CORE 4: EDUCATION Background: Columbia University currently has a large and successful training program in Computational Biology. This program currently involves about 50 PhD students, 20 Masters students and 30 postdoctoral fellows. Central to our educational efforts are the C2B2/MAGNet Graduate Program and the affiliated Training Program in Computational Biology. In addition, there are 7 other graduate programs which provide training in Computational Biology from the perspectives of their particular disciplines: Biochemistry and Molecular Biophysics, Biomedical Informatics, Biological Sciences, Computer Science, Pharmacology, Electrical Engineering, and Applied Physics and Applied Mathematics. Columbia currently offers over 20 courses in Computational Biology, Bioinformatics, and related fields. With C2B2 support, the MAGNet education core coordinates Computational Biology education across the various programs and unifies them into a single meta-program. The Education Core is run by Dr. Dennis Vitkup, Director, and Dr. Richard Friedman, Educational Coordinator. The Education Core reduces redundancy between courses, recruits, advises, and tracks students and postdoctoral fellows.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA121852-09
Application #
8532841
Study Section
Special Emphasis Panel (ZRG1-BST-K)
Project Start
Project End
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
9
Fiscal Year
2013
Total Cost
$225,509
Indirect Cost
$208,437
Name
Columbia University (N.Y.)
Department
Type
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Bisikirska, Brygida; Bansal, Mukesh; Shen, Yao et al. (2016) Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression. Cancer Res 76:664-74
Hosios, Aaron M; Hecht, Vivian C; Danai, Laura V et al. (2016) Amino Acids Rather than Glucose Account for the Majority of Cell Mass in Proliferating Mammalian Cells. Dev Cell 36:540-9
Sheng, Ren; Jung, Da-Jung; Silkov, Antonina et al. (2016) Lipids Regulate Lck Protein Activity through Their Interactions with the Lck Src Homology 2 Domain. J Biol Chem 291:17639-50
Park, Mi-Jeong; Sheng, Ren; Silkov, Antonina et al. (2016) SH2 Domains Serve as Lipid-Binding Modules for pTyr-Signaling Proteins. Mol Cell 62:7-20
Wang, Donglai; Kon, Ning; Lasso, Gorka et al. (2016) Acetylation-regulated interaction between p53 and SET reveals a widespread regulatory mode. Nature 538:118-122
Alvarez, Mariano J; Shen, Yao; Giorgi, Federico M et al. (2016) Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nat Genet 48:838-47
Nicoletti, Paola; Bansal, Mukesh; Lefebvre, Celine et al. (2015) ABC transporters and the proteasome complex are implicated in susceptibility to Stevens-Johnson syndrome and toxic epidermal necrolysis across multiple drugs. PLoS One 10:e0131038
Dantas Machado, Ana Carolina; Zhou, Tianyin; Rao, Satyanarayan et al. (2015) Evolving insights on how cytosine methylation affects protein-DNA binding. Brief Funct Genomics 14:61-73
Bansal, Mukesh; Mendiratta, Geetu; Anand, Santosh et al. (2015) Direct ChIP-Seq significance analysis improves target prediction. BMC Genomics 16 Suppl 5:S4
Emmett, Kevin J; Rosenstein, Jacob K; van de Meent, Jan-Willem et al. (2015) Statistical inference for nanopore sequencing with a biased random walk model. Biophys J 108:1852-5

Showing the most recent 10 out of 249 publications