Our adaptive immune system is able to learn from past experiences to better fit an unforeseen future. This is made possible by a diverse and dynamic repertoire of cells expressing unique antigen receptors on their surface and capable of rapid evolution within an individual. However, naturally occurring immune responses exhibit limits in efficacy, speed and capacity to adapt to new challenges. In this talk, I will discuss theoretical frameworks we developed to (1) explore functional impacts of non-equilibrium antigen recognition, and (2) identify conditions under which natural selection acting local in time can find adaptable solutions favorable in the long run. Using these examples, I show that a generalized landscape theory provides a unifying framework for connecting physical mechanisms and evolved biological functions. In light of coevolution, I will discuss a broader scope of our work and its implications for vaccine strategies.
Shenshen Wang is an assistant professor in the Department of Physics and Astronomy at University of California, Los Angeles. Shenshen studied physics at Nanjing University and worked with Tai Kai Ng during her MPhil at HKUST. She transitioned to soft condensed matter and biophysics research at UCSD, where she earned a PhD in physics under the supervision of Peter Wolynes. Before joining UCLA, she acquired her postdoc training on computational immunology at MIT, in collaboration with Mehran Kardar and Arup Chakraborty. Her group uses theory and computation, grounded in statistical physics, information and evolution theory, to understand emergent behaviors in complex and living systems, with a recent focus on understanding how the immune system fights fast evolving pathogens to propose design principles for novel vaccine strategies.
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