The overarching goal of the proposed Center for Quantitative Biology is to instantiate at Princeton a research and teaching environment that fully meets the challenge and opportunity to practice a usefully quantitative biological science. The Center will increase the bandwidth of communication among researchers from different disciplines and departments (including Molecular Biology, Ecology and Evolutionary Biology, Computer Science, Chemical Engineering and Physics), some of whom are already collaborating on quantitative projects and others who are planning to do so. For both undergraduate and graduate students, the Center will provide a focus for a new way of multidisciplinary teaching and learning, where the quantitative and biological are integrated from the beginning. The goal is to provide an education that prepares students with equal facility in biological and quantitative concepts.
The specific aims are: (1) to develop realistic and quantitative models of biological processes; (2) to collect large-scale data sets comprehensively describing biological processes.; (3) to devise new and improved methods for computational analysis and display of complex models, structures and data; (4) to institute at Princeton new curricula and course 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 a specific biological question in the subject areas of (i) spatial patterning during development; (ii) intracellular signaling and transcriptional networks, and (iii) virus-host interactions. All these projects have common quantitative goals, require a common infrastructure (i.e. substantial computation, microarray and imaging core facilities) and will benefit from the intellectual synergy and multi-disciplinary cooperation that the proposed Center will bring. An important aspect of this aim is to make all underlying data publicly available upon publication.
| 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 |
| Bartlett, Thomas M; Bratton, Benjamin P; Duvshani, Amit et al. (2017) A Periplasmic Polymer Curves Vibrio cholerae and Promotes Pathogenesis. Cell 168:172-185.e15 |
| 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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