Optimizing a Synthetic Signaling System, Using Mathematical Modeling to Direct Experimental Work.

摘要

Synthetic biology uses engineering principles and biological parts to probe existing biological networks and build new biological systems. As biological components become better characterized, synthetic biology can make use of predictive mathematical models to analyze the activity of biological systems. This thesis demonstrates the utility of modeling in optimizing a synthetic signaling system for a bacterial testing platform and advances the use of model-based bacterial systems as an effective tool of plant synthetic biology. Using models in combination with experimental data, I showed that increasing the concentration of a single component of the synthetic signaling system, the PBP, results in a 100 fold increase in sensitivity, and an order of magnitude increase in fold change response in the response of the bacterial testing platform. Additional mathematical exploration of the system identified another component, the number of PhoB inducible promoters, which could be adjusted to further increase maximum signal. In addition, our model has suggested additional avenues of research, including the potential to introduce new functions, such as memory, to the existing circuit. In this way the prototype synthetic signaling system developed by the Medford Lab has been refined to improve detection and generate substantial response, moving the technology closer to real-world use. Once validated, this modeling based protocol, using a microbial platform for developing and optimizing plant synthetic systems, will serve as a foundation for engineering advanced plant synthetic systems.