Computational Biology & Bioinformatics

PHD in Computational Biology & Bioinformatics

Program Principles & Goals

The PhD Program in Computational Biology & Bioinformatics (CBB) is an integrative, multi-disciplinary training program that encompasses the study of biology using computational and quantitative methods. In and out of the classroom, students learn to apply the tools of statistics, mathematics, computer science and informatics to biological problems. The vibrant and innovative Duke research in these fields provides exciting interactions between biological and computational scientists. Because the Program in Computational Biology and Bioinformatics is based in the Duke Center for Genomic and Computational Biology, it offers a unique opportunity for students to become one of tomorrow's leaders in the genome sciences.

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Meet A Faculty Member

  • Professor of Biostatistics and Bioinformatics

    Elizabeth Hauser, Professor in the Department of Medicine with secondary appointments in Biostatistics and Bioinformatics, Statistical Science, and Nursing, is a Statistical Geneticist with graduate degrees in Biostatistics and Epidemiology. Her research interests include statistical methods development for the analysis of complex genetic traits, genetic analysis of family data, identification of gene-environment interactions, and integrated analysis of metabolomics and genomic data. She has worked on studies of cardiovascular disease, diabetes, kidney disease, aging, and cancer. 

    My research interests are focused on developing and applying statistical methods to search for genes causing common human diseases. Recent work has been in the development of statistical methods for genetic studies and in identifying optimal study designs for genetic studies of complex traits. As application of these methods to specific diseases has progressed it has become apparent that etiologic and genetic heterogeneity is a major stumbling block in the research for genes for common diseases. I am interested in assessing the effects of complex modes on methods and in developing methods to detect and account for genetic heterogeneity.

    Collaborative studies under way at Duke University and elsewhere provide the opportunity to apply new methods to ongoing studies. My main area of application is in identifying genes for cardiovascular conditions including coronary artery disease and heart failure. One such study is the GENECARD study to identify genes for early onset coronary artery disease in families. This study has been underway for 10 years. Another study is the AGENDA study based on the CATHGEN database. These two studies have been used to successfully identify a number of novel genes for coronary artery disease. I also work on studies of genetic effects in aging, kidney disease and prenatal outcomes.

    In turn these Collaborative studies continue to raise methodological research questions such as the effect of model misspecification on the results of linkage studies, the interpretation of confirmation studies to replicate linkage results, and the utility of a method for including additional phenotypic information when assessing linkage results.

    Keywords: linkage analysis, genetic association, gene mapping, genetic epidemiology, statistical genetics, biostatistics, cardiovascular disease, diabetes, aging, prenatal genetics, kidney disease

Debraj Ghose

2nd year CBB Student. Daniel Lew Lab
May 10
Dejian Ren, PhD, Associate Professor of Biology, University of Pennsylvania
Duke Neurobiology

TBD-Dejian Ren, PhD

May 13
Silvia Arber, PhD. Professor of Neurobiology, Biozentrum, University of Basel
Ruth K. Broad Foundation Seminar Series on Neurobiology and Disease

TBD-Silvia Arber, PhD

May 17
Krishna V. Shenoy, PhD, Professor of Electrical Engineering, Stanford University, and Investigator, Howard Hughes Medical Institute
Duke Neurobiology

TBD-Krishna V. Shenoy, PhD