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Peptide recognition domains (PRD) bind to short linear motifs on their biological partners and are found in several cellular pathways including those found to be critical in tumorigenesis. In this study, I aimed to generate peptide probes against PRDs present on proteins involved in ovarian cancer. Using bioinformatics, I identified 66 potential PRDs present on these proteins. I then used peptide phage display to successfully generate peptides against 27 of the 66 domains. To validate my results, I performed an extensive literature review and structural analysis. For several cases, the phage-display derived binding preferences are similar to previously reported studies. However, for a subset of domains, I identified non-canonical binding preferences that have not been reported previously in literature. The binding preferences obtained ...
Comment: 13 pages and 6 figures including supplementary information. The paper has been modified in light of new theoretical results available at arXiv:0808.0488
We report on the temperature dependent electron transport in graphene at different carrier densities $n$. Employing an electrolytic gate, we demonstrate that $n$ can be adjusted up to 4$\times10^{14}$cm$^{-2}$ for both electrons and holes. The measured sample resistivity $\rho$ increases linearly with temperature $T$ in the high temperature limit, indicating that a quasi-classical phonon distribution is responsible for the electron scattering. As $T$ decreases, the resistivity decreases more rapidly following $\rho (T) \sim T^{4}$. This low temperature behavior can be described by a Bloch-Gr\"{u}neisen model taking into account the quantum distribution of the 2-dimensional acoustic phonons in graphene. We map out the density dependence of the characteristic temperature $\Theta_{BG}$ defining the cross-over between the two distinct re...
Biological systems exhibit rich and complex behavior through the orchestrated interplay of a large array of components. It is hypothesized that separable subsystems with some degree of functional autonomy exist; deciphering their independent behavior and functionality would greatly facilitate understanding the system as a whole. Discovering and analyzing such subsystems are hence pivotal problems in the quest to gain a quantitative understanding of complex biological systems. In this work, using approaches from machine learning, physics and graph theory, methods for the identification and analysis of such subsystems were developed. A novel methodology, based on a recent machine learning algorithm known as non-negative matrix factorization (NMF), was developed to discover such subsystems in a set of large-scale gene expression data. Th...
The conductance and thermoelectric power (TEP) of graphene is simultaneously measured using microfabricated heater and thermometer electrodes. The sign of the TEP changes across the charge neutrality point as the majority carrier density switches from electron to hole. The gate dependent conductance and TEP exhibit a quantitative agreement with the semiclassical Mott relation. In the quantum Hall regime at high magnetic field, quantized thermopower and Nernst signals are observed and are also in agreement with the generalized Mott relation, except for strong deviations near the charge neutrality point.
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