EECS500 Fall 2012 Department Seminar

Jonathan Carlson
Modeling HIV adaptation: Insights into HIV virology, immunology and vaccine design from machine learning and computational biology.
Microsoft Research
White Bldg., Room 411
11:30am - 12:30pm
October 16, 2012

The human immunodeficiency virus (HIV-1) mutates at a startling rate,
with millions of viral variants generated in each patient each day. This
high rate of mutation, coupled with high mutational tolerance, provides
the virus with the ability to rapidly adapt to changing environments and
typically proves an insurmountable challenge to the human immune system.
Viral mutation is not, however, without constraints: given large enough
datasets, patterns begin to emerge. By studying these patterns, we have
gained significant new insights into what is attacking the virus
(immunology), what is being attacked (virology), how that attack is
evaded (evolution), and how adaptation influences disease progression
(pathology). In addition, we have begun to identify features of
individuals who naturally control the virus, offering tantalizing hints
at how an effective vaccine might work. In this talk, I will describe
the statistical models we have developed for studying HIV adaptation,
the insights these models have provided and the open questions we
continue to pursue.


Jonathan Carlson, Ph.D., joined the Escience Group at Microsoft Research
in 2008, where he studies viral evolution, immunology and vaccine design
through statistical modeling. His models of viral escape have achieved
broad recognition in the HIV community, where they have led to the
discovery of novel viral-host interactions, insights into mechanisms of
natural immune control, and the identification of vaccine candidates
that are slated for clinical trials. He has authored over 50 papers in
the field and has served on advisory panels and committees for the
Institutes of Medicine, the Gates Foundation and the Center for HIV/AIDS
Vaccine Immunology (CHAVI). In 2009, he received his Ph.D. in computer
science and computational molecular biology from the University of
Washington, where he studied under David Heckerman (Microsoft Research)
and Larry Ruzzo (UW) and was given the university's Distinguished
Dissertation Award. He received his B.A. in Biology and Computer Science
from Dartmouth in 2003, where he studied bioinformatics and
transcriptional regulation under Bob Gross.