Computational Biology
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.
Faculty
Bekiranov, Stefan
Physical Modeling of Microarray Hybridization; Analysis of Genomic Tiling Array Data; Bioinformatics; Computational Biology; Regulatory Networks
Bochkis, Irina
Epigenetic and genetic mechanisms underlying metabolic disease
Chen, Wei-Min
Statistical genetics and genomics.
Condron, Barry G.
Regulation and Function Serotonergic Neurons During Development
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.
Guertin, Michael
Transcription, Chromatin, Cancer, Molecular Biology, Genomics, and Computational Biology
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.
Kawasaki, Masashi
Neuroethology of electric fish
Li, Hui
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
Mura, Cameron
Structure, function & evolution of RNA-processing assemblies; structural and computational biology; molecular biophysics
Naegle, Kristen
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
Sheffield, Nathan
computational biology & bioinformatics; high performance computing; epigenomics & chromatin; pediatric cancer; computational regulatory genomics; machine learning
Zang, Chongzhi
Bioinformatics methodology development; Epigenetics and chromatin biology; Transcriptional regulation; Cancer genomics and epigenomics; Statistical methods for biomedical data integration; Theoretical and computational biophysics