Natural Immunity to HIV: From Statistical Mechanics to Clinical Data

McDonnell Douglas Engineering Auditorium

ChEMS Seminar


Dr. Elizabeth Read

Postdoctoral Fellow

Massachusetts Institute of Technology


Without therapy, most people infected by the HIV virus ultimately progress to AIDS. However, rare individuals called “elite controllers” exhibit natural immunity to the disease, providing tantalizing evidence that a protective vaccine is feasible, despite the virus’ rapid mutation rate. Genetic correlates of elite control are known, but the underlying molecular mechanisms have remained unclear.

Multiscale computational modeling can provide mechanistic insight into complex human diseases like HIV by integrating diverse data. I will present work that merges methods from statistical mechanics and chemical kinetics with clinical data, addressing two questions: How do protective genes promote elite control, and why is the phenomenon so rare even in individuals with these protective genes?

In the latter part of the talk, I will discuss results suggesting that HIV escape from immune pressure is a type of stochastic barrier-crossing event, leading to wide person-to-person variability in disease outcomes. This work indicates that biological noise can play a decisive role even at the scale of the immune system, and that recent progress in modeling switching events in noisy biochemical networks can pave the way for understanding critical processes in the development of human disease.




Elizabeth Read is a Jane Coffin Childs postdoctoral fellow at the Massachusetts Institute of Technology working with Professor Arup Chakraborty and the Ragon Institute for HIV Research. After her undergraduate studies in chemistry and mathematics at the University of Colorado, she earned a Ph.D. at UC Berkeley in physical chemistry. Her graduate research applied optical spectroscopy and theoretical modeling to study light harvesting in photosynthesis, advised by Professor Graham Fleming. This work inspired her interest in using theoretical and computational methods to study dynamics of complex biological systems.