The influenza virus has a high evolution rate, which makes designing the annual flu vaccine challenging. A mismatch between the strain in the vaccine and the strain infecting the public leads to a less effective vaccine and to broader infection in the population. A precise measure of how differently the immune system perceives the vaccine and virus enables a better design of the flu shot. I will discuss a method to predict vaccine efficacy that we have developed, which is at least as predictive as, and sometimes more so than, animal model studies. Interestingly, the immune system typically recognizes the H1N1 strain of the flu to a greater degree than it does the H3N2 strain, leading to better flu shots for H1N1 than H3N2. The evolution rate of H1N1 is also greater than that of H3N2, presumably due to greater pressure on the virus to evolve. I will also discuss a technique we have developed for early detection of new flu strains. I will show that this method is able to detect new versions of the flu earlier than the present approaches used by health authorities. Finally, I will discuss evolution with the immune system of bacteria, CRISPR.
[b]More bio[/b]
Michael Deem is a fellow of the American Institute for Medical and Biological Engineering, the American Physical Society, the Biomedical Engineering Society, and the AAAS. His honors include a Sloan Foundation fellowship; the Camille Dreyfus Teacher-Scholar Award in 2002; a National Science Foundation CAREER Award; the Colburn Award for excellence in publications as well as the Professional Progress Award from the American Institute of Chemical Engineers; and the O'Donnell Award from the Academy of Medicine, Engineering & Science of Texas. He gave the Vaughan Lectures at Caltech in 2007 and was chosen one of MIT's [i]Technology Review[/i] 1999 Young Innovators. He is an associate editor of [i]Physical Biology and Protein Engineering Design & Selection[/i].