Modern biomedical research relies on interdisciplinary approaches such as bioinformatics that synthesize knowledge and methods from other disciplines to provide an integrated framework for solving biomedical problems. The rapid advancement of high-throughput technologies for measuring biological systems has generated a significant demand at Dartmouth College and other research institutions across Northern New England for interdisciplinary approaches in the quantitative sciences (e.g. bioinformatics, biostatistics, genomics, mathematical biology, proteomics, and systems biology). Integrating high-dimensional research databases with clinical databases from medical schools and hospitals across the region will be needed for translational medicine to become a reality. Unfortunately, the research institutions in Maine, New Hampshire, and Vermont are in a largely rural setting have not kept pace those in larger metropolitan areas such as nearby Boston or New York. The goal of this COBRE program is to establish a Quantitative Biology Research Institute (QBRI) that will support and enhance quantitative biology research across the region and facilitate its integration and synergy with experimental and observational biology. This will be accomplished by 1) establishing a Quantitative Biology Research Institute (QBRI) focused on developing, supporting, and enhancing quantitative biology research in Maine, New Hampshire, and Vermont that will become nationally and internationally recognized, free standing, and will foster meaningful collaborations with experimental biologists thus improving the ability of investigators in the region to compete for NIH funding, 2) recruiting talented tenure track quantitative biologists to Maine, New Hampshire, and Vermont, 3) mentoring the development of four junior quantitative biologists across the region and 4) promoting synergistic collaborations between quantitative biologists and experimental biologists through four research projects, an Administrative Core and an Integrative Biology Core. The scientific focus of the four research projects is gene-environment interaction within the context of environmental health and toxicology. This provides an important unifying and synergistic theme for the COBRE.

Public Health Relevance

The goal of this program is to establish a Quantitative Biology Research Institute that will enhance the ability of scientists working in Northern New England to use mathematics and computer science to solve complex biomedical research questions. As such it is highly responsive to the RFA and to the strategic mission of the NCRR.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory Grants (P20)
Project #
5P20GM103534-03
Application #
8517152
Study Section
Special Emphasis Panel (ZRR1-RI-B (01))
Program Officer
Liu, Yanping
Project Start
2011-09-01
Project End
2016-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
3
Fiscal Year
2013
Total Cost
$2,119,671
Indirect Cost
$560,026
Name
Dartmouth College
Department
Genetics
Type
Schools of Medicine
DUNS #
041027822
City
Hanover
State
NH
Country
United States
Zip Code
03755
Zhao, Yanding; Varn, Frederick S; Cai, Guoshuai et al. (2018) A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma. Cancer Epidemiol Biomarkers Prev 27:86-95
Ji, Xuemei; Bossé, Yohan; Landi, Maria Teresa et al. (2018) Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk. Nat Commun 9:3221
Chernikova, Diana A; Madan, Juliette C; Housman, Molly L et al. (2018) The premature infant gut microbiome during the first 6 weeks of life differs based on gestational maturity at birth. Pediatr Res 84:71-79
Demidenko, Eugene (2017) Exact and Approximate Statistical Inference for Nonlinear Regression and the Estimating Equation Approach. Scand Stat Theory Appl 44:636-665
Frost, H Robert; Amos, Christopher I (2017) Gene set selection via LASSO penalized regression (SLPR). Nucleic Acids Res 45:e114
Byun, Jinyoung; Han, Younghun; Gorlov, Ivan P et al. (2017) Ancestry inference using principal component analysis and spatial analysis: a distance-based analysis to account for population substructure. BMC Genomics 18:789
Demidenko, Eugene; Glaholt, S P; Kyker-Snowman, E et al. (2017) Single toxin dose-response models revisited. Toxicol Appl Pharmacol 314:12-23
Kodaman, Nuri; Sobota, Rafal S; Asselbergs, Folkert W et al. (2017) Genetic Effects on the Correlation Structure of CVD Risk Factors: Exome-Wide Data From a Ghanaian Population. Glob Heart 12:133-140
Telomeres Mendelian Randomization Collaboration; Haycock, Philip C; Burgess, Stephen et al. (2017) Association Between Telomere Length and Risk of Cancer and Non-Neoplastic Diseases: A Mendelian Randomization Study. JAMA Oncol 3:636-651
Varn, Frederick S; Wang, Yue; Mullins, David W et al. (2017) Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment. Cancer Res 77:1271-1282

Showing the most recent 10 out of 182 publications