The overarching goal of the proposed Center for Quantitative Biology remains to instantiate at Princeton a research and teaching environment that fully meets the challenge and opportunity, presented by advances in computation and genomics, to practice a usefully quantitative biological science, sometimes referred to as """"""""systems biology"""""""". The programs and infrastrucure of the Center increase the bandwidth of communication among researchers from different disciplines and departments (including Molecular Biology, Ecology and Evolutionary Biology, Computer Science, Chemistry and Physics). One in four (54/217) papers published with Center support in the last four years is a joint publication between two or more Center faculty. For both undergraduate and graduate students, the Center provides a focus for multidisciplinary teaching and learning; quantitative and biological ideas are integrated from the beginning, with the result that students acquire nearly equal facility in biological and quantitative thinking. The Center's specific aims are: (1) to develop realistic and quantitative models of biological processes;(2) to collect large-scale data sets that comprehensively describe biological processes;(3) to devise new and improved methods for computational analysis and display of complex models, structures and data and to make, upon publication, all underlying data, algorithms and anal3/tical systems publicly accessible;(4) to devise and support new curricula and courses of quantitative biology education for undergraduates, graduate students, and the larger scientific community;and (5) to reduce these ideas to practice in several collaborative and multi-disciplinary projects, each aimed at specific systemlevel questions in the subject areas of (i) intracellular signaling, (ii) pattern and cell signaling in multicellular organisms, (iii) host-pathogen interactions and (iv) bioinformatics and data visualization. These projects share common quantitative goals, require a common infrastructure (i.e. computation, microarray, imaging and metabolomics core facilities) and all of them benefit from the intellectual synergy and multi-disciplinary cooperation that the Center provides.

Public Health Relevance

Advancing technology in genomics and computation has led to new ways of thinking, understanding, and studying living things. Revolutionary diagnostic and therapeutic possibilities for complex diseases like cancer have become possible. Realizing these possibillities requires the multidisciplinary environment, technical resources and a fully quantitative biological education that the Princeton Center provides.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Specialized Center (P50)
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Special Emphasis Panel (ZGM1-CBCB-4 (SB))
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Brazhnik, Paul
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Princeton University
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Lee, Ha Neul; Mitra, Mithun; Bosompra, Oye et al. (2018) RECK isoforms have opposing effects on cell migration. Mol Biol Cell 29:1825-1838
Garcia, Hernan G; Gregor, Thomas (2018) Live Imaging of mRNA Synthesis in Drosophila. Methods Mol Biol 1649:349-357
Mitra, Mithun; Johnson, Elizabeth L; Swamy, Vinay S et al. (2018) Alternative polyadenylation factors link cell cycle to migration. Genome Biol 19:176
Gibney, Patrick A; Schieler, Ariel; Chen, Jonathan C et al. (2018) Common and divergent features of galactose-1-phosphate and fructose-1-phosphate toxicity in yeast. Mol Biol Cell 29:897-910
Mitra, Mithun; Lee, Ha Neul; Coller, Hilary A (2018) Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts. J Vis Exp :
Lee, Andrew H; Dhingra, Satish K; Lewis, Ian A et al. (2018) Evidence for Regulation of Hemoglobin Metabolism and Intracellular Ionic Flux by the Plasmodium falciparum Chloroquine Resistance Transporter. Sci Rep 8:13578
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Klibaite, Ugne; Berman, Gordon J; Cande, Jessica et al. (2017) An unsupervised method for quantifying the behavior of paired animals. Phys Biol 14:015006
Sanchez, Monica R; Miller, Aaron W; Liachko, Ivan et al. (2017) Differential paralog divergence modulates genome evolution across yeast species. PLoS Genet 13:e1006585
Matheson, Kinnari; Parsons, Lance; Gammie, Alison (2017) Whole-Genome Sequence and Variant Analysis of W303, a Widely-Used Strain of Saccharomyces cerevisiae. G3 (Bethesda) 7:2219-2226

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