A Cell-Free Framework for Rapid Enzymatic Pathway Prototyping and Discovery

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It is pertinent now more than ever that we find sustainable alternatives to produce chemicals. For decades, scientists and engineers have turned to biological systems to help meet societal needs in energy, medicine, materials, and moreespecially when chemical synthesis is untenable. Often, biologically-produced small molecules are insufficient for production at the optimal titer, rate, or yield because natural sources are difficult to optimize and are simply not scalable (e.g., plants grow slowly). Thus, engineers seek to design enzymatic reaction schemes in microorganisms to meet manufacturing criteria. Unfortunately, this is difficult because design-build-test (DBT) cyclesiterations of re-engineering organisms to test new sets of enzymesare detrimentally slow due to the constraints of cell growth. As a result, a typical project today might only explore dozens of variants of an enzymatic reaction pathway. This is often insufficient to identify a commercially relevant solution, consequently requiring more DBT iterations. In this dissertation, I establish and develop an in vitro prototyping and rapid optimization of biosynthetic enzymes approach (termed iPROBE) to test biosynthetic pathways in cell-free systems and inform cellular metabolic engineering. In this approach, cell-free cocktails for synthesizing target small molecules are assembled in a mix-and-match fashion from crude cell lysates selectively enriched with pathway enzymes. I demonstrate that this approach can quickly study pathway enzyme ratios, tune individual enzymes in the context of a multi-step pathway, screen enzyme variants for high-performance enzymes, and discover enzyme functionalities. I develop automation techniques including liquid-handling robotics and machine learning to enhance cell-free screening experiments. Furthermore, I study the cell-free physiochemical environment of cell-free biosynthetic pathways to gain control over it, enhancing our ability to engineer pathways in cell-free systems. The rapid ability to build pathways in vitro using iPROBE allows us to generate large amounts of data describing pathway operation under several operating conditions. I reduce that data into a quantitative metric that combines product titer, production rate, and enzyme expression (TREE score). By simplifying the complexity of available cell-free data to one value we can now quickly screen and rank pathways in the cell-free environment and provide useful information for cellular metabolic engineering. I demonstrate iPROBE for the production of 3-hydroxybutyrate and n-butanol in Clostridium, an industrially relevant non-model organism. I anticipate that iPROBE will facilitate efforts to define, manipulate, and understand metabolic pathways for accelerated DBT cycles in the cell-free environment before engineering organisms. In addition, I have applied iPROBE to the study of natural product pathways often encrypted in large biosynthetic gene clusters with hopes of understanding and discovering new natural products. There are many firsts in this body of work and it is with great hopes that I share this work with the scientific community.

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  • 11/20/2019
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