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.
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.
|Andrew, Angeline S; Gui, Jiang; Hu, Ting et al. (2015) Genetic polymorphisms modify bladder cancer recurrence and survival in a USA population-based prognostic study. BJU Int 115:238-47|
|Kodaman, Nuri; Pazos, Alvaro; Schneider, Barbara G et al. (2014) Human and Helicobacter pylori coevolution shapes the risk of gastric disease. Proc Natl Acad Sci U S A 111:1455-60|
|Darabos, Christian; White, Marquitta J; Graham, Britney E et al. (2014) The multiscale backbone of the human phenotype network based on biological pathways. BioData Min 7:1|
|Davis, Matthew A; Gilbert-Diamond, Diane; Karagas, Margaret R et al. (2014) A dietary-wide association study (DWAS) of environmental metal exposure in US children and adults. PLoS One 9:e104768|
|Frost, H Robert; Moore, Jason H (2014) Optimization of gene set annotations via entropy minimization over variable clusters (EMVC). Bioinformatics 30:1698-706|
|Pechenick, Dov A; Payne, Joshua L; Moore, Jason H (2014) Phenotypic robustness and the assortativity signature of human transcription factor networks. PLoS Comput Biol 10:e1003780|
|Sirugo, Giorgio; Predazzi, Irene M; Bartlett, Jacquelaine et al. (2014) G6PD A- deficiency and severe malaria in The Gambia: heterozygote advantage and possible homozygote disadvantage. Am J Trop Med Hyg 90:856-9|
|Gorlov, Ivan P; Moore, Jason H; Peng, Bo et al. (2014) SNP characteristics predict replication success in association studies. Hum Genet 133:1477-86|
|Goody, Michelle F; Sullivan, Con; Kim, Carol H (2014) Studying the immune response to human viral infections using zebrafish. Dev Comp Immunol 46:84-95|
|Peterson, Sarah M; Thompson, Jeffrey A; Ufkin, Melanie L et al. (2014) Common features of microRNA target prediction tools. Front Genet 5:23|
Showing the most recent 10 out of 32 publications