Predicting Gene Expression For Robust Genetic System Design
Open Access
- Author:
- Cetnar, Daniel
- Graduate Program:
- Chemical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- July 19, 2021
- Committee Members:
- Andrew Zydney, Major Field Member
Howard Salis, Chair & Dissertation Advisor
Phillip Savage, Major Field Member
Philip Bevilacqua, Outside Unit & Field Member
Seong Han Kim, Program Head/Chair - Keywords:
- mRNA
RNA Decay
NGS
Synthetic Biology
Gene Expression
RNase
Machine Learning
Modeling - Abstract:
- This body of work aims to further the chief goal of synthetic biology, which fundamentally consists of the ability to design, build, and test new biological functions. To achieve this goal, we require predictive control over the fundamentals of genetic systems. In particular, we need clear and predictive models to simulate and design entire genetic systems. A key aspect of this design ability is to have control over each component of the biological system that impacts gene expression. Here I describe my work to better understand and model the fundamental processes that determine gene expression. In chapter 2, I use rational design to build small libraries to test and quantify the core sequence and structure determinants of mRNA decay in E. coli. In chapter 3, I expand on the idea of mRNA decay by using Next Generation Sequencing (NGS) to test a large library of constructs and build a comprehensive model of mRNA decay using machine learning. In chapter 4, I use a similar NGS experimental approach to build a library of promoters for use in a variety of organisms. In chapter 5, I discuss the main findings and describe avenues for future research. Overall, this work describes methods of determining and modeling the fundamental processes of gene expression to aid in the building of new genetic systems and explaining the performance of existing systems.