The overriding research interest of the lab is trying to disentangle the relationship between genotype and phenotype, and to understand the forces shaping functional genetic variation in humans. We approach this broad topic through a combination of experimental and computational approaches: We employ high-throughput functional genomics and genome-wide association analysis (GWAS) to identify variable regions of the genome with functional effects on aspects of the transcription profile (expression levels, splicing, etc.), and to investigate how these patterns are altered in different contexts. At the same time, we examine the levels and patterns of segregating nucleotide variation within human populationss (population genetics) and between human and other species (comparative genomics) to identify regions of the genome with patterns of variation suggestive of natural selection or selective constraint over time, as such patterns may be indicative of function.
The Antonopoulos Lab is presently focused on studying complex microbial communities in natural environments, including both topsoil and subsurface environments. Many of the same metagenomics-enabled approaches to studying these systems are being used by the lab group to study human gastrointestinal microbial communities in the context of several digestive diseases. This work is in collaboration with faculty in the Section of Gastroenterology at the University of Chicago including research as part of the NIH Human Microbiome Project.
Dr. Grossman research group focuses on bioinformatics, data mining, cloud computing, data intensive computing, and related areas. Current research projects include: Bionimbus (http://www.bionimbus.org), a cloud-based system for managing, analyzing and sharing genomic data and Sector/Sphere (sector.sourceforge.net), a cloud-based system for data intensive computing. He is also interested in developing new algorithms for the large scale analysis of genomic and phenotypic data.
The Jones lab is utilizing protein micro-array, mass spectrometric, and cell biological tools to query both the theoretical biophysical nature of protein-protein interaction connectivity as well as the dynamics of cellular protein abundance, post-translational modification, and interaction connectivity. We are focusing our efforts primarily on those interactions and modifications that would not be easily addressed using traditional yeast two hybrid methodologies. Our goal is to gain a better understanding of the modular signaling molecules whose location, abundance, and modification state underlie cell growth, migration, differentiation, and cell death: These processes lie at the heart of cancer biology and an understanding of these processes at the molecular level should enable the identification of many new therapeutic targets.
Dr. Rust laboratory combines optical microscopy of living cells and single molecules with biochemistry and mathematical modeling to understand the function of small networks of strongly interacting biological molecules. These projects are motivated by the belief that explaining the origin of systems properties such as robustness to perturbations and adaptation to changing input will require understanding the nature of the molecular events that comprise the interactions.
My main interest is in (asymptotic) understanding how phenotypes, such as human healthy diversity and maladies, are implemented at the level of genes and networks of interacting molecules. To harvest as much information about known molecular interactions as possible, my group runs a large-scale text-mining effort aiming at analysis of a vast corpus of biomedical publications. Currently we can extract from text automatically about 500 distinct flavors of relations among biomedical entities (such as bind, activate, merystilate, and transport). To sharpen our text-mining axes, we are actively designing related models and computational applications. Furthermore, in cooperation with our experimentally talented colleagues, we are striving to use text-mined networks to understand, interpret and refine high- or low-throughput experimental data. We are also computationally generating biological hypotheses that our generous collaborators are attempting to test experimentally. My older (still smoldering) passion is in developing and applying computational methods related to phylogenetics and evolutionary biology.
My laboratory uses a combination of genomics, computational, and genetic approaches to investigate large-scale networks of factors that control gene expression during development and disease. A major challenge in the “genomic era” of biology is to assemble the thousands of genes and proteins encoded within each genome into comprehensive subsets that specify particular developmental events or physiological processes. We are approaching this challenge using Drosophila melanogaster as a model and in the human genome directly.