics along with ideas from representation learning and AI to identify low dimensional physical laws for the non-equilibrium dynamics of biomimetic membranes. We will use a combination of experiments by Petia Vlahovska and co-workers, and theory from Petia Vlahovska, Suri Vaikuntanathan and co-workers. Briefly, data from experiments studying the fluctuations of model lipid membranes in electric fields mimicking polarized cellular membranes will, to the best of our knowledge for the first time, be analyzed using dimensional reduction techniques to infer physical laws and constraints in low dimensional spaces. These constraints will then be related to modern non-equilibrium thermodynamic bounds. If successful, this integration of theoretical approaches based on thermodynamics and AI and experiments with biomimetic membranes will compactly reveal how biological lipid membrane dynamics can be described, controlled, and leveraged. The project will establish a new collaboration of faculty from University of Chicago and Northwestern University with complementary expertise in statistical and continuum mechanics modeling, and experimental biomimetic membrane systems.