Computational Biology

computational Biology Image-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
  • Adli, Mazhar

    Identify and target key genomic and epigenomic drivers in cancer

  • 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.

    Mechanisms of cell entry by influenza; Viral glycan recognition; drug resistance; molecular dynamics simulation; distributed computing.

  • 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

  • McConnell, Michael J

    The Cause and Consequence of Somatic Mosaicism in Neurons

  • 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