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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

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dc.contributor.authorRaymond, William Scott, author
dc.contributor.authorMunsky, Brian, advisor
dc.contributor.authorStasevich, Timothy J., committee member
dc.contributor.authorSnow, Christopher D., committee member
dc.contributor.authorBen-Hur, Asa, committee member
dc.contributor.authorKrapf, Diego, committee member
dc.date.accessioned2024-05-27T10:32:58Z
dc.date.available2024-05-27T10:32:58Z
dc.date.issued2024
dc.description.abstractDespite 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.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierRaymond_colostate_0053A_18365.pdf
dc.identifier.urihttps://hdl.handle.net/10217/238535
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectmachine learning
dc.subjectmRNA
dc.subjectriboswitch
dc.subjectmechanistic modeling
dc.subjectlive-cell imaging
dc.subjectquantitative biology
dc.titleStochastic modeling to explore the central dogma of molecular biology and to design more informative single-molecule, live-cell fluorescence microscopy experiments
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineBiomedical Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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