A laser tweezers system for transporting dissociated neurons into small “cages” on a culture dish was constructed, and it was studied extensively.

The system consists of an inverted microscope, a 1064 nm or 980 nm laser module, a beam expander, a motorized mechanical stage, a CCD camera, and steering mirrors. A laser beam is generated by the IR laser module, and the beam is expanded by the beam expander to match the size of the back aperture of the objective. The beam is then steered into the objective where it is focused to a point. The system uses this single, tightly focused laser beam to trap a neuron. Once a neuron is trapped and lifted, the mechanical stage is moved to locate the neuron above its destination. The system will know the location of the neurocages and will automatically move neurons to their destination.

Mid-ocean ridge basalts (MORB) are mixtures of melts produced over a range of pressure and temperature in a nearly adiabatic open system undergoing changes in composition as melting proceeds. Interpretation of the compositional variations observed in MORB and their correlation with geophysical aspects of the ridge therefore requires complex forward models to connect experimental observations of isothermal, isobaric batch melting of peridotite to natural compositions. Previous attempts to construct such models have relied on parameterizations of melt composition or partition coefficients and extent of melting in pressure-temperature space from experimental batch melting data. This thesis undertakes the examination of an alternative approach using thermodynamic models of silicate minerals and melts to predict equilibria under quite arbitrary constraints, including variable bulk composition and constant entropy. The liquids predicted from the thermodynamic models along polybaric paths can then be integrated to produce comprehensive forward models of MORB genesis.

Previous efforts in designing protein binding interfaces have focused on altering binding specificities. These methods fall short, however, when applied to the design of novel binding sites due to difficulties in accurately modeling protein backbones. The goal of this project is to create dimers from monomeric proteins. We developed a special docking algorithm that positions the member protein subunits to a plausible configuration with respect to each other using parameters determined from known complex structures. The docking procedure treats the proteins as rigid bodies and uses Fourier correlation theorem and fast Fourier transform to efficiently search for dimers with the highest interfacial surface complementarities. Using the docked structures as scaffolds for design and employing hydrophobic surface residues to drive dimer formation, we have demonstrated two successful designs, one heterodimer and one homodimer, using protein G and engrailed homeodomain respectively as the starting monomeric proteins. The designed dimers were characterized using circular dichroism, nuclear magnetic resonance, analytical ultracentrifugation, and X-ray crystallography methods. This is the first report of computationally designed de novo protein homodimers generated using a combination of protein docking and protein design tools. These results suggest that this strategy can be used to address the protein recognition problem, and is generally applicable to creating novel binding sites with compatible binding partners

The enzyme-inhibitor dissociation constants, i.e., [...], were evaluated for the six isomeric pairs of C-substituted indolecarboxylate ions and carboxamides. The variation of [...] with the position and nature of the substituent indicates that the enzyme-indole complex exhibits a high degree of steric hindrance near the 4 position of the indole ring and electrostatic repulsion due to a negative group near the indole nitrogen.

Particulate air pollution is of growing concern in the United States and around the world. Elevated concentrations of aerosols (solid particles and liquid droplets suspended in air) are correlated with increased cases of lung cancer, cardiopulmonary disorders, and human mortality. A detailed understanding of the size, chemical composition, and concentration of atmospheric particles is needed to assess their effects on human health, as well as on regional visibility and global climate. One can acquire such knowledge through direct measurements, or by utilizing mathematical air quality models. New and innovative instruments allow us to measure the size and composition of individual particles, rather than to infer aerosol chemical properties from bulk particulate matter samples. Concurrently, air quality models have been developed to numerically simulate the emissions of discrete particles, and their transport and chemical evolution in the atmosphere. This thesis focuses on how to integrate and compare measurements taken by state-of-the-science single-particle instruments with the air pollutant properties calculated using state-of-the-science mathematical models. A 1996 field experiment conducted in the Los Angeles air basin serves as the case study for this thesis research.