Defining Reproducibility in PURE Cell-Free Expression through Ontological Benchmarking and a Minimal Information Reporting Standard

Cell-free protein synthesis systems offer a flexible platform to study biological processes outside of living cells. Despite their promise, these systems remain difficult to characterize and reproduce across laboratories, limiting their broader adoption and utility. This project will develop a rigorous benchmarking framework for protein expression using recombinantly expressed elements (PURE), a cell-free protein synthesis platform. The research will address a community-wide reproducibility challenge and laying the groundwork for rational, data-driven design of cell-free systems. By making PURE more accessible, predictable, and well-understood, this work will accelerate progress in biotechnology, synthetic biology, and the emerging field of synthetic cells: engineered, cell-like systems with the potential to transform medicine and precision biomanufacturing. This project advances NSF's priorities in artificial intelligence, advanced manufacturing, and biotechnology through foundational research to improve the reproducible cell-free synthesis of proteins. Protein expression using recombinantly expressed elements (PURE), achieves cell-free protein translation by reconstituting 36 proteins, ribosomes, tRNAs, and small molecules into a defined, minimal reaction environment. Because of its defined composition, PURE is an ideal platform for the construction of synthetic cells: engineered, cell-like systems that recapitulate the core functions of living cells while enabling precise, bottom-up control. Despite its complexity, PURE systems are typically benchmarked only by measuring fluorescent protein translation yields a convenient but information-limited metric that offers no diagnostic insight when systems underperform. Batch-to-batch and lab-to-lab variability further compound reproducibility challenges. This project proposes reducing this methodological complexity by combining PURE's constituent components into functionally related groups: transcription, translation, energy regeneration, and aminoacylation, thereby refining experimental methods to encapsulate the salient features of each group (e.g., ATP and GTP synthesis in energy regeneration, or percent tRNA charging in aminoacylation). These metrics will serve as input parameters for machine learning models trained to predict translation yield from system composition alone, enabling a computer-aided design workflow for PURE optimization and, by extension, synthetic cell engineering. Ultimately, a fully characterized and predictive PURE system lays the groundwork for the controlled central dogma that any protein-synthesizing synthetic cell will require. This award is funded by a collaboration between NSF, the National Institute of Standards and Technology, and Schmidt Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.