Research in Computational Biology at UVA uses mathematical and computational techniques to analyze, explain, and predict biological systems.The past years have brought a dramatic increase in the amount of experimental data generated by high-throughput sequencing, proteomics, metabolic and gene expression profiling, and structural techniques. Simultaneously, the computational power we have available has continued to increase exponentially. We thus have huge data sets at our disposal coupled with the ability to make increasingly sophisticated analyses.
Computational biology at UVA includes cutting-edge research in computational biophysics, genomics, computational structural biology, and computational systems biology. Our work is focused on addressing fundamental biological questions and understanding diseases of medical relevance, such as cancer, cardiovascular disease, lung infections in cystic fibrosis, drug-resistant bacterial infections, and influenza.
In addition to analyzing biological systems, many laboratories at UVA combine computational and experimental work, using sophisticated tools to analyze biomolecular behavior and then verifying predictions in the lab.
Physical Modeling of Microarray Hybridization; Analysis of Genomic Tiling Array Data; Bioinformatics; Computational Biology; Regulatory Networks
Epigenetic and genetic mechanisms underlying metabolic disease
Statistical genetics and genomics.
Regulation and Function Serotonergic Neurons During Development
Systems Genetics of Skeletal Development and Maintenance
Clinical Chemistry and Toxicology. Medical Automation Research. Neurotransmitters, cell surface receptors and intracellular second messengers.
Transcription, Chromatin, Cancer, Molecular Biology, Genomics, and Computational Biology
Healing after myocardial infarction, cardiac growth and remodeling, and image-based modeling and diagnosis.
Systems-biology approaches to cancer biology and virology.
Physical mechanisms of infectious disease; influenza infection; membrane fusion; antibiotic resistance; molecular dynamics simulation; machine learning.
Neuroethology of electric fish
Gene regulation in cancer, RNA processing; Epigenetic modification; Stem cell and development
Hematologic malignancies; bone marrow disorders; leukemia; large granular lymphocyte (LGL)
Statistical Genetics, Genetic Epidemiology, Biostatistics, Network analysis
Genetic variation, Complex diseases, Coronary artery disease, Genomics, Epigenomics, Regulatory mechanisms, Vascular biology, Pharmacology and Physiology
Structure, function & evolution of RNA-processing assemblies; structural and computational biology; molecular biophysics
Regulation and function of tyrosine phosphorylation in complex networks
Obesity and Aging
Systems biology, infectious disease, cancer, toxicology, metabolic engineering
Protein Evolution; Computational Biology
Tissue Engineering and Regeneration, Computational Systems Biology, Vascular Growth and Remodeling, Stem Cell Therapies
computational biology & bioinformatics; high performance computing; epigenomics & chromatin; pediatric cancer; computational regulatory genomics; machine learning
Bioinformatics methodology development; Epigenetics and chromatin biology; Transcriptional regulation; Cancer genomics and epigenomics; Statistical methods for biomedical data integration; Theoretical and computational biophysics