Biostatistical and Computational Biology

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The mission of the Biostatistics and Computational Biology Core (BCB) is to provide quantitative suppor for intramural collaboration and coordination of all AIDS-related research activities at Duke, thereby increasing the scientific impact of research done by CFAR investigators. We will work closely with the other CFAR Core leadership to anticipate the needs of HIV-related scholarship at Duke and create the appropriate statistical methods and computer infrastructure to fulfill these needs. Key areas that we anticipate will be critical needs for the Duke CFAR are: 1. Biostatistical consulting for social and behavioral sciences research and clinical trials 2. Laboratory assay development and analysis for flow cytometry and molecular virology 3. Mentoring and education in statistics and quantitative analysis 4. Data management and integration of laboratory and clinical data CFAR investigators can use the Core to consult about statistical or computational biology issues, collaborate on a manuscript or grant, upgrade their quantitative skills by taking a statistics or computational module, or simply make use of software tools developed by Core members. To facilitate such Core-Core interactions, we adopt a model where a single member is the primary contact for each CFAR Core, allowing us to specialize according to our skill sets and scientific interests while also becoming deeply familiar with the relevant science. During the first CFAR funding cycle, the BCB Core has been extensively utilized and has provided critical support to investigators from the Flow Cytometry, Molecular Virology, Social and Behavioral Sciences and Clinical Cores. In addition, BCB Core investigators have made independent scientific contributions through the development of innovative software for multi-parameter flow cytometry, and developed methods for inferring the natural histories of HIV sequences, inferring the V(D)J recombination components for immunoglobulin and T-cell receptor DNA sequences, predicting Hl_A binding by potential peptide T-cell epitopes, and enhanced data processing for cellular tracking, and ELISA, Luminex and gene expression microarray results.