Surface Fluctuating Hydrodynamics Methods for the Drift-Diffusion Dynamics of Proteins and Microstructures within Curved Lipid Bilayer Membranes

Paul Atzberger

Professor of Mathematics & Mechanical Engineering
University of California Santa Barbara

Seminar Information

Seminar Series
MAE Department Seminars

Seminar Date - Time
October 12, 2026, 11:00 am
-
12:00

Seminar Location
Hybrid: In Person & Zoom (connection in link below)

Center for Magnetic Recording Research (CMRR)
Auditorium (Immediately left upon entry)

Note: Students must attend in person to receive credit for MAE 205.

Paul Atzberger

Abstract

We introduce surface fluctuating hydrodynamics approaches for investigating transport and fluid-structure interactions arising in cell mechanics. We focus particularly on drift-diffusion dynamics of interacting proteins and microstructures within curved lipid bilayer membranes. We show how a mesoscale stochastic description of the mechanics can be formulated (SPDEs) accounting for geometric contributions, hydrodynamic coupling, and thermal fluctuations. The underlying stochastic equations (SPDEs) pose practical challenges for use in simulations, including, (i) a need for accurate and stable discretizations of geometric terms and differential operators on curved geometries, (ii) techniques for hydrodynamics handling surface incompressibility constraints, and (iii) stiffness from rapid time-scales introduced by the thermal fluctuations. We show how practical spectral methods and meshfree computational approaches can be developed for simulations over long spatial-temporal scales. We then present results for protein and microstructure interactions within membranes and the roles played by hydrodynamic coupling and geometry.

Speaker Bio

Paul J. Atzberger studied mathematics at the Courant Institute at New York University where he received his PhD. Subsequently, he was a postdoctoral fellow at Rensselaer Polytechnic Institute before joining the faculty at the University of California Santa Barbara. His research is in stochastic analysis, scientific computation, and machine learning. He works on fundamental and applied problems in the natural sciences and engineering.