Integrating discrete stochastic models with single-cell and single-molecule experiments
| dc.contributor.author | Fox, Zachary R., author | |
| dc.contributor.author | Munsky, Brian, advisor | |
| dc.contributor.author | Stargell, Laurie, committee member | |
| dc.contributor.author | Wilson, Jesse, committee member | |
| dc.contributor.author | Prasad, Ashok, committee member | |
| dc.date.accessioned | 2019-09-10T14:35:43Z | |
| dc.date.available | 2019-09-10T14:35:43Z | |
| dc.date.issued | 2019 | |
| dc.description.abstract | Modern biological experiments can capture the behaviors of single biomolecules within single cells. Much like Robert Brown looking at pollen grains in water, experimentalists have noticed that individual cells that are genetically identical behave seemingly randomly in the way they carry out their most basic functions. The field of stochastic single-cell biology has been focused developing mathematical and computational tools to understand how cells try to buffer or even make use of such fluctuations, and the technologies to measure such fluctuations has vastly improved in recent years. This dissertation is focused on developing new methods to analyze modern single-cell and single-molecule biological data with discrete stochastic models of the underlying processes, such as stochastic gene expression and single-mRNA translation. The methods developed here emphasize a strong link between model and experiment to help understand, design, and eventually control biological systems at the single-cell level. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | Fox_colostate_0053A_15535.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/197313 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright 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.subject | gene expression | |
| dc.subject | stochastic modeling | |
| dc.subject | master equation | |
| dc.subject | computational biology | |
| dc.title | Integrating discrete stochastic models with single-cell and single-molecule experiments | |
| dc.type | Text | |
| dcterms.rights.dpla | This 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.discipline | Bioengineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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