Simulations in Chemical and Condensed Matter Physics
This group studies a variety of complex many body systems providing insight at many length and time scales into the collective phenomena of interest. A current focus is on abstracted models of interacting polymer systems far from equilibrium in which they are accumulating data on the statisitcal distribution of chemical morphologies and dynamics. These models are intended to provide better understanding of how dynamic metastable states involving large molecules can emerge from a starting configuration of small molecules as is believed to have occurred in the origin of life. The researchers have studied a "well mixed reactor" version of such a model and are currently studying an extension in which spatial heterogeneity and diffusion can occur. Studies of the detailed morphological and dynamic character of the well mixed model also continue. Simulations using more chemically realistic descriptions of the atomic level are also planned.
A second focus is on the behavior of oxide water interfaces using in-house self consistent tightbinding codes. There is tremendous current interest in oxides as electrodes in a variety of technologies using aqueous electrolytes including fuel cells, batteries and electrolysers. Water-oxide interfaces are also a key component in corroding metal surfaces so such studies are also relevant to attempts to understand and inhibit corrosion. In one project the researchers are simulating at titania water interfaces with particular emphasis on new methods for calculating surface energies to understand the propensity of titania water interfaces to dissociate water. This project is a collaboration with former student Patrick Schelling, now an associate professor at the University of Central Florida, and his students. A second project in this category is a simulation of the magnetite water interface. In addition to its obvious corrosion relevance, the study is intended to provide better understanding of the mechanisms of water dissociation at a magnetite water interface, as occurs in experiments which the group is doing on the use of magnetite electrodes in electrolysers for production of gaseous hydrogen as an energy storage medium. This project is a collaboration with Professor Melissa Eblen of the Carleton College Physics Department, the Natural Resources Research Institute in Duluth, and high school physics teacher Jon Huber, who worked with the Halley Group in the Research Experiences for Teachers program at Minnesota in summer 2015 and continues experiments with students in Burnsville. In a third related project the researchers are beginning molecular dynamics simulations of membrane proteins in water at various solid aqueous interfaces. The aim is to explore the possible use of such membrane proteins to solve certain technical problems associated with the behavior of lithium-based electrodes in water in applications to batteries. This is a preliminary exploratory collaboration with Jonathan Sachs of the Department of Biomedical Engineering.
Thirdly the group simulates quantum fluid phenomena. A current emphasis is on condensate mediated transmission using Diffusion Monte Carlo methods to obtain informaton about excited scattering states in the strongly interacting helium four superfluid. The methods are unique and were developed in this group. They have published results in Physical Review B using a guiding wave function which did not conserve particle current. The current project is to use an improved guiding wave function which removes that defect. The project is relevant to an experiment they proposed more than a decade ago and which has been tried in various laboratories, still without a definitive result, to observe the condensate mediated transmission effect. Another quantum fluids project, carried on at a low intensity level, is the exploration of effects of disorder, and in particular of disorder induced pairing, in superconductors.
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HIV and HTLV Molecular and Cell Biology
These researchers are using MSI resources for two projects.
- HIV Reverse Transcriptase-Mediated Mutagenesis: HIV-1 has a high mutation rate, which contributes to its ability to evade the host immune system, limits the efficacy of antiretroviral drugs, and drives the emergence of drug resistance. Drug resistance conferring mutations as well as other viral mutations are primarily attributed to the error-prone nature of reverse transcriptase (RT). An intentional increase in RT-mediated mutations decreases virus infectivity by increasing the mutation rate to a level that is not able to maintain survival of the virus population. The potency by which HIV-1 infectivity can be decreased by increasing RT-mediated errors has led to an initiative to discover small molecules that may increase the HIV-1 mutation rate. An interdisciplinary collaborative team has been assembled to conduct discovery studies to identify new small molecules that increase RT-mediated errors, use molecular analyses to identify the mechanism(s) by which small molecules increase the HIV mutation rate and result in virus extinction, and to assess the mechanism of RT-mediated mutation using biochemical methods. Through preliminary studies, the researchers have identified four small molecules that increase RT-mediated mutations. In order to elucidate the structure-activity relationship driving this increase and to optimize this activity, they are first pursuing discovery studies to identify small molecules that can increase RT-mediated errors. The antiviral and mutagenic activities of these molecules will be assessed in cell culture. Second, they will examine the mechanism by which small molecules induce mutations and cause virus extinction in HIV-1 using cell culture methodologies. Here they will examine small molecules that they have already discovered as well as any lead molecules that they identify. Third, they will investigate the mechanism of action using biochemical methods to elucidate the mechanistic basis for increased RT-mediated mutation. Successful completion of these studies will provide deeper insight into the mechanisms of RT-mediated viral mutagenesis and its impact on viral replication and extinction.
- HTLV-1 Particle Analysis and Gag Interactions: Human T-cell leukemia virus (HTLV-1) infects about 20 million individuals worldwide and is the etiological agent of an adult T-cell leukemia/lymphoma (ATLL). It can also result in an inflammatory disease syndrome called HTLV-1-associated myelopathy (HAM)/tropical spastic paraparesis (TSP). Prevalence rates for HTLV-1 infection in the general population are greater than 1% in the Caribbean Basin, Central Africa, and South Japan. HTLV-1 is notorious for being difficult to study in cell culture, which has prohibited a rigorous analysis of how these viruses replicate in cells, including the steps involved in retrovirus assembly. The details for how retrovirus particle assembly occurs are poorly understood even for other more tractable retroviral systems. Using a tractable model system, state-of-the-art biophysical approaches, and an interdisciplinary research team, these researchers have made novel observations that form the basis for this project. The researchers are beginning to investigate questions related to HTLV-1 particle size, Gag stoichiometry in particles, and HTLV-1 Gag interactions in living cells using multiple experimental approaches. In particular, they will apply cryo-electron microscopy/tomography (cryo-EM/ET), total internal reflection fluorescence (TIRF) microscopy, and the novel single-molecule technology of fluorescence fluctuation spectroscopy (FFS) to investigate questions related to particle size and Gag stoichiometry, Gag targeting to membrane, and HTLV-1 particle biogenesis. The results from these studies should provide further insight into fundamental aspects of HTLV-1 and retrovirus particle assembly, which may aid in developing therapeutics.
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Global Land Model Development: Time to Shift From a Plant Functional Type to a Plant Functional Trait Approach
This project will advance global land models by shifting from the current plant functional type approach to one that better utilizes what is known about the importance and variability of plant traits, within a framework of simultaneously improving fundamental physiological relations that are at the core of model carbon cycling algorithms. A primary goal for earth system modeling is to make accurate predictions of the future trajectory of the climate system, based on a mechanistic understanding of processes regulating fluxes of mass and energy among system components. Land plays an important role in modifying the earth's mass and energy balance, as a critical link in the global cycling of carbon, among others. Land surface models have developed to include mechanistic representations of vegetation physiology, carbon and nutrient dynamics in plants and soils, how they might respond to changing climate and chemistry, and how those changes might feedback to influence changes in atmospheric greenhouse gases themselves. This project addresses these processes.
Existing models represent the global distribution of vegetation types using the Plant Functional Type concept. Plant Functional Types are classes of plant species with similar evolutionary and life history with presumably similar responses to environmental conditions like CO2, water and nutrient availability. Fixed properties for each Plant Functional Type are specified through a collection of physiological parameters, or traits. These traits, mostly physiological in nature (e.g., leaf nitrogen and longevity) are used in model algorithms to estimate ecosystem properties and/or drive calculated process rates. In most models, 5 to 15 functional types represent terrestrial vegetation; in essence, they assume there are a total of only 5 to 15 different kinds of plants on the entire globe. This assumption of constant plant traits captured within the functional type concept has serious limitations, as a single set of traits does not reflect trait variation observed within and between species and communities. While this simplification was necessary decades past, substantial improvement is now possible. Rather than assigning a small number of constant parameter values to all grid cells in a model, procedures will be developed that predict a frequency distribution of values for any given grid cell. Thus, the mean and variance, and how these change with time, will inform and improve model performance.
The trait-based approach will improve land modeling by: incorporating patterns and heterogeneity of traits into model parameterization, thus evolving away from a framework that considers large areas of vegetation to have near identical trait values; utilizing what is know about trait-trait, -soil, and -climate relations to improve algorithms used to predict processes at multiple stages; and allowing for improved treatment of physiological responses to environment (such as temperature and/or CO2 response of photosynthesis or respiration).
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