This action funds an NSF Postdoctoral Fellowship in Biology for FY 2008. The fellowship supports a research and training plan entitled "Physical Underpinnings of Protein-DNA Interactions: The Role of DNA Tension in Modifying Protein Binding" for Kevin B. Wood who will be hosted by Prof. Sunney Xie at Harvard University.
Binding interactions between protein molecules and DNA form the basis for much of molecular biology. A quantitative physical understanding of binding is crucial for developing a complete picture of many genetic processes, including DNA transcription and genetic regulation. In this project, a combination of theoretical and experimental techniques are being employed to characterize the binding of proteins to DNA, with an eye towards understanding how binding can be modified by external forces. Specific goals of the project include the development of general physical models of force-modulated protein binding to DNA, the testing of these models using a fluorescence-based single molecule binding assay, and the continued expansion of the still relatively young statistical approaches for extracting model parameters from single molecule data. Because external tension can be directly applied to single DNA molecules--both in theory and practice-- the response of the binding interface to perturbation can be quantitatively probed, lending insight into the underlying physics of the protein-DNA complex. The synthesis of rigorous modeling with statistical data analysis and a novel experimental approach provides a setting in which model development can be constrained by experimental validation, meaning that the theoretical and empirical thrusts will mutually reinforce and guide one another.
In addition to research, the Fellow is taking an active role in mentoring and training summer undergraduate students and graduate rotation students in the lab. The research is presented at scientific meetings and also during interactive seminars at several small undergraduate colleges which lack graduate level science programs. The theoretical models and methods of data extraction will be published, with software source code freely and openly available to the wider science community.
