This proposal is a resubmission of an application for a training grant for the Duke University Program in Computational Biology and Bioinformatics (CBB). CBB is a graduate program. It admits its own students and grants the PhD. Its mission is to educate the next generation of scientists who will work at the interface between the life sciences and the quantitative sciences. CBB supports students for their first two years, after which they move onto support from the faculty. The program aims to admit around ten new students each year. This proposal requests ten slots, which would support half the students. The program typically takes five years to complete, and it embodies a wide range of academic activities, including three core courses and three research rotations, all taken in the first year, a required seminar series, careful RCR training, and interdisciplinary mentoring and supervision. The program is supported by an unusually broad, but well integrated group of faculty. Research areas of the faculty are quite broad, and are grouped into six areas: Systems Biology, Genome Analysis, Computational Structural Biology, Genomic Medicine, Evolutionary Genomics and Mathematical and Statistical Modeling of Biological Systems. Students emerge well prepared to work in collaborative research groups where biologists and computational scientists make joint intellectual contributions to solving real biological problems. CBB is completing its fifth year of initial funding from the NIH. These five years have seen significant growth of computational biology at Duke University, the program now includes 57 faculty (24 in quantitative departments and 33 in biological ones) from 15 departments in three schools and one Institute. Excellent students have bee recruited into the Program and retention is excellent with 43 of the 49 students that have been admitted since the program began having finished their PhD or making good progress towards their degree. Graduates have done extremely well, 6 have taken postdoctoral positions at prestigious institutions and 4 have taken strong industrial positions.

Public Health Relevance

The mission of the Duke University Program in Computational Biology and Bioinformatics (CBB) is to educate the next generation of scientists who will work at the interface between the life sciences and the quantitative sciences. The program accomplishes this mission through a wide range of academic activities and is supported by an unusually broad, but well integrated group of faculty who prepare students to work in collaborative research groups to solve real biological problems. Founded in 2001, CBB has become the primary mechanism for training computational biology students and Duke and is a model for similar programs nationwide.

National Institute of Health (NIH)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Marcus, Stephen
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Duke University
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
Zip Code
Gorka, Adam X; Knodt, Annchen R; Hariri, Ahmad R (2015) Basal forebrain moderates the magnitude of task-dependent amygdala functional connectivity. Soc Cogn Affect Neurosci 10:501-7
Richards, Adam J; Staats, Janet; Enzor, Jennifer et al. (2014) Setting objective thresholds for rare event detection in flow cytometry. J Immunol Methods 409:54-61
Benjamin, Ashlee M; Nichols, Marshall; Burke, Thomas W et al. (2014) Comparing reference-based RNA-Seq mapping methods for non-human primate data. BMC Genomics 15:570
Rudicell, Rebecca S; Kwon, Young Do; Ko, Sung-Youl et al. (2014) Enhanced potency of a broadly neutralizing HIV-1 antibody in vitro improves protection against lentiviral infection in vivo. J Virol 88:12669-82
Guinney, Justin; Ferte, Charles; Dry, Jonathan et al. (2014) Modeling RAS phenotype in colorectal cancer uncovers novel molecular traits of RAS dependency and improves prediction of response to targeted agents in patients. Clin Cancer Res 20:265-72
Bristow, Sara L; Leman, Adam R; Simmons Kovacs, Laura A et al. (2014) Checkpoints couple transcription network oscillator dynamics to cell-cycle progression. Genome Biol 15:446
Lubelsky, Yoav; Prinz, Joseph A; DeNapoli, Leyna et al. (2014) DNA replication and transcription programs respond to the same chromatin cues. Genome Res 24:1102-14
Zhong, Jianling; Wasson, Todd; Hartemink, Alexander J (2014) Learning protein-DNA interaction landscapes by integrating experimental data through computational models. Bioinformatics 30:2868-74
Lee, Hangnoh; McManus, C Joel; Cho, Dong-Yeon et al. (2014) DNA copy number evolution in Drosophila cell lines. Genome Biol 15:R70
Majoros, William H; Lebeck, Niel; Ohler, Uwe et al. (2014) Improved transcript isoform discovery using ORF graphs. Bioinformatics 30:1958-64

Showing the most recent 10 out of 14 publications