NOV 12 – Caroline Wagner – MECH Research Seminar Series

Caroline Wagner is a postdoctoral researcher in the Ecology and Evolutionary Biology department at Princeton University. She completed her PhD in Mechanical Engineering at the Massachusetts Institute of Technology (MIT), combining experimentation and mathematical modeling to study how the fluid mechanical properties of biological gels could be interpreted as indicators of the underlying biopolymer microstructure. Dr. Wagner’s current work focuses on modeling the nonlinear dynamics of infectious diseases, both at the population level and at the level of the in-host dynamics and biological processes regulating parasite transmission and suppression by natural immunity or vaccines. Dr. Wagner completed her B. Eng. at McGill University and her MS at MIT, both in Mechanical Engineering.

Mathematical disease models: from mucus rheology to infectious disease dynamics and control

The cross-linked polymeric microstructures of biological hydrogels give rise to their mechanical properties, which in turn contribute to their proper biological function. Quantification and modeling of the mechanical properties of these materials can provide insight into their microstructures, which is particularly important when structural changes are associated with impaired biological function. In the first part of this presentation, we will discuss this relationship between microstructure and mechanical properties in the context of the biological hydrogel mucus. To do so, we explore the network structure and association dynamics of reconstituted mucin gels using micro- and macrorheology in order to gain insight into how environmental factors, including pathogens and therapeutic agents, alter the mechanical properties of fully-constituted mucus. We then apply these findings to interpret changes in the mechanical properties of cervical mucus and saliva as biomarkers for disease. In the second part of this presentation, I will present plans for my current and future research directions, which include leveraging data sets in order to explore the complex forcing functions of nonlinear epidemic dynamics across the molecular-to-individual and the individual-to-global length-scales.

See poster here