Abstract

The aim of the training program that we are proposing is to educate a generation of scientists who have the knowledge required to identify important biological problems and the expertise required to develop and apply advanced computational methods towards their solution. The scientist we envision will be an integral part of a discipline involving computational science but will be comfortable attending a meeting of experimental biologists as well. Indeed we believe that it is essential that the next generation of computational biologists have a deep understanding of biology. In addition it is important that they have familiarity with more than one computational discipline. For example, it would be desirable if computer scientists working on new algorithms to classify proteins would understand structural biology and have expertise in computational studies of protein structure and function as well. Integrating different computational disciplines is a challenging goal in its own right and is made more complex by the fact that the unifying theme is biological. We will ensure that our trainees have a deep understanding of experimental biology, have expertise in at least one area of computational biology and have familiarity with other areas.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Institutional National Research Service Award (T32)
Project #
5T32GM082797-04
Application #
8106252
Study Section
National Institute of General Medical Sciences Initial Review Group (BRT)
Program Officer
Hagan, Ann A
Project Start
2008-07-01
Project End
2013-06-30
Budget Start
2011-07-01
Budget End
2012-06-30
Support Year
4
Fiscal Year
2011
Total Cost
$220,940
Indirect Cost

  Institution

Name
Columbia University (N.Y.)
Department
Biochemistry
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032

  Publications

Boland, Mary Regina; Jacunski, Alexandra; Lorberbaum, Tal et al. (2016) Systems biology approaches for identifying adverse drug reactions and elucidating their underlying biological mechanisms. Wiley Interdiscip Rev Syst Biol Med 8:104-22
Lorberbaum, Tal; Sampson, Kevin J; Chang, Jeremy B et al. (2016) Coupling Data Mining and Laboratory Experiments to Discover Drug Interactions Causing QT Prolongation. J Am Coll Cardiol 68:1756-1764
Fazlollahi, Mina; Muroff, Ivor; Lee, Eunjee et al. (2016) Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets. Proc Natl Acad Sci U S A 113:E1835-43
Gayvert, Kaitlyn M; Dardenne, Etienne; Cheung, Cynthia et al. (2016) A Computational Drug Repositioning Approach for Targeting Oncogenic Transcription Factors. Cell Rep 15:2348-56
Lorberbaum, Tal; Sampson, Kevin J; Woosley, Raymond L et al. (2016) An Integrative Data Science Pipeline to Identify Novel Drug Interactions that Prolong the QT Interval. Drug Saf 39:433-41
Westphalen, C Benedikt; Takemoto, Yoshihiro; Tanaka, Takayuki et al. (2016) Dclk1 Defines Quiescent Pancreatic Progenitors that Promote Injury-Induced Regeneration and Tumorigenesis. Cell Stem Cell 18:441-55
Palmer, Cameron; Pe'er, Itsik (2016) Bias Characterization in Probabilistic Genotype Data and Improved Signal Detection with Multiple Imputation. PLoS Genet 12:e1006091
Chang, Jonathan; Gilman, Sarah R; Chiang, Andrew H et al. (2015) Genotype to phenotype relationships in autism spectrum disorders. Nat Neurosci 18:191-8
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
Lee, Albert K; Kulcsar, Kirsten A; Elliott, Oliver et al. (2015) De novo transcriptome reconstruction and annotation of the Egyptian rousette bat. BMC Genomics 16:1033

Showing the most recent 10 out of 33 publications