B.A., Pomona College, Molecular Biology, Mathematics.
Ph.D., California Institute of Technology, Biology.
Biological systems consist of interacting components operating at different scales of time, space, and functional organization. A hallmark of biological systems is their adaptive nature: changes in properties of any component can result in altered system behavior, which in turn modifies the selective pressure on the components. We plan to quantitatively study evolving biological systems using a combination of experimental biology and mathematical analysis.
We are particularly interested in cooperative systems. Cooperation can be found virtually everywhere: between different cell types in our body, different individuals in an ant colony, and different species in a mutualistic interaction. The pervasiveness of cooperation is paradoxical because cooperative systems are threatened by "cheaters" that consume benefits without paying a fair cost. How can cooperative systems survive cheaters and evolve to their often sophisticated current forms?
Unfortunately, natural cooperative systems are often complicated and difficult to study. We have therefore constructed an engineered system consisting of two complementary types of yeast cells: the red-fluorescent cells require adenine to grow and release lysine and the yellow-fluorescent cells require lysine to grow and release adenine. Together, the two cell types form a cooperative system termed CoSMO (Cooperation that is Synthetic and Mutually Obligatory). Aspects of system behavior have been mathematically deduced from properties of the two cooperating cell types. Thus, CoSMO provides a model system for quantitatively linking component properties to system behavior and for addressing questions on the evolutionary trajectories, the cheater tolerance, and the dynamic and stability properties of cooperation.
Our scientific interests are broad. We like to think about eccentric phenomena, problems that are often talked about but rarely studied, and questions that can be best addressed through a combination of experiments and calculations. In essence, we want to learn, to discover, and to have fun.
Engineering microbial consortia for controllable outputs.. The ISME journal. 10(9):2077-2084.. 2016.
Challenges in microbial ecology: building predictive understanding of community function and dynamics.. The ISME journal. 10(11):2557-2568.. 2016.
Defectors Can Create Conditions That Rescue Cooperation.. PLoS computational biology. 11(12):e1004645.. 2015.
Theory, models and biology.. eLife. 4. 2015.
Constructing synthetic microbial communities to explore the ecology and evolution of symbiosis.. Methods in molecular biology (Clifton, N.J.). 1151:27-38.. 2014.
Modeling community population dynamics with the open-source language R.. Methods in molecular biology (Clifton, N.J.). 1151:209-31.. 2014.
Spatial self-organization favors heterotypic cooperation over cheating.. eLife. 2:e00960.. 2013.
Adaptation to a new environment allows cooperators to purge cheaters stochastically.. Proceedings of the National Academy of Sciences of the United States of America. 109(47):19079-86.. 2012.
Cryosectioning yeast communities for examining fluorescence patterns.. Journal of visualized experiments : JoVE. (70). 2012.
Using artificial systems to explore the ecology and evolution of symbioses.. Cellular and molecular life sciences : CMLS. 68(8):1353-68.. 2011.