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. To address this need, UCSD initiated an interdisciplinary Graduate Program in Bioinformatics in 2000. The primary objectives in this renewal application of the training grant are to continue the premier program, support the highest quality students and train them in a truly interdisciplinary paradigm blending biomedicine, computer science and engineering. The Program will continue to offer a core curriculum in Bioinformatics and a host of electives that will prepare a student solve difficult problems in biomedical research. Given the extraordinary number and quality of applicants, this application seeks to enhance the number of trainee slots to 15. Each trainee will continue to be funded for three years. The proposed Bioinformatics Graduate Training Program will train students through specialized courses and highly interdisciplinary research programs across eight departments: Bioengineering, Biological Sciences, Biomedical Sciences, Cell and Molecular Medicine, Chemistry and Biochemistry, Computer Science, Mathematics and Physics. Several participating faculty members in the program work on research problems of immediate and long term relevance to human health and medicine. Understanding transcriptional processes quantitatively - a problem highly relevant to regenerative medicine and to diseases such as cancer, mapping the biochemical networks involved in insulin resistance - a problem of fundamental interest to a host of diseases, predicting outcomes in cardiac activity in normal and pathological states, and studying inflammation and accompanying immune responses are some of the significant research problems that will form research topics for trainees in the UCSD Bioinformatics Program. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
2T32GM008806-06
Application #
7065851
Study Section
Special Emphasis Panel (ZGM1-BRT-3 (01))
Program Officer
Li, Jerry
Project Start
2001-07-01
Project End
2011-06-30
Budget Start
2006-08-17
Budget End
2007-06-30
Support Year
6
Fiscal Year
2006
Total Cost
$221,779
Indirect Cost
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
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
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
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