Science
A Low-Cost, High-Throughput Design-Build-Test Pipeline for Engineering Genetic Systems: Stress Testing with Complex Structural Proteins
Key Points
Genetic systems engineering is constrained by high DNA synthesis costs, assembly inefficiencies, and challenges in expressing complex proteins. To address these limitations, we developed a highly parallel, low-cost pipeline for the design, assembly, and functional screening of genetic systems, which we stress-tested on highly repetitive structural proteins, including spider silk, biocements, reflectins, and talins. The integrated pipeline combines computational genetic systems design,...
Genetic systems engineering is constrained by high DNA synthesis costs, assembly inefficiencies, and challenges in expressing complex proteins. To address these limitations, we developed a highly parallel, low-cost pipeline for the design, assembly, and functional screening of genetic systems, which we stress-tested on highly repetitive structural proteins, including spider silk, biocements, reflectins, and talins. The integrated pipeline combines computational genetic systems design, low-cost many-plasmid DNA assembly from oligopools, automated many-to-many mapping using nanopore sequencing data, and a label-free biosensor to measure single-cell protein expression levels. We applied this pipeline to build 239 plasmids, achieving a 88% success rate (up to 2000 bp) using standard clonal isolation and 58% assembly efficiency (up to 5600 bp) without selective DNA purification, while lowering material costs by up to 24-fold. We applied the biosensor to identify genetic factors that create distinct cellular subpopulations with varying protein expression levels. Overall, the integrated pipeline will dramatically lower the cost of high-throughput synthetic biology, while demonstrating how designing genetic systems to improve build efficiency ("design for build") and directly incorporating biosensors into genetic systems ("design for test") will greatly accelerate design-build-test workflows.