We currently have projects in the following research areas:
If you're interested in learning more, contact the appropriate researcher.
We also have a weekly group meeting; schedule can be found here.
Theory of evolutionary dynamics
What are the relevant processes in the dynamics of evolution? What are their timescales, and how do they relate to the microscopic parameters in simple models? We study a variety of basic theoretical models of evolution, using analytical arguments augmented with computational exploration to develop intuition for the statistical dynamics of evolution.
Emphasis is placed on gaining an understanding which transcends the specifics of a model, and ideally helps guide the development of which observables are important for understanding real populations as well. Our use of asymptotics takes advantage of the area where humans still have an advantage over computer models and simulation: extrapolation over a wide range of parameter space.
Recent and current projects include:
- Two chromosome model, intermediate recombination
- Epistasis on high dimensional random landscapes
Microbial experimental evolution
The evolution of large asexual cell populations underlies 30% of deaths worldwide, including those caused by bacteria, fungi, parasites, and cancer. However, the dynamics underlying these evolutionary processes were poorly understood because they typically involve many competing beneficial lineages. We helped to develop a sequencing based ultra high resolution lineage tracking system and associated quantitative models and analysis tools to track >500,000 cell lineages simultaneously.
The central goal of this (ongoing) research drive is to develop a predictive understanding of how rapidly cell populations adapt, and specifically what factors matter most (e.g. population size, mutation rate, growth rate, drug concentration, recombination rate etc). Understanding how these parameters affect the speed of evolution could lead to improved treatments for evolutionary diseases such as bacterial infections, cancer and HIV.
In order to navigate, animals need to maintain a representation of their environment as well as their position within it. How are these representations learned and encoded, and how do they interact with each other? Relatively recently, “grid neurons”, with a characteristic hexagonal firing pattern, have been discovered in mammals. In collaboration with the Ganguli and Giocomo labs, we work on a combination of theory and data analysis to understand how an animal navigates and learns about its environment from the combination of step-counting and landmarks.