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A growing body of work in the biomedical sciences generates and analyzes omics data; our lab’s work contributes to these efforts by focusing on the integration of different omics data types to bring mechanistic insights to the multi-scale nature of cellular processes. The focus of our research is on developing systems genomics approaches to study the impact of genomic variation on genome function. We have used this focus to study genetic and molecular variation in both natural and engineered cellular systems and approach these topics through the lens of computational biology, machine learning and advanced omics data integration. More specifically, we create methods to reveal functional relationships across genomics, transcriptomics, ribosome profiling, proteomics, structural genomics, metabolomics and phenotype variability data. Our integrative omics methods improve understanding of how cells achieve regulation at multiple scales of complexity and link to genetic and molecular variants that influence these processes. Ultimately, the goal of our research is advancing the analysis of high-throughput omics technologies to empower patient care and clinical trial selections. To this end, we are developing integrative methods to improve mutation panels by selecting more informative genetic and molecular biomarkers that match disease relevance.


Experimental Evolution of Viruses. We use both computational and experimental approaches to understand how viruses adapt to their host environment. Our research attempts to determine how genome complexity constrains adaptation, and how virus ecology and genetics interact to determine whether a virus will shift to utilizing new host. In addition, we are trying to develop a framework for predicting which virus genes will contribute to adaptation in particular ecological scenarios such as frequent co-infection of hosts by multiple virus strains. For more information, and for advice on applying to graduate school at UNC, check out my lab website www.unc.edu/~cburch/lab.


Our lab is trying to understand the mechanisms by which long noncoding RNAs orchestrate the epigenetic control of gene expression. Relevant examples of this type of gene regulation occur in the case of X-chromosome inactivation and autosomal imprinting. We specialize in genomics, but rely a combination of techniques —  including genetics, proteomics, and molecular, cell and computational biology — to study these processes in both mouse and human stem and somatic cell systems.


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.


Developing and applying novel mass spectrometry (MS)-based proteomics methodologies for high throughput identification, quantification, and characterization of the pathologically relevant changes in protein expression, post-translational modifications (PTMs), and protein-protein interactions. Focuses in the lab include: 1) technology development for comprehensive and quantitative proteomic analysis, 2) investigation of systems regulation in toll-like receptor-mediated pathogenesis and 3) proteomic-based mechanistic investigation of stress-induced cellular responses/effects in cancer pathogenesis.


The Dominguez lab studies how gene expression is controlled by proteins that bind RNA. RNA binding proteins control the way RNAs are transcribed, spliced, polyadenylated, exported, degraded, and translated. Areas of research include: (1) Altered RNA-protein interactions in cancer; (2) RNA binding by noncanonical domains; and (3) Cell signaling and RNA processing.


My lab studies how genes function within the three-dimensional context of the nucleus to control development and prevent disease. We combine genomic approaches (ChIP-Seq, ChIA-PET) and genome editing tools (CRISPR) to study the epigenetic mechanisms by which transcriptional regulatory elements control gene expression in embryonic stem cells.  Our current research efforts are divided into 3 areas: 1) Mapping the folding pattern of the genome 2) Dynamics of three-dimensional genome organization as cells differentiate and 3) Functional analysis of altered chromosome structure in cancer and other diseases.


Appropriate allocation of cellular lipid stores is paramount to maintaining organismal energy homeostasis. Dysregulation of these pathways can manifest in human metabolic syndromes, including cardiovascular disease, obesity, diabetes, and cancer. The goal of my lab is to elucidate the molecular mechanisms that govern the storage, metabolism, and intercellular transport of lipids; as well as understand how these circuits interface with other cellular homeostatic pathways (e.g., growth and aging). We utilize C. elegans as a model system to interrogate these evolutionarily conserved pathways, combining genetic approaches (forward and reverse genetic screens, CRISPR) with genomic methodologies (ChIP-Seq, mRNA-Seq, DNA-Seq) to identify new components and mechanisms of metabolic regulation.


The Elston lab is interested in understanding the dynamics of complex biological systems, and developing reliable mathematical models that capture the essential components of these systems. The projects in the lab encompass a wide variety of biological phenomena including signaling through MAPK pathways, noise in gene regulatory networks, airway surface volume regulation, and understanding energy transduction in motor proteins. A major focus of our research is understanding the role of molecular level noise in cellular and molecular processes. We have developed the software tool BioNetS to accurately and efficiently simulate stochastic models of biochemical networks


The Reproductive Endocrinology Group in the National Toxicology Program (NTP) Labs, led by Dr. Fenton, focuses on the role of environmental chemicals in breast developmental timing as it relates to puberty, increased susceptibility to form breast tumors, altered lactational ability, and the effects of chemicals on independent breast cancer risk factors such as obesity, breast density and pubertal timing. The projects within the lab often take a systems biology approach to the problem and instead of delving into exact mechanisms of an insult, which is in line with the missions of the NTP. The group also provides expertise in the use of whole mount mammary gland preparations in evaluating early life development of both male and female rat offspring and lifelong effects in female mice.


In the Ferris lab, we use genetically diverse mouse strains to better understand the role of genetic variation in immune responses to a variety of insults. We then study these variants mechanistically. We also develop genetic and genomic datasets and resources to better identify genetic features associated with these immunological differences.


Research interests include: transport processes in the lung, flow and structure of nano-materials & macromolecular fluids, weakly compressible transport phenomena, solitons and optical fiber applications, inverse problems for material characterization and modeling of transport in multiphase porous media.


My lab has a long-standing interest in gene regulation, epigenetics, chromatin and RNA biology, especially as it pertains to cancer. We are interested in studying the formation and function of transcriptional enhancers and the non-coding RNAs that are actively produced at enhancers, known as enhancer RNAs, which are involved in modulating several aspects of gene regulation. In addition, we aim to understand how transcriptional enhancers help orchestrate responses to external stimuli found in the tumor microenvironment. We address these research aims by using an interdisciplinary approach that combines molecular and cellular techniques with powerful genomic and computational approaches.


Our goal is to revolutionize the treatment of psychiatric and neurological illness by developing novel brain stimulation paradigms. We identify and target network dynamics of physiological and pathological brain function. We combine computational modeling, optogenetics, in vitro and in vivo electrophysiology in animal models and humans, control engineering, and clinical trials. We strive to make our laboratory a productive, collaborative, and happy workplace.


The Furey Lab is interested in understanding gene regulation processes in specific cell types, especially with respect to complex phenotypes, and the effect of genetic and environmental variation on gene regulation. We have explored these computationally by concentrating on the analysis of genome-wide open chromatin data generated from high-throughput sequencing experiments; and the development of statistical methods and computational tools to investigate underlying genetic and biological mechanisms of complex phenotypes. Our current projects include determining the molecular effects of exposure to ozone on chromatin, gene regulation, and gene expression in alveolar (lung) macrophages of genetically diverse mouse strains. We are also exploring genetics, chromatin, transcriptional, and microbial changes in inflammatory bowel diseases to identify biomarkers of disease onset, severity, and progression.


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.


My group develops and deploys computational tools to predict physiological function and dysfunction. We are interested in a range of applications in medicine and biology, but our primary focus is the cardiovascular system. My group is actively developing fluid-structure interaction (FSI) models of the heart, arteries, and veins, and of cardiovascular medical devices, including bioprosthetic heart valves, ventricular assist devices, and inferior vena cava filters. We are also validating these models using in vitro and in vivo approaches. We also model cardiac electrophysiology and electro-mechanical coupling, with a focus on atrial fibrillation (AF), and aim to develop mechanistically detailed descriptions of thrombosis in AF. This work is carried out in collaboration with clinicians, engineers, computer and computational scientists, and mathematical scientists in academia, industry, and regulatory agencies.


Dynamic control of signaling networks in living cells; Rho family and MAPK networks in motility and network plasticity; new tools to study protein activity in living cells (i.e., biosensors, protein photomanipulation, microscopy). Member of the Molecular & Cellular Biophysics Training Program and the Medicinal Chemistry Program.


Hedrick, Tyson
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Biology

Research in my laboratory focuses on how animals produce and control movement, with a particular interest in animal flight.  We use both computational and experimental techniques to examine how organismal components such as the neuromuscular and neurosensory systems interact with the external environment via mechanics and aerodynamics to produce movement that is both accurate and robust.  Keywords: biomechanics, flight, avian, insect, neural control, muscle, locomotion, computational modeling.


My research interest is in genomic characterization and integrative genomic approaches to better understand cancer. My group is part of the NCI Genome Data Analysis Center focused on RNA expression analysis. We have a number of ongoing projects including developing molecular classifications for potential clinical utility, developing methods for deconvolution to understand bulk tissue heterogeneity, analysis of driver negative cancers, and analysis of ancestry markers with cancer features.


Dr Jiang’s primary research interests lie in statistical modeling, method development and data analysis in genetics and genomics. His current research is focused on developing statistical methods and computational algorithms to better utilize and analyze different types of next-generation sequencing data under various setting, with application to data from large-scale cohort studies of human health and disease. Special focus is on single-cell transcriptomics, single-cell epigenomics, cancer genomics, tumor phylogeny, data normalization, and copy number variation detection.


The goal of my research is to identify, clone, and characterize the evolution of genes underlying natural adaptations in order to determine the types of genes involved, how many and what types of genetic changes occurred, and the evolutionary history of these changes. Specific areas of research include: 1) Genetic analyses of adaptations and interspecific differences in Drosophila, 2) Molecular evolution and population genetics of new genes and 3) Evolutionary analysis of QTL and genomic data.


We focus on a variety of design goals including the creation of novel protein-protein interactions, protein structures, vaccine antigens and light activatable protein switches. Central to all of our projects is the Rosetta program for protein modeling. In collaboration with developers from a variety of universities, we are continually adding new features to Rosetta as well as testing it on new problems.


We study protein structure and dynamics as they relate to protein function and energetics. We are currently using NMR spectroscopy (e.g. spin relaxation), computation, and a variety of other biophysical techniques to gain a deeper understanding of proteins at atomic level resolution.  Of specific interest is the general phenomenon of long-range communication within protein structures, such as observed in allostery and conformational change.  A. Lee is a member of the Molecular & Cellular Biophysics Training Program.


Life is animate and three-dimensional.  Our lab develops tools to better understand living specimens at single molecule, cellular, and tissue level length scales.  Our current efforts comprise three synergistic research areas: 1) development and application of novel fluorescent imaging modalities including: super resolution, light sheet, and adaptive optical microscopy 2) investigation of how mechanical forces and cytoskeletal dynamics drive cancer cell migration through complex three-dimensional environments, and 3) generation of microfabricated platforms to precisely control the cellular microenvironment for tissue engineering and drug screening.


The Yun Li group develops statistical methods and computational tools for modern genetic, genomic, and epigenomic data. We do both method development and real data applications. The actual projects in the lab vary from year to year because I am motivated by real data problems, and genomics is arguably (few people argue with me though) THE most fascinating field with new types and huge amount of data generated at a pace more than what we can currently deal with. For current projects, please see: https://yunliweb.its.unc.edu/JobPostings.html


Trauma and stress are common in life. While most individuals recover following trauma/stress exposure, a substantial subset will go on to develop adverse neuropsychiatric outcomes such as chronic pain, posttraumatic stress disorder (PTSD), depression, and postconcussive symptoms. Our research is focused on understanding individual vulnerability to such outcomes and to identify novel biomarkers and targets for therapeutic intervention. We use translational research approaches, including bioinformatics analysis of large prospective human cohort data, animal model research, and systems and molecular biology to better understand pathogenic mechanisms. We are particularly interested in the genetic and psychiatric/social factors influencing adverse outcome development, as well as biological sex differences that contribute to higher rates of these outcomes in women vs men.


Statistical machine learning and data mining, nonparametric statistics and functional estimation, bioinformatics, design and analysis of experiments

 


The Love Lab uses statistical models to infer biologically meaningful patterns in high-dimensional datasets, and develops open-source statistical software for the Bioconductor Project. At UNC-Chapel Hill, we often collaborate with groups in the Genetics Department and the Lineberger Comprehensive Cancer Center, studying how genetic variants relevant to diseases are associated with changes in molecular and cellular phenotypes.


Our fundamental interest is in how the nervous system processes sensory information. We have been studying these problems using in vitro preparations that allow us to examine how single cells in the auditory cortex and auditory brainstem operate to integrate synaptic input, generate precisely timed action potentials, and adapt to changes in sensory input produced by hearing loss.  This has involved investigations into the kinds of ion channels expressed in particular subsets of cells, determination of the kinetics and voltage dependence of those channels, studies of synaptic transmission, and the generation of computational models that reflect our current understanding of how these cells operate and produce responses to acoustic stimuli.  A longstanding interest has been in the types of processing that take place in the elaborate network of cells in cerebral cortex. The structure and function of neurons in the auditory cortex depends extensively on sensory experience. We are now studying the functional spatial organization of auditory cortical neural networks at the level of connections between classes individual cells, using optical methods in normal mice and mice with noise-induced hearing loss.


My research program studies how species form. We use a combination of approaches that range from field biology, behavior, and computational biology.


The overall focus of our lab is to develop new and exciting approaches for enhancing the efficacy of cancer immunotherapies. We utilize cutting-edge techniques to identify transcriptional and epigenetic regulators controlling T cell differentiation and function in the tumor microenvironment, and we seek to leverage this insight to reprogram or tailor the activity of T cells in cancer. Our group is also interested in understanding how to harness or manipulate T cell function to improve vaccines and immunotherapies for acute and chronic infections.


We are interested in the physics of soft and squishy materials, especially the organization and mechanics of living cellular materials. We use theory and simulation in close collaboration with experiments to understand the complex structural and mechanical behavior of these systems. These questions and our approach to them are interdisciplinary and intersect several traditional fields, including cell biology, biophysics, fluid dynamics and applied mathematics.


Nylander-French, Leena
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PHD PROGRAM
Toxicology

My research focuses on understanding the relationship between dermal and inhalation exposure and the effect of individual genetic differences on the function of enzymes that detoxify hazardous agents and that affect the development of disease. My research group has pioneered approaches to quantitatively measure skin and inhalation exposures to toxicants; additionally, my group has developed sophisticated exposure modeling tools using mathematical and statistical principles in an effort to standardize and improve exposure and risk assessment.


The Palmer lab investigates combination cancer therapy: understanding the mechanisms of successful drug combinations to inform the development of combinations with new cancer therapies. Our approach is a synthesis of experiments, analysis of clinical data, and modeling. Students can pursue projects that are experimental, computational, or a mixture of both. Our goals are to improve the design of drug combinations, the interpretation of clinical trials, and patient stratification to increase rates of response and cure through more precise use of cancer medicines in combinations.


Non-Mendelian genetics including, meiotic drive, parent-of-orifin effects and allelic exclusion.


My graduate students and I use the formalism of equilibrium thermodynamics and the tools of molecular biology and biophysics to understand how nature designs proteins.


We study the behavior of individual cells with a specific focus on “irreversible” cell fate decisions such as apoptosis, senescence, and differentiation. Why do genetically identical cells choose different fates? How much are these decisions controlled by the cell itself and how much is influenced by its environment? We address these questions using a variety of experimental and computational approaches including time-lapse microscopy, single-molecule imaging, computational modeling, and machine learning. Our ultimate goal is to not only understand how cells make decisions under physiological conditions—but to discover how to manipulate these decisions to treat disease.


We are interested in the links between epigenetics and gene regulation. Our primary focus is on understanding how changes to the composition of chromatin remodeling complexes are regulated, how their disruption affects their function, and contributes to disease. We focus on the SWI/SNF complex, which is mutated in 20% of all human tumors. This complex contains many variable subunits that can be assembled in combination to yield thousands of biochemically distinct complexes. We use a variety of computational and wet-lab techniques in cell culture and animal models to address these questions.


Heart failure is an increasingly prevalent cause of death world-wide, but the genetic and epigenetic underpinnings of this disease remain poorly understood. Our laboratory is interested in combining in vitro, in vivo and computational techniques to identify novel markers and predictors of a failing heart. In particular, we leverage mouse populations to perform systems-level analyses with a focus on co-expression network modeling and DNA methylation, following up in primary cell culture and CRISPR-engineered mouse lines to validate our candidate genes and identify potential molecular mechanisms of disease progression and amelioration.


We are interested in unraveling the molecular basis for human disease and discover new treatments focused on human and microbial targets. Our work extends from atomic-level studies using structural biology, through chemical biology efforts to identify new drugs, and into cellular, animal and clinical investigations. While we are currently focused on the gut microbiome, past work has examined how drugs are detected and degraded in humans, proteins designed to protect soldiers from chemical weapons, how antibiotic resistance spreads, and novel approaches to treat bacterial infections. The Redinbo Laboratory actively works to increase equity and inclusion in our lab, in science, and in the world. Our lab is centered around collaboration, open communication, and trust. We welcome and support anyone regardless of race, disability, gender identification, sexual orientation, age, financial background, or religion. We aim to: 1) Provide an inclusive, equitable, and encouraging work environment 2) Actively broaden representation in STEM to correct historical opportunity imbalances 3) Respect and support each individual’s needs, decisions, and career goals 4) Celebrate our differences and use them to discover new ways of thinking and to better our science and our community


I work on predicting the determinants of adaptive immune responses. Most of my work has focused on T-cell epitope prediction for mutant antigens derived from cancer. I have collaborated closely with clinical groups to translate this work in personalized cancer vaccine trials. More recently I have also been working on joint T-cell and B-cell prediction for viral pathogens. The technologies and techniques applied across all of my projects are at the intersection of computational immunology, genomics, and machine learning.


The Schrider Lab develops and applies computational tools to use population genetic datasets to make inferences about evolutionary history. Our research areas include but are not limited to: characterizing the effects natural selection on genetic variation within species, identifying genes responsible for recent adaptation, detecting genomic copy number variants and other weird types of mutations, and adapting machine learning tools for application to questions in population genetics and evolution. Study organisms include humans, the fruit fly Drosophila melanogaster and its relatives, and the malaria vector mosquito Anopheles gambiae.


My primary research area is computational geometry, in which one studies the design and analysis of algorithms for geometric computation. Computational geometry finds application in problems from solid modeling, CAD/CAM, computer graphics, molecular biology, data structuring, and robotics, as well as problems from discrete geometry and topology.  Most of my work involves identifying, representing, and exploiting geometric and topological information that permit efficient computation.  My current focus is on applications of computational geometry in Molecular Biology and Geographic Information Systems (GIS). Examples of the former include docking and folding problems, and scoring protein structures using Delaunay tetrahedralization.


We are a lab exploring how variations in the genome change the structure and development of the brain, and in doing so, create risk for neuropsychiatric illness. We study genetic effects on multiple aspects of the human brain, from macroscale phenotypes like gross human brain structure measured with MRI to molecular phenotypes like gene expression and chromatin accessibility measured with genome-sequencing technologies. We also use neural progenitor cells as a modifiable and high fidelity model system to understand how disease-associated variants affect brain development.


Superfine’s group studies stimulus-responsive active and living materials from the scale of individual molecules to physiological tissues, including DNA, cells and microfluidic-based tissue models. We develop new techniques using advanced optical, scanning probe, and magnetic force microscopy. We pursue diverse physiological phenomena from cancer to immunology to mucus clearance in the lung. Our work includes developing systems that mimic biology, most recently in the form of engineered cilia arrays that mimic lung tissue while providing unique solutions in biomedical devices.


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.


The Vincent laboratory focuses on immunogenomics and systems approaches to understanding tumor immunobiology, with the goal of developing clinically relevant insights and new cancer immunotherapies.  Our mission is to make discoveries that help cancer patients live longer and better lives, focusing on research areas where we feel our work will lead to cures. Our core values are scientific integrity, continual growth, communication, resource stewardship, and mutual respect.


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).


Our research focuses on long-read (single-molecule) sequencing and informatics. We develop novel methods to enable more efficient *omic analysis and apply carefully architected high-performance computing approaches to improve the utility of genomics in studies of human diseases, including infectious disease, cancer, and GI. Ongoing work includes genomic epidemiology of SARS-CoV-2, MPXV, and antibiotic resistance; classification of pediatric leukemias and solid tumors in low-resource settings using nanopore transcriptome sequencing; and metagenomics/metataxonomics of mucosa-associated microbiota in inflammatory bowel diseases.


We are actively engaged in multiple research arenas centered around understanding the associations between environmental exposures (primarily air pollution) and health outcomes. We use large clinical cohorts and electronic health records to understand associations between air pollution and health outcomes such as cardiovascular disease, metabolic disease, and aging. We use metabolomics and epigenetic data (primarily DNA methylation) to investigate molecular mechanisms, and highlight the integration of ‘omics data in a systems biology framework to better understand dysregulated pathways. Finally, we have projects centered around methods development and causal analyses to improve our understanding of the biology central to environmental health effects.


One of the most amazing discoveries of recent years has been the profound role of RNA in regulating all areas of biology. Further, the functions of many RNA molecules require that an RNA fold back on itself to create intricately and complexly folded structures. Until recently, however, we had little idea of the broad contributions of RNA structure and function because there simply did not exist rigorous methods for understanding RNA molecules in cells and viruses. The vision of our laboratory is therefore, first, to invent novel chemical microscopes that reveal quantitative structure and function interrelationships for RNA and, second, to apply these RNA technologies to broadly important problems in biology. Mentoring and research in the lab are highly interdisciplinary. Students learn to integrate ideas and concepts spanning chemical and computational biology, and technology development, and extending to practical applications in virology, understanding biological processes in cells, and discovery of small molecule ligands targeted against medically important RNAs. Each student has a distinct project which they drive to an impactful conclusion, but do so as part of the lab team which, collectively, has shown an amazing ability to solve big problems in RNA biology. The overarching goal of mentoring in the lab is to prepare students for long-term leadership roles in science.


Our group develops novel statistical bioinformatics tools and applies them in biomedical research to help understanding the precision medicine for cancer (e.g., breast cancer and lung cancer) subtypes, the disease associated integrative pathways across multiple genomic regulatory levels, and the genetics based drug repurposing mechanisms. Our recent focus includes pathway analysis, microbiome data analysis, data integration and electronica medical records (EMR). Our application fields include cancer, stem cell, autoimmune disease and oral biology. In the past, we have developed gene set testing methods with high citations, in the empirical Bayesian framework, to take care of small complex design and genewise correlation structure. These have been widely used in the microarray and RNAseq based gene expression analysis. Contamination detection for data analysis for Target DNA sequencing is work in progress. Recently, we also work on single cell sequencing data for pathway analysis with the local collaborators.


We are a translational cancer research lab. The overall goal of our research is to find therapeutic targets and biomarkers for patients with pancreatic cancer and to translate our results to the clinic. In order to accomplish this, we analyze patient tumors using a combination of genomics and proteomics to study the patient tumor and tumor microenvironment, identify and validate targets using forward and reverse genetic approaches in both patient-derived cell lines and mouse models. At the same time, we evaluate novel therapeutics for promising targets in mouse models in order to better predict clinical response in humans.


Psychosocial stress is abundant in modern societies and, when chronic or excessive, can have detrimental effects on our bodies. But how exactly does stress “get under the skin?” Our lab examines how stress shapes the human epigenome as age advances. Epigenetic changes are a set of chemical modifications that regulate gene transcription without altering the genetic code itself. We examine how lasting epigenetic patterns result from stressful experiences, accrue throughout life, and can in turn shape health or disease trajectories. We address these questions through a translational approach that combines large-scale analyses in human cohorts with mechanistic work in cellular models. We use both bioinformatics and wet lab tools. Our passion is to promote creative team work, offer strong mentorship, and foster scientific growth.


Our laboratory is focusing on developing and applying solution-state NMR methods, together with computational and biochemical approaches, to understand the molecular basis of nucleic acid functions that range from enzymatic catalysis to gene regulation. In particular, we aim to visualize, with atomic resolution, the entire dynamic processes of ribozyme catalysis, riboswitch-based gene regulation, and co-transciptional folding of mRNA. The principles deduced from these studies will provide atomic basis for rational manipulation of RNA catalysis and folding, and for de novo design of small molecules that target specific RNA signals. Research program in the laboratory provides diverse training opportunities in areas of spectroscopy, biophysics, structural biology, computational modeling, and biochemistry.


My research has been concentrated on the areas of statistical genetics and genomics to investigate the role of genetic variations on complex quantitative traits and diseases. I work primarily in the development, as well as the examination of statistical properties, of theoretical methodologies appropriate for the interpretation of genetic data.