Abstract

Biology is increasingly becoming an information-driven science. There is an enormous demand for biologists who are trained in mathematics and computer science and can think quantitatively. However, current disciplinary graduate training programs are not designed to accommodate these rapid changes in the biological research perspective. This need serves as the motivation for the development of specialized graduate training programs that will train students at the interface between biology, engineering and computer science. The University of California at San Diego, recognizing the need for quantitatively trained biologists, has launched a Graduate Program in Bioinformatics. UCSD, with its rich history of interdisciplinary programs and a large number of faculty who are pioneers in bioinformatics, offers a unique opportunity to advance graduate training in Bioinformatics. The proposed Bioinformatics Graduate Training Program will train students through specialized courses and highly interdisciplinary research programs across seven departments; Bioengineering, Biology, Biomedical Sciences, Chemistry and Biochemistry, Computer Science, Mathematics, and Physics.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM008806-04
Application #
6768783
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Whitmarsh, John
Project Start
2001-07-01
Project End
2006-06-30
Budget Start
2004-07-01
Budget End
2005-06-30
Support Year
4
Fiscal Year
2004
Total Cost
$253,462
Indirect Cost

  Institution

Name
University of California San Diego
Department
Engineering (All Types)
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093

  Publications

Bui, Nam; Huang, Justin K; Bojorquez-Gomez, Ana et al. (2018) Disruption of NSD1 in Head and Neck Cancer Promotes Favorable Chemotherapeutic Responses Linked to Hypomethylation. Mol Cancer Ther 17:1585-1594
Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku et al. (2018) Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. Cell Syst 6:484-495.e5
Kuo, Chih-Chung; Chiang, Austin Wt; Shamie, Isaac et al. (2018) The emerging role of systems biology for engineering protein production in CHO cells. Curr Opin Biotechnol 51:64-69
Nguyen, Nam-Phuong D; Deshpande, Viraj; Luebeck, Jens et al. (2018) ViFi: accurate detection of viral integration and mRNA fusion reveals indiscriminate and unregulated transcription in proximal genomic regions in cervical cancer. Nucleic Acids Res 46:3309-3325
Huang, Justin K; Jia, Tongqiu; Carlin, Daniel E et al. (2018) pyNBS: a Python implementation for network-based stratification of tumor mutations. Bioinformatics 34:2859-2861
Beyter, Doruk; Lin, Miin S; Yu, Yanbao et al. (2018) ProteoStorm: An Ultrafast Metaproteomics Database Search Framework. Cell Syst 7:463-467.e6
Panopoulos, Athanasia D; D'Antonio, Matteo; Benaglio, Paola et al. (2017) iPSCORE: A Resource of 222 iPSC Lines Enabling Functional Characterization of Genetic Variation across a Variety of Cell Types. Stem Cell Reports 8:1086-1100
Shen, John Paul; Zhao, Dongxin; Sasik, Roman et al. (2017) Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions. Nat Methods 14:573-576
Si, Fangwei; Li, Dongyang; Cox, Sarah E et al. (2017) Invariance of Initiation Mass and Predictability of Cell Size in Escherichia coli. Curr Biol 27:1278-1287
Kramer, Michael H; Farré, Jean-Claude; Mitra, Koyel et al. (2017) Active Interaction Mapping Reveals the Hierarchical Organization of Autophagy. Mol Cell 65:761-774.e5

Showing the most recent 10 out of 29 publications