Computational Biology

About

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

  • Beenhakker, Mark P.

    Circuit mechanisms of sleep and epilepsy

  • 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

  • Bourne, Philip E

    Data Science

  • Condron, Barry G.

    Regulation and Function Serotonergic Neurons During Development

  • Dolatshahi, Sepideh

    Systems Immunology, Cancer Systems Biology, , Neonatal and Maternal Immunology

  • Fallahi-Sichani, Mohammad

    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.

  • 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

  • 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

  • Sheynkman, Gloria M.

    Proteoform Systems Biology: proteogenomic approaches to uncover the role of proteomic variation in human disease

  • Swiatecka-Urban, Agnieszka

    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

  • Zang, Chongzhi

    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