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.
Beenhakker, Mark P.
Circuit mechanisms of sleep and epilepsy
Physical Modeling of Microarray Hybridization; Analysis of Genomic Tiling Array Data; Bioinformatics; Computational Biology; Regulatory Networks
Epigenetic and genetic mechanisms underlying metabolic disease
Bourne, Philip E
Condron, Barry G.
Regulation and Function Serotonergic Neurons During Development
Systems Immunology, Cancer Systems Biology, , Neonatal and Maternal Immunology
Cancer systems biology, Single-cell quantitative biology, Computational modeling
Farber, Charles R.
Systems Genetics of Skeletal Development and Maintenance
Felder, Robin A.
Clinical Chemistry and Toxicology. Medical Automation Research. Neurotransmitters, cell surface receptors and intracellular second messengers.
Holmes, Jeffrey W.
Healing after myocardial infarction, cardiac growth and remodeling, and image-based modeling and diagnosis.
Janes, Kevin A.
Systems-biology approaches to cancer biology and virology.
Kasson, Peter M.
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
Loughran, Jr., Thomas P
Hematologic malignancies; bone marrow disorders; leukemia; large granular lymphocyte (LGL)
Manichaikul, Ani W.
Statistical Genetics, Genetic Epidemiology, Biostatistics, Network analysis
Miller, Clint L.
Genetic variation, Complex diseases, Coronary artery disease, Genomics, Epigenomics, Regulatory mechanisms, Vascular biology, Pharmacology and Physiology
Regulation and function of tyrosine phosphorylation in complex networks
O’Rourke, Eyleen Jorgelina
Obesity and Aging
Papin, Jason A.
Systems biology, infectious disease, cancer, toxicology, metabolic engineering
Pearson, William R.
Protein Evolution; Computational Biology
Peirce-Cottler, Shayn M.
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
Sheynkman, Gloria M.
Proteoform Systems Biology: proteogenomic approaches to uncover the role of proteomic variation in human disease
Regulation of cell-surface stability and intracellular trafficking of membrane proteins in epithelial cells
Trinh, Bon Q
Understanding Protein and RNA regulations of gene expression via chromatin structure in myeloid cell development and diseases
Bioinformatics methodology development; Epigenetics and chromatin biology; Transcriptional regulation; Cancer genomics and epigenomics; Statistical methods for biomedical data integration; Advanced machine learning; Theoretical and computational biophysic