The past decade has seen the introduction of high throughput experimental and computational methods that promise to revolutionize the approach to, and understanding of, the biology of the cell. We propose to build on this foundation by developing the computational tools and methods that will enable researchers to begin to understand collective cellular properties, and by substantially strengthening an educational program designed to train a new generation of biologists who can use these methods creatively. The goal is to develop and make widely available a large array of techniques that build upon concepts from statistics, computer science and theoretical chemistry, and to develop a system that takes advantage of their synergistic nature, and that allows integration of new and existing data upon which the methods would operate. As a demonstration project we focus on two well studied pathways related to the regulation of growth and apoptosis in a human cell line, and test the ability of these methods to unravel the relation between these pathways and the cascades of genes and proteins that they modulate. Plans for an organization that will educate the next generation of biomedical researchers, while creating new methodologies and advancing biomedical science will be developed by a large group of faculty from computer science, the experimental and clinical sciences, and statistics.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
5P20GM066401-02
Application #
6617988
Study Section
Special Emphasis Panel (ZRG1-SSS-E (01))
Program Officer
Anderson, James J
Project Start
2002-08-01
Project End
2005-07-31
Budget Start
2003-08-01
Budget End
2004-07-31
Support Year
2
Fiscal Year
2003
Total Cost
$403,750
Indirect Cost
Name
Boston University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
049435266
City
Boston
State
MA
Country
United States
Zip Code
02215
Dalgin, Gul S; Drever, Michele; Williams, Tara et al. (2008) Identification of novel epigenetic markers for clear cell renal cell carcinoma. J Urol 180:1126-30
Reddy, Timothy E; DeLisi, Charles; Shakhnovich, Boris E (2007) Binding site graphs: a new graph theoretical framework for prediction of transcription factor binding sites. PLoS Comput Biol 3:e90
Gustafson, Adam M; Snitkin, Evan S; Parker, Stephen C J et al. (2006) Towards the identification of essential genes using targeted genome sequencing and comparative analysis. BMC Genomics 7:265
Wu, Jie; Hu, Zhenjun; DeLisi, Charles (2006) Gene annotation and network inference by phylogenetic profiling. BMC Bioinformatics 7:80
Snitkin, Evan S; Gustafson, Adam M; Mellor, Joseph et al. (2006) Comparative assessment of performance and genome dependence among phylogenetic profiling methods. BMC Bioinformatics 7:420
Hu, Zhenjun; Mellor, Joe; Wu, Jie et al. (2005) VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res 33:W352-7
Yu, Liqun; Haverty, Peter M; Mariani, Juliana et al. (2005) Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum. Physiol Genomics 23:89-102
Tullai, John W; Schaffer, Michael E; Mullenbrock, Steven et al. (2004) Identification of transcription factor binding sites upstream of human genes regulated by the phosphatidylinositol 3-kinase and MEK/ERK signaling pathways. J Biol Chem 279:20167-77
Wu, Chang-Jiun; Fu, Yutao; Murali, T M et al. (2004) Gene expression module discovery using gibbs sampling. Genome Inform 15:239-48
Frith, Martin C; Fu, Yutao; Yu, Liqun et al. (2004) Detection of functional DNA motifs via statistical over-representation. Nucleic Acids Res 32:1372-81

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