Cell membranes are marvels of molecular self-assemblies. Primarily formed of amphiphilic lipid molecules,  they are thought to be the first structures formed in protocells and are the most common means of cellular compartmentalization. In fact, it is fascinating how this 2-molecule thick layer regulates so many cellular functions. What is more intriguing is the seemingly contradictory requirements that membranes have to satisfy: they should be fluid enough to allow lipids and proteins to reorganize on the time scales of signaling events, yet rigid enough to maintain structural integrity. Our lab specializes in experimental and computational methods capable of investigating this dynamic interplay within model lipid membranes on the nanoscale, i.e. at unprecedented length and time scales over which molecular events occur! 

Fundamental questions include:

• What structure-property relations govern membrane mechanics and fast molecular dynamics? 
• How can these relations be utilized in understanding disease (e.g. viral infection) or in designing stable liposomal drug/vaccine carriers? 
• How do membranes dynamically respond to molecular inclusions (e.g. sterols, drug molecules) and protein binding events?  
• Can lateral lipid heterogeneities control collective membrane dynamics and related biophysical functions? 

To address these questions, we use a suite of experimental methods including X-ray and neutron scattering, Langmuir monolayer studies, calorimetry, and deuterium NMR (in collaboration with M. Brown Lab at U. Arizona). We often synergistically combine our results with molecular dynamics simulations, through computing time proposals or through many wonderful collaborators that we are fortunate to have. 

Cell membranes adopt various shapes and curvatures, much of which are driven by dynamic cytoskeletal deformations and are critical to the cell function. While very little is known about membrane restructuring during such events, numerous observations allude to the possibility that cells use membrane reshaping as a machinery to translate mechanical signals into compositional rearrangements and subsequent biochemical processes. To address this pressing question, we utilize topographically tunable nanopatterned hydrogel scaffolds that emulate the cell cytoskeleton. This system allows real-time observations of lateral membrane organization and protein-membrane interactions in response to membrane topography and provides insights into critical membrane functions, namely curvature-driven domain stabilization and protein recruitment. Our system also offers a promising route to thermally switchable membrane-based biosensors. The aim of this project is to understand:   

• how imposed curvature dictates lateral domain formation, stabilization, and localization
• how dynamic changes in scaffold topography influence membrane remodeling
• how peripheral protein binding responds to local changes in membrane curvature

To adress these questions we utilize a suite of experimental approaches including AFM, fluorescence microscopy, and neutron reflectometry as well as molecular dynamics simulations. 

Structured nanomaterials are attractive candidates for a wide range of applications, including nanofluidics, molecular sorting, biological sensors, and lab-on-chip devices. These materials often involve nanoscale features that are critical for envisioned functionality but are typically challenging for traditional characterization methods. In order to incorporate functionality, responsive and tunable features are often desired. In our lab, we are particularly interested in the use of stimuli-responsive polymer coatings for functional nanostructured interfaces. Our focus is on perdiodic interfaces, commonly found in a wide range of applications. To undertsand the responsive nature of these materials in-situ, we adopt a rather unique approach founded on a synergistic combination of neutron or X-ray scattering measurements and theoretical data modeling. The scattering signals from these materials are rich in 3D structural information that can be elegantly extracted using a Dynamical Theory developed and refined by our group.