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Browsing by Author "Stasevich, Timothy J., committee member"

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    Characterizing unique features of prion-like domains recruited to stress granules
    (Colorado State University. Libraries, 2021) Baer, Matthew Henry, author; Ross, Eric D., advisor; Stasevich, Timothy J., committee member; Zabel, Mark D., committee member
    Upon nucleation into a cross-β-structure, a classical amyloid will elongate in a concentration-dependent manner as successive hydrogen bonds are made along each exposed β-strand. Solid-state assemblies in biomolecular condensates such as the Balbiani Body in oocytes share a similar β-sheet rich structure that contributes to low dynamicity of the interactions in this separated phase. Prion-like domains (PrLDs) are domains that compositionally resemble yeast prion domains. Some PrLDs can form solid-state assemblies; however, PrLDs have also been associated with the formation of more liquid-like biomolecular condensates. Liquid-like biomolecular condensates form upon varying stimuli, and they appear to assemble through different mechanisms of recruitment than what has been observed for solid-state assemblies. Many solid-state assemblies form via homotypic interactions between identical assembly-prone PrLDs. In contrast, liquid-like assemblies generally form through heterotypic interactions across multiple components. Both solid-state and liquid-like condensates recruit domains that are currently classified as "prion-like", but evidence is mounting that these contrasting condensates preferentially recruit functionally different types of prion-like domains. A unique feature of prion-like domains recruited to liquid-like condensates is that formation of these assemblies appears to obey a more diverse set of rules for recruitment. This diversity of recruitment of PrLDs may be largely due to the heterotypic interaction mechanism for assembly into liquid-like condensates. This mechanism of recruitment may also be partially responsible for the different compositional preferences observed for PrLDs recruited to liquid-like assemblies. PrLD manipulation is a convenient tool for analyzing the compositional elements contributing to formation of both classes of biomolecular condensates. To elucidate the divergent features of PrLDs recruited to liquid-like assemblies, we used systematic mutation of synthetic PrLDs, among other techniques, to examine their unique compositional requirements for assembly. We have created a set of synthetic PrLDs which demonstrates a gradient of assembly-propensity, where localization to stress granules is modulated by differences in residue composition of the domains. To measure the different degrees of assembly propensity of PrLDs, we developed a novel means of quantitative image analysis to determine the enrichment of PrLDs in stress granules. The goals of this work are to help uncover some of the compositional features unique to prion-like domains that are recruited to liquid-like assemblies such as stress granules, to introduce a novel method of image analysis that will allow for quantification of assembly propensity of PrLDs in yeast, and to probe the concentration dependence for assembly of PrLDs into stress granules.
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    Stochastic modeling to explore the central dogma of molecular biology and to design more informative single-molecule, live-cell fluorescence microscopy experiments
    (Colorado State University. Libraries, 2024) Raymond, William Scott, author; Munsky, Brian, advisor; Stasevich, Timothy J., committee member; Snow, Christopher D., committee member; Ben-Hur, Asa, committee member; Krapf, Diego, committee member
    Despite being described nearly a century ago, the Central Dogma of Molecular Biology still harbors many intricacies and mysteries that scientists have yet to unravel. With the convergence of many multidisciplinary scientific advances such as stronger computing power, next-generation sequencing, machine learning, and single-cell and single-molecule experiments, cellular biologists have never had more investigative power. These complex methods often are used in tandem--necessitating a closer relationship between computational biologists, computer scientists, and bench top experimentalists. As practice of this emerging dynamic, my corpus of work spans multiple areas within computational and quantitative biology with the goal to facilitate better computational tools to interpret and design experiment. For my main work at Colorado State University, I have developed the open source Python package "RNA sequence to Nascent protein simulator," rSNAPsim, to simulate Nascent Chain Tracking experiments and used it as a backbone for an entire experiment simulation pipeline to check experiment design feasibility. The rSNAPsim software provides start-to-finish capabilities for model design, model fitting, and model selection so that experimentalists can fit a mechanistic model to the Nascent Chain Tracking single-mRNA translation experiments. Along with this main work, I have provided computational modeling efforts on live-cell data on the first two steps of the Central Dogma, DNA transcription and mRNA translation. For the final entry in my corpus, I have used my interdisciplinary skills acquired at CSU to do machine learning based ncRNA riboswitch classification and discovery within the human genome; This work provides the broader scientific community with a starting point for searching for this important secondary structure within humans, where it has not been described as of time of writing.