STRUCTURAL ANALYSIS OF RIBOSOME BINDING ELEMENTS OF THE 3´ UTR IN TWO POSITIVE-STRAND PLANT RNA VIRUSES

Turnip crinkle virus (TCV) and Pea enation mosaic virus (PEMV) are two positive (+)-strand RNA viruses that are used to investigate the regulation of translation and replication due to their small size and simple genomes. Both viruses contain cap-independent translation elements (CITEs) within their 3´ untranslated regions (UTRs) that fold into tRNA-shaped structures (TSS) according to nuclear magnetic resonance and small angle x-ray scattering analysis (TCV) and computational prediction (PEMV). Specifically, the TCV TSS can directly associate with ribosomes and participates in RNA-dependent RNA polymerase (RdRp) binding. The PEMV kissing-loop TSS (kl-TSS) can simultaneously bind to ribosomes and associate with the 5´ UTR of the viral genome. Mutational analysis and chemical structure probing methods provide great insight into the function and secondary structure of the two 3´ CITEs. However, lack of 3-D structural information has limited our understanding of their functional dynamics. Here, I report the folding dynamics for the TCV TSS using optical tweezers (OT), a single molecule technique. My study of the unfolding/folding pathways for the TCV TSS has provided an unexpected unfolding pathway, confirmed the presence of Ψ3 and hairpin elements, and suggested an interconnection between the hairpins and pseudoknots. In addition, this study has demonstrated the importance of the adjacent upstream adenylate-rich sequence for the formation of H4a/Ψ3 along with the contribution of magnesium to the stability of the TCV TSS. In my second project, I report on the structural analysis of the PEMV kl-TSS using NMR and SAXS. This study has re-confirmed the base-pair pattern for the PEMV kl-TSS and the proposed interaction of the PEMV kl-TSS with its interacting partner, hairpin 5H2. The molecular envelope of the kl-TSS built from SAXS analysis suggests the kl-TSS has two functional conformations, one of which has a different shape from the previously predicted tRNA-shaped form. Along with applying biophysical methods to study the structural folding dynamics of RNAs, I have also developed a technique that improves the production of large quantities of recombinant RNAs in vivo for NMR study. In this project, I report using the wild-type and mutant E.coli strains to produce cost-effective, site-specific labeled, recombinant RNAs. This technique was validated with four representative RNAs of different sizes and complexity to produce milligram amounts of RNAs. The benefit of using site-specific labeled RNAs made from E.coli was demonstrated with several NMR techniques.

Prediction of Upward Flame Spread over Polymers

In this work, the existing understanding of flame spread dynamics is enhanced through an extensive study of the heat transfer from flames spreading vertically upwards across 5 cm wide, 20 cm tall samples of extruded Poly (Methyl Methacrylate) (PMMA). These experiments have provided highly spatially resolved measurements of flame to surface heat flux and material burning rate at the critical length scale of interest, with a level of accuracy and detail unmatched by previous empirical or computational studies. Using these measurements, a wall flame model was developed that describes a flame’s heat feedback profile (both in the continuous flame region and the thermal plume above) solely as a function of material burning rate. Additional experiments were conducted to measure flame heat flux and sample mass loss rate as flames spread vertically upwards over the surface of seven other commonly used polymers, two of which are glass reinforced composite materials. Using these measurements, our wall flame model has been generalized such that it can predict heat feedback from flames supported by a wide range of materials. For the seven materials tested here – which present a varied range of burning behaviors including dripping, polymer melt flow, sample burnout, and heavy soot formation – model-predicted flame heat flux has been shown to match experimental measurements (taken across the full length of the flame) with an average accuracy of 3.9 kW m-2 (approximately 10 – 15 % of peak measured flame heat flux). This flame model has since been coupled with a powerful solid phase pyrolysis solver, ThermaKin2D, which computes the transient rate of gaseous fuel production of constituents of a pyrolyzing solid in response to an external heat flux, based on fundamental physical and chemical properties. Together, this unified model captures the two fundamental controlling mechanisms of upward flame spread – gas phase flame heat transfer and solid phase material degradation. This has enabled simulations of flame spread dynamics with a reasonable computational cost and accuracy beyond that of current models. This unified model of material degradation provides the framework to quantitatively study material burning behavior in response to a wide range of common fire scenarios.

Computational Analysis of Intelligent Agents: Social and Strategic Settings

The central motif of this work is prediction and optimization in presence of multiple interacting intelligent agents. We use the phrase `intelligent agents' to imply in some sense, a `bounded rationality', the exact meaning of which varies depending on the setting. Our agents may not be `rational' in the classical game theoretic sense, in that they don't always optimize a global objective. Rather, they rely on heuristics, as is natural for human agents or even software agents operating in the real-world. Within this broad framework we study the problem of influence maximization in social networks where behavior of agents is myopic, but complication stems from the structure of interaction networks. In this setting, we generalize two well-known models and give new algorithms and hardness results for our models. Then we move on to models where the agents reason strategically but are faced with considerable uncertainty. For such games, we give a new solution concept and analyze a real-world game using out techniques. Finally, the richest model we consider is that of Network Cournot Competition which deals with strategic resource allocation in hypergraphs, where agents reason strategically and their interaction is specified indirectly via player's utility functions. For this model, we give the first equilibrium computability results. In all of the above problems, we assume that payoffs for the agents are known. However, for real-world games, getting the payoffs can be quite challenging. To this end, we also study the inverse problem of inferring payoffs, given game history. We propose and evaluate a data analytic framework and we show that it is fast and performant.