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.
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.
|Gussow, Ayal B; Copeland, Brett R; Dhindsa, Ryan S et al. (2017) Orion: Detecting regions of the human non-coding genome that are intolerant to variation using population genetics. PLoS One 12:e0181604|
|Ojewole, Adegoke; Lowegard, Anna; Gainza, Pablo et al. (2017) OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design. Methods Mol Biol 1529:291-306|
|Hivert, Marie-France; Scholtens, Denise M; Allard, Catherine et al. (2017) Genetic determinants of adiponectin regulation revealed by pregnancy. Obesity (Silver Spring) 25:935-944|
|Azmi, Ishara F; Watanabe, Shinya; Maloney, Michael F et al. (2017) Nucleosomes influence multiple steps during replication initiation. Elife 6:|
|Washburne, Alex D; Silverman, Justin D; Leff, Jonathan W et al. (2017) Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets. PeerJ 5:e2969|
|Midani, Firas S; Wynn, Michelle L; Schnell, Santiago (2017) The importance of accurately correcting for the natural abundance of stable isotopes. Anal Biochem 520:27-43|
|Guo, Cong; McDowell, Ian C; Nodzenski, Michael et al. (2017) Transversions have larger regulatory effects than transitions. BMC Genomics 18:394|
|Vockley, Christopher M; McDowell, Ian C; D'Ippolito, Antony M et al. (2017) A long-range flexible billboard model of gene activation. Transcription 8:261-267|
|Silverman, Justin D; Washburne, Alex D; Mukherjee, Sayan et al. (2017) A phylogenetic transform enhances analysis of compositional microbiota data. Elife 6:|
|Gao, Chuan; McDowell, Ian C; Zhao, Shiwen et al. (2016) Context Specific and Differential Gene Co-expression Networks via Bayesian Biclustering. PLoS Comput Biol 12:e1004791|
Showing the most recent 10 out of 47 publications