Dr. Belger's research focuses on studies of the cortical circuits underlying attention and executive function in the human brain, as well as the breakdown in these functions in neuropsychiatric and neurodevelopment disorders such as schizophrenia and autism. Her research also examines changes in cortical circuits and their physiological properties in individuals at high risk for psychotic disorders. Dr. Belger combines functional magnetic resonance imaging, electrophysiological scalp recording, experimental psychology and neuropsychological assessment techniques to explore the behavioral and neurophysiological dimensions of higher order executive functions. Her most recent research projects have begun focusing on electrophysiological abnormalities in young autistic children and individuals at high risk for schizophrenia.
Molecular evolution and mechanistic enzymology find powerful synergy in our study of aminoacyl-tRNA synthetases, which translate the genetic code. Class I Tryptophanyl-tRNA Synthetase stores free energy as conformational strain imposed by long-range, interactions on the minimal catalytic domain (MCD) when it binds ATP. We study how this allostery works using X-ray crystallography, bioinformatics, molecular dynamics, enzyme kinetics, and thermodynamics. As coding sequences for class I and II MCDs have significant complementarity, we also pursuing their sense/antisense ancestry. Member of the Molecular & Cellular Biophysics Training Program.
We use the premier model plant species, Arabidopsis thaliana, and real world plant pathogens like the bacteria Pseudomonas syringae and the oomycete Hyaloperonospora parasitica to understand the molecular nature of the plant immune system, the diversity of pathogen virulence systems, and the evolutionary mechanisms that influence plant-pathogen interactions. All of our study organisms are sequenced, making the tools of genomics accessible.
Our lab tries to understand viral pathogenesis. To do so, we work with two very different viruses - West Nile Virus (WNV) and Kaposi¹s sarcoma-associated herpesvirus (KSHV/HHV-8).
The Dokholyan group focuses primarily on understanding protein dynamics, more specifically on how induced changes in protein folding and aggregation lead to diseases, such as cystic fibrosis, many types of cancers, and a number of neurodegenerative diseases. The Dokholyan group focuses on several such diseases, including Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig’s disease, and Huntington disease. The Dokholyan group is developing a hierarchy of molecular models, from simplified coarse-grained models to more detailed ones, to create a novel multi-scale simulation methodology. This methodology will enable simulations of large molecular complexes at the biologically-relevant time scales, thereby allowing to directly glance into processes associated with human diseases. Member of the Molecular & Cellular Biophysics Training Program.
We are interested in uncovering the fundamental systems-wide processes and mechanisms that underlie life, with a human-health focus. We apply a combination of both modern and traditional tools to this pursuit, including bioinformatics, proteomics, microarrays, molecular genetics, bench work, and software development. Current research areas we focus on include: 1) locating the molecular mechanisms that underlie antibiotic tolerance in the bacteria Pseudomonas aeruginosa, to address the threat that drug resistant organisms pose to those with COPD and Cystic Fibrosis; 2) annotation of the human genome with proteomic data, to determine which genes are translated and when, and how those correlate with prevalent diseases such as cancer; 3) development of computational agent-based models of intramolecular pathways and pathogen-host interactions in HIV, to determine how host-pathogen interactions relate to disease progression; 4) development of software tools for analysis of RNA structures, such as the viral HIV genome, to assist with determining how RNA structure impacts function; and 5) developing software for finding post-translational modifications (PTMs) on proteins by integrating proteomic data sets, to determine the role that these play on cellular signaling in healthy and diseased states. We have a wide diversity lab members, from microbiology bench scientists to computer scientists, and would be a great fit for a student looking for a broad, cross-disciplinary training environment focused on either microbiology and/or genomics.
Our primary research is in the area of computational systems biology, with particular interest in the study of biological signaling networks; trying to understand their structure, evolution and dynamics. In collaboration with wet lab experimentalists, we develop and apply computational models, including probabilistic graphical and multivariate methods along with more traditional engineering approaches such as system identification and control theory, to current challenges in molecular biology and medicine. Examples of recent research projects include: prediction of protein interaction networks, multivariate modeling of signal transduction networks, and development of methods for integrating large-scale genomic data sets.
bioinformatics, scholarly communications, digital libraries, user interface design, annotation, virtual environments, medical informatics, databases and datamining.
Spatio-temporal regulation of signal relay systems in cells using live cell fluorescence imaging and targeted gene disruption of signaling proteins to define their role in development, physiology and pathophysiology.
Signal transduction coupled by heterotrimeric G proteins. We use Arabidopsis, genetics, biochemistry, & in vivo imaging of protein-protein interactions. The type of signals we study include light, hormones, & sugars.
We use high-throughput DNA sequencing, microarrays, and other technologies to study how and where proteins interact with the genome, and how these interactions affect the biology of living cells. We use three systems: yeast, C. elegans and human. Our C. elegans studies focus on developmental processes, and we use human cell lines and clinical samples to study diseases like cancer and diabetes. All of our projects focus on chromatin and DNA-binding proteins.
My laboratory studies diffuse gliomas, devastating primary tumors of the central nervous system for which few effective drugs are currently available. We utilize model systems (genetically engineered mice, cultured cells, and human tumor specimens) to explore the molecular pathogenesis of
and develop drugs and diagnostic markers for individualized therapy of gliomas. Rotating students gain experience with techniques that include genomics (expression microarrays and array CGH), fluorescence microscopy, computer-enhanced image analysis, and tissue microarrays.
We are identifying genetic variants that influence common human traits with complex inheritance patterns, and we seek to understand the biological function of the identified variants. Currently we are investigating susceptibility to type 2 diabetes and obesity, as well as variation in cholesterol levels, blood pressure, body size, weight gain and early growth. In addition to examining the primary effects of genes, the lab is exploring the interaction of genes with environmental risk factors in disease pathogenesis. Approaches include genome-wide association studies, genetic epidemiology, resequencing, bioinformatic analysis, molecular biology, cell biology, and mouse models to compare high- and low-risk alleles in a whole-animal setting.
My research focuses on plant community ecology and such related fields as plant geography, conservation biology, ecoinformatics and plant population ecology. I am particularly interested in how plant communities are assembled and vary across landscapes. Toward this end I am helping define the emerging discipline of ecoinformatics through development of international databases and standards for large-scale data integration and exchange. My current research on the vegetation of the Southeastern United States includes on-going studies of the long-term dynamics of Southeastern forests, human impacts on floodplain ecosystems, targets for restoration, and more generally factors influencing the composition and species diversity of terrestrial plant communities across a range of spatial scales.
Human carcinomas show great diversity in their morphologies, clinical histories and in their responsiveness to therapy. This wide tumor diversity poses the main challenge to the effective treatment of cancer patients. The main focus of the Perou Lab is to characterize the biology diversity of human tumors using microarray analysis, genomics, molecular genetics, and cell biology, and then to mimic these findings in animal models. We ultimately use these animal systems to develop predictive computational models and to test new therapeutics that are specific for each tumor subtype.
High-performance computing: algorithms, programming languages, compilers and architectures. Scientific computing with focus on computational biology and bioinformatics. High-level programming languages and problem solving environments.
Bioinformatics, Cancer Biology, Cell Biology, Chemical Biology, Computational Biology, Genomics, Molecular Medicine, Neurobiology, Pharmacology, Systems Biology, Toxicology, Translational Medicine
Our laboratory applies molecular, biochemical, genetic and genomics approaches to understanding the mechanisms of environmental agent-related organ injury and carcinogenesis. Specifically, we are interested in nuclear receptor-mediated pathways in chemical carcinogenesis, oxidative DNA damage and repair, the role that alcohol and diet play in cancer, and the genetic determinants of the susceptibility to toxicant-induced liver injury. Through a combination of in vivo animal studies and experiments that utilize cellular and molecular models, we aim to better understand why certain chemicals cause cancer or organ damage in rodents and whether humans in general, or any susceptible sub-population in particular, are at risk from similar exposures.
I study complex traits using linkage, association, and genetic epidemiological approaches. Disorders include schizophrenia (etiology and pharmacogenetics), smoking behavior, and chronic fatigue.
The major area of our research is Biomolecular Informatics, which implies understanding relationships between molecular structures (organic or macromolecular) and their properties (activity or function). We are interested in building validated and predictive quantitative models that relate molecular structure and its biological function using statistical and machine learning approaches. We exploit these models to make verifiable predictions about putative function of untested molecules.
"Our lab uses computational and molecular tools to study the evolution of genome organization, primarily in the flowering plants. Areas of
investigation include the origin and consequences of differences in gene order within populations and between species, the evolutionary and functional diversification of gene families (phytome.org), and the application of genomics to evolutionary model organisms (mimulusevolution.org). We also are involved in a number of cyberinfrastructure initiatives through the National Evolutionary Synthesis Center (nescent.org), including work on digital scientific libraries(datadryad.org), open bioinformatic software development (e.g. gmod.org) and the application of semantic web technologies to biological data integration(phenoscape.org)."
The Wang group designs novel data models and algorithms to address fundamental computational issues in analyzing large sets of experimental data. Ongoing research projects include: 1) Classification and clustering analysis of gene-expression profiles, 2) Discovery of discriminative structural motifs in proteins and 3) Query and integration of heterogeneous databases.
The Wilhelmsen lab is engaged in the genetic mapping of susceptibility loci for complex neurological diseases and has been developing large-scale automated gene mapping technologies to facilitate these mapping efforts. They have invested heavily in automation that enables high-throughput genotyping and data processing. As data accumulates, this will enable parametric and nonparametric linkage analysis of large numbers of traits at regular intervals for the entire genome. The Wilhelmsen lab is applying these techniques to two projects: (1) the genetics of alcoholism and (2) positional cloning of the gene responsible for a family of disorders called frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17).
