Physics
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Item A UNITED-ATOM REPRESENTATION FOR SPHINGOLIPIDS IN THE CHARMM MOLECULAR DYNAMICS FORCE FIELD(2023) Lucker, Joshua; Klauda, Jeffery B; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The development of the CHARMM force field (FF) in the late 1970’s and early 1980’s was groundbreaking at the time. For the first time, a computer program was created that could simulate biological systems on a macromolecular scale. Starting with the simulation of simple proteins, CHARMM has since expanded to include such macromolecules as nucleic acids and lipids, now being able to model complex biological systems and processes. Force fields like CHARMM can be represented in different ways. For example, force fields can be represented through an all-atom representation, in which all atoms in a system are modeled as distinct interaction units. This representation can be simplified into a united-atom representation, which shall be the primary focus of this thesis. A united atom FF has no explicit interaction sites for hydrogen. Instead, the hydrogens are lumped onto the atoms they are connected to, termed ‘heavy atoms’ as these atoms have a greater atomic weight than hydrogen. The CHARMM FF originally had a united-atom representation for proteins, which was abandoned to focus on all-atom representations. However, in certain cases, such as lipid tails, united-atom representations are often useful in certain situations; as compared to all-atom representations, united-atom models often speed up simulation times, which is useful in the simulation of large enough systems of molecules. Although there are currently united-atom representations for many types of biomolecules in the CHARMM FF, including multiple types of membrane lipids, there has yet to be a united-atom model for sphingolipids, a type of membrane lipid most commonly found in the myelin sheath of neurons, although its presence has been noted in many types of eukaryotic cells. The goal of this thesis is thus to develop such a model and implement it in the CHARMM FF.Item MULTI-DIMENSIONAL ANALYSIS APPROACHES FOR HETEROGENEOUS SINGLE-CELL DATA(2018) Shen, Yang; Losert, Wolfgang; Chemical Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Improvements in experimental techniques have led to an explosion of information in biology research. The increasing number of measurements comes with challenges in analyzing resulting data, as well as opportunities to obtain deeper insights of biological systems. Conventional average based methods are unfit to analyze high dimensional datasets since they fail to take full advantage of such rich information. More importantly, they are not able to capture the heterogeneity that is prevalent in biological systems. Sophisticated algorithms that are able to utilize all available measurements simultaneously are hence emerging rapidly. These algorithms excel at making full use of information within datasets and revealing detailed heterogeneity. However, there are several important disadvantages of existing algorithms. First, specific knowledge in statistics or machine learning is required to appropriately interpret and tune parameters in these algorithms for future use. This may result in misusage and misinterpretation. Second, using all measurements with equal weighting runs the risk of noise contamination. In addition, information overload has become more common in biology research, with a large volume of irrelevant measurements. Third, regardless of the quality of measurements, analysis methods that simultaneously use a large number of measurements need to avoid the “curse of dimensionality”, which warns that distance estimation and nearest neighbor estimation are not meaningful in high dimensional space. However, most current sophisticated algorithms involve distance estimation and/or nearest neighbor estimation. In this dissertation, my goal is to build analysis methods that are complex enough to capture heterogeneity and at the same time output results in a format that is easy to interpret and familiar to biologists and medical researchers. I tackle the dimension reduction problem by finding not the best subspace but dividing them into multiple subspaces and examine them one by one. I demonstrate my methods with three types of datasets: image-based high-throughput screening data, flow cytometry data, and mass cytometry data. From each dataset, I was able to discover new biological insights as well as re-validate well-established findings with my methods.Item Biological control of noise(2012) Gupta, Ashutosh; Levens, David L; Upadhyaya, Arpita; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biological systems are remarkably precise in a lot of different ways. Not only do organisms have the capacity to reproduce, they also have the capacity to defend themselves from external factors. The capability to fight diseases, in particular the immune system, is an integral part of the evolution and natural selection in all plants and animals. For most species there are multiple layers of defense, which are adaptive and provide mechanisms (or adaptive immunological memory) to remember previous attacks and successively improve the response. From reproduction to defense and maintenance, each organism constantly monitors its internal and external environments at several different levels. Several crucial constituent factors are required to be maintained at close tolerances. A deviation, or a push, away from equilibrium could prove fatal to an individual cell or the whole organism. These deviations also have a shared history with our evolution in the form of diseases like cancer. In this study, we present some of our efforts to understand the origin and control of this biological noise at four different levels from a physical sciences perspective. The entire study of this dissertation has its origins linked to a proto-oncogene called c-myc, which is believed to regulate about 10% of mammalian genes. It controls all major decisions of cells, including cell division and cell death, and it is known to be deregulated in most types of cancers. Noisy c-myc transcription can have disastrous effects, thus its expression levels must be controlled very tightly by cells. At the DNA level, we examine a dynamic feedback mechanism where DNA supercoils during transcription, and dynamic torsional stresses are mechanically coupled with ongoing transcription to control the transcriptional noise. DNA supercoiling has been previously shown to regulate the c-myc proto-oncogene. We have developed genome-wide maps of transcription generated dynamic DNA supercoiling in vivo. We observe, experimentally, that most of the torsional stress is located within about ±2000 bp of transcription start site, and is differentially regulated by topoisomerases I and II. At the RNA level, we have made an attempt to define the state of the cell using the expression levels of a sub-network of differentially expressed human kinases. Based on this definition, we have been successfully able to cluster together different molecular subtypes in lung cancer cell lines. We were able to identify and confirm previously known deregulated kinases. Many kinase genes are also identified as novel therapeutic targets. Currently we are testing these predictions, and working towards defining the complete state of a cell by getting a digital count of mRNAs at the single cell level. At the protein level, we studied the dynamics of protein decay to test the hypothesis that protein decay is a one step stochastic process. In several cases we have observed potentially multi-step decay processes in the ubiquitin proteasome system, however more experiments are needed before making any inferences.