- we have an open postdoc position of at least two years duration with solid pay and great benefits
- the project is to learn about anti-HIV antibody development and coevolution using data coming from a unique HIV superinfection cohort for which the viral history has been carefully characterized
- there will be many opportunities to develop and deploy new methods in a team pushing the boundaries of Bayesian phylogenetic analysis
- we will work with collaborators to validate inferences using lab techniques
- the larger research group is highly collaborative, with deep expertise in virology, protein evolution, and structural/functional analysis
- we’re a Python shop looking for someone who isn’t afraid of large-scale data analysis and has high standards for organization and code clarity
This is a unique time, full of opportunity, to be working on B cell receptor (antibody) sequences. The field is awash with data, and methods haven’t really caught up: there is still lots to do to develop methods that fully make use of the data but also scale to large data sets.
The Fred Hutch is also a unique place to be doing this work, with a convergence of many very strong research programs. This project is a collaborative endeavor between
Julie Overbaugh’s lab brings decades of experience analyzing HIV infection and the corresponding immune response.
Jesse Bloom’s lab does groundbreaking work understanding protein evolution at the pathogen-immune interface; they recently did deep mutational scanning on HIV env, influenza in the presence of antibody selection and are gearing up to do more in HIV, including DMS on anti-HIV antibodies.
Kelly Lee’s lab brings a full stack of cutting-edge technology to study virus/immune reactions from a structural perspective.
My group works to develop advanced Bayesian phylogenetic techniques and tools to understand antibody sequences.
(In passing, I also note that the HIV Vaccine Trials Network has its primary leadership here, and they are very interested in using B cell sequencing to understand vaccine response.)
This project is to analyze the antibody immune response in Dr. Overbaugh’s priceless samples from a cohort of Kenyan sex workers in the era before widely-available antiretrovirals. The general goal is to understand the broad and potent antibody responses raised by these women. Our part of that goal will be to perform sequence analysis to understand the events leading to, selective pressures on, and co-evolution of HIV-responsive lineages. This will include close inspection of individual data sets as well as methods development to characterize the antibody response in more detail.
This project will entail be a convergence of the two primary themes in my group: Bayesian phylogenetics and B cell receptor sequence analysis. Our recent work has convinced us that Bayesian methods are needed for antibody ancestral sequence reconstruction, and we’re going all-in. There special challenges, such as context sensitive mutation and strong natural selection, and also special opportunities.
One of the special opportunities that this work provides is that these inferences can be validated by lab work.
The Overbaugh lab is expert at expressing antibodies and testing their properties, in particular against viruses isolated from these same individuals. This will create a beautiful dynamic feedback loop that we can use to learn about coevolution.
In case you aren’t already stoked, here’s an image from Liao et al 2013 showing the epic evolutionary battle between HIV (top) and antibodies (bottom):
This is a great opportunity for people with a few different backgrounds, so drop us a line even if you don’t already know how to approach all aspects of this work. In particular, this would be a good fit for folks
- from evolutionary biology who are interested in a field awash with data with real biomedical consequences (and job opportunities!)
- from immunology who want to be immersed in a group with tons of computational evolutionary biology experience and ambition to advance Bayesian methods
- from other fields who are just really good at getting computers to process a lot of data with complex models.
The formal requirements are:
- an open and curious mind, ready to learn what’s needed
- desire to advance understanding of a biological problem
- serious Python programming chops
- expertise in large-scale computing pipelines with a passion for reproducibility (this certainly includes Git and Linux-fu)
- a team spirit, ready to work with diverse group
Ideally we’d recruit someone with
- understanding of evolutionary biology, including coevolution
- knowledge of adaptive immunology
- expertise in Bayesian statistics
- C++ experience
Fred Hutchinson Cancer Research Center, home of about 190 faculty including three Nobel laureates, is an independent, nonprofit research institution dedicated to the development and advancement of biomedical research. The environment is lively yet casual, with a strong emphasis on collaborative work.
The center is housed in a lovely campus next to Lake Union within walking distance from downtown.
How to apply
Please email Erick:
- a substantial code sample
- two representative publications or preprints
- a several paragraph statement of research interests
- names and email addresses of three references