Determining The Genotype-Phenotype Connection In Synthetic Inducible Gene Expression Systems

<p><strong>Introduction</strong> Gene expression is an important process whereby the genotype controls an individual cell’s phenotype. However, even genetically identical cells display a variety of phenotypes, which may be attributed to differences in their environment. Yet, even after controlling for these two factors, individual phenotypes still diverge due to noisy gene expression. Synthetic gene expression systems allow investigators to isolate, control, and measure the effects of noise on cell phenotypes. I used mathematical and computational methods to design, study, and predict the behavior of synthetic gene expression systems in <em>S. cerevisiae</em>, which were affected by noise.</p> <p><strong>Methods</strong> I created probabilistic biochemical reaction models from known behaviors of the <em>tetR</em> and <em>rtTA</em> genes, gene products, and their gene architectures. I then simplified these models to account for essential behaviors of gene expression systems. Finally, I used these models to predict behaviors of modified gene expression systems, which were experimentally verified.</p> <p><strong>Results</strong> Cell growth, which is often ignored when formulating chemical kinetics models, was essential for understanding gene expression behavior. Models incorporating growth effects were used to explain unexpected reductions in gene expression noise, design a set of gene expression systems with “linear” dose-responses, and quantify the speed with which cells explored their fitness landscapes due to noisy gene expression.</p> <p><strong>Conclusions</strong> Models incorporating noisy gene expression and cell division were necessary to design, understand, and predict the behaviors of synthetic gene expression systems. The methods and models developed here will allow investigators to more efficiently design new gene expression systems, and infer gene expression properties of TetR based systems.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation (PhD)

Year dc:date.available

2012

Author dc:creator

• Adams, Rhys M

Contributors dc:contributor

• Gábor Balázsi
• John H. Byrne
• Oleg Igoshin

• noise
• systems biology
• escape rate
• fitness
• autoregulation
• phenotype switching
• synthetic biology
• gene expression autoregulation
• positive feedback
• negative feedback
• Biophysics

Repository record dc:identifier

https://digitalcommons.library.tmc.edu/utgsbs_dissertations/273

OAI identifier oai:identifier

oai:digitalcommons.library.tmc.edu:utgsbs_dissertations-1283