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

Date

2019

Authors

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

Journal Title

Journal ISSN

Volume Title

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.

Description

2019 Summer.
Includes bibliographical references.

Rights Access

Subject

gene expression
stochastic modeling
master equation
computational biology

Citation

Associated Publications