Stephen Smith is an Assistant Professor in the Department of Ecology and Evolutionary Biology. The Smith lab group is primarily interested in examining evolutionary processes using new data sources and analysis techniques. They develop methods to address questions about the rates and modes of evolution using the large data sources (e.g., genomes and transcriptomes) that have become more common in the biological disciplines over the last ten years. In particular, they use DNA sequence data to construct phylogenetic trees and conduct analyses about processes that shape the evolution of lineages and their genomes using these trees. In addition to this research program, they also address how new data sources can facilitate new research in evolutionary biology. To this end, they sequence transcriptomes, primarily in plants, with the goal of better understanding where, within the genome and within the phylogeny, processes like gene duplication and loss, horizontal gene transfer, and increased rates of molecular evolution occur.
Aaron A. King is an Associate Professor of Ecology & Evolutionary Biology, and is affiliated with the Department of Mathematics, the Center for the Study of Complex Systems, the Center for Computational Medicine & Bioinformatics, the Fogarty International Center, and the National Institutes of Health. Prof. King develops and applies computationally intensive methods for using stochastic dynamical systems models to learn about infectious disease ecology and epidemiology. These systems are typically highly noisy and nonlinear and are frequently uncomfortably high-dimensional. Nevertheless, the King group’s approaches allow them to find out what the data have to say about the mechanisms that generate them.
Annette Ostling is an Associate Professor in Ecology and Evolutionary Biology. Her lab focuses on the mechanisms by which competing species coexist, and how those mechanisms influence the structural patterns of ecological communities, i.e. the presence and relative abundance of species, and their distribution on axes of trait variation. A key approach the lab takes is the simulation of stochastic community assembly models, which enable examination of the influence of immigration and demographic stochasticity in combination with mechanisms of niche differentiation. Her group also uses computational approaches to study the evolution of species interactions, especially predation and host-pathogen interactions, in a spatial context.
Microbial communities host incredible biological diversity that is encoded at the genomic level. The recent application of high-throughput DNA-sequencing technologies to these communities provides exciting new insights into uncultured organisms while presenting new computational challenges associated with massive and multidimensional data. Professor Dick’s laboratory utilizes such metagenomic and metatranscriptomic approaches to problems in environmental and geological science, addressing questions such as: How do microorganisms control elemental cycles in deep-sea hydrothermal systems? How did microbial processes influence the timing of Earth’s oxygenation? Computational applications focus on metagenomic and metatranscriptomic assembly, comparative genomics, and “binning,” whereby fragmentary metagenomic sequences of unknown origin are assigned to organisms on the basis of genome signatures of nucleotide composition.