Integrating discrete stochastic models with single-cell and single-molecule experiments

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.

Name thesis:degree_name

Doctor of Philosophy (Ph.D.)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Bioengineering

Grantor dc:publisher

Colorado State University. Libraries

Year dc:date.issued

2019

Authors dc:creator

• Fox, Zachary R., author
• Munsky, Brian, advisor
• Stargell, Laurie, committee member
• Wilson, Jesse, committee member
• Prasad, Ashok, committee member

• gene expression
• stochastic modeling
• master equation
• computational biology

Statement 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.

Language dc:language.iso

eng, English

Identifier URI

https://doi.org/10.25675/3.018854

OAI identifier oai:identifier

oai:mountainscholar.org:10217/197313