ADVANCING RNA CIRCUITRY ENGINEERING WITH GUIDANCE OF MATHEMATICAL AND COMPUTATIONAL MODELS
The main goal of synthetic biology is to harness the power of biological genetic expression in order to perform various tasks, which could often have an impact on many aspects of our lives ranging from healthcare, pharmaceutical, to renewable energy and environments. These tasks are often carried out through synthetic genetic circuits composed of genetic parts like promoters, RBS, terminators, and inducible gene regulators. Numerous studies have expanded the toolbox for synthetic circuitry and established the guideline of circuitry assembly in the past decade. As the field advances, regulatory sRNA is emerging as a powerful tool in synthetic biology. These regulatory sRNAs are versatile and designable, more importantly, they offer a fast dynamic in genetic circuitry due to the fast production rates and degradation rates of RNA molecules. However, little work has been done to create a theoretical foundation for RNA circuit design. With the ultimate goal of gaining full control of RNA circuitry dynamics, this work focuses on building the theoretical foundation to understand and guide the design of RNA synthetic circuitry. A prerequisite to building such a foundation is to create a modeling framework that accurately describes the dynamics of RNA circuits. In the first part of this work, we build an effective model composed of ordinary differential equations to describe transcriptional RNA genetic circuitry and validate the model using a three-level cascade as a test case. We develop a sensitivity analysis based parameterization procedure that requires only a handful of simple experiments that can be performed in parallel using rapid cell-free transcription-translation (TX-TL) reactions. This part of the work establishes a fundamental method to predict circuit dynamics, which allow us to build more complex systems. Next, we expand the repertoire of synthetic gene networks built from these regulators by constructing a transcriptional negative autoregulation (NAR) network out of small RNAs (sRNAs). Using parameter sensitivity analysis, we design a simple set of experiments that allow us to accurately predict the NAR circuit dynamic in TX-TL. We also transfer the successful network design into Escherichia coli and show that our sRNA transcriptional network is functional in vivo. In the third part of this work, we investigate into an interesting observation where the transfer functions of inducible systems are altered by small transcriptional activating RNAs (STARs). We combine theory, computational model and experimental results to uncover the underlying causes of this phenomenon. Based on which, we establish the design principle of transfer function manipulation and dynamic range amplification induced by STARs. Together the work presented here establishes a theoretical foundation of RNA circuitry design. We anticipate that this foundation would support the development of new mathematical and computational models that provide insights and guidance for RNA circuitry design —which ultimately contributes to the advancement of synthetic circuitry engineering.
Model guided circuit design; Model parameterization; RNA Circuitry; Systems Biology; Applied mathematics; Bioengineering; system identification; synthetic biology
Varner, Jeffrey D.
Daniel, Susan; Lucks, Julius
Ph. D., Chemical Engineering
Doctor of Philosophy
Attribution-NonCommercial-NoDerivatives 4.0 International
dissertation or thesis
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International