Engineering of next-generation synthetic biology tools for biological applications

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Synthetic biology, by engineering biological systems for specific functions, could address many societal needs, whether in medicine, in environmental remediation, or in the green production of chemicals. However, foundational work remains to be done before synthetic biology can effectively address these challenges. For example, while synthetic circuits made of well-characterized components have been able to achieve a variety of functions, they generally have done so at a much lower precision, robustness, and stability than their natural counterparts. The long-term goals of my research program are to engineer next-generation synthetic gene circuits suitable for impactful applications, and to use them as models and tools to learn more about biology. Recently, we succeeded in engineering by far the most precise and robust synthetic circuit to date. This was achieved by using a quantitative approach and a microfluidic device enabling precise characterization of the dynamics of thousands of individual bacteria for hundreds of generations under precisely controlled growth conditions. However, this precision required expressing high number of proteins, which can impose a metabolic burden on the cells by using up scarce cellular resources. Such burden has made previous synthetic circuits unusable in industrial contexts, because natural selection immediately breaks such circuits. In this research proposal, we will generalize our previous approach to generate a wide range of synthetic oscillators with specific properties (toolbox of oscillators, Obj. 1): precision, robustness, period, amplitude, metabolic burden, etc. The number of these circuits and the range of their specific properties will be orders of magnitude greater than the few synthetic oscillators currently available. These toolboxes will enable us to study how cells can encode information in the dynamics of their protein levels and enable applications such as dynamic drug delivery (i.e. temporal variation of the level of drugs). We will also add new parts to the synthetic biologist toolkit, by measuring the memory properties of prion proteins in order to develop them as ultra-stable synthetic memory elements (Obj. 2). Prion proteins have recently been found to have a functional role across domains of life as a form of non-genetic inheritance, and measuring their dynamic properties in our microfluidic device will enable us to test hypotheses about the currently unknown mechanisms of prion formation and loss. Finally, we will use a recently developed burden sensor to optimize our synthetic circuits to be precise while carrying a low burden on their cellular host (Obj. 3), opening up their use in impactful applications. These objectives represent the first step towards our long-term goals, by providing the foundation for technologies such as dynamical drug delivery while teaching us about biology, for example by understanding mechanisms of prion-based information transfer.