Browsing by Author "O'Connell, Ronan"

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    Development of experimental platforms for ultra-high- throughput exploration of complex genetic design spaces
    (2024-12-05) O'Connell, Ronan; Bashor, Caleb J
    Cells sense and process signals from their environment to execute a diverse array of tasks, ranging from proliferation and differentiation to programmed cell death. Inspired by the capabilities of biological systems to carry out sophisticated computations, the field of synthetic biology aims to use nucleic acid-encoded “synthetic” regulatory programs to quantitatively engineer novel cellular behaviors for environmental, biotechnological, and therapeutic purposes. Like many forms of engineering, synthetic biology projects follow a design-build-test-learn cycle: an iterative process of constructing, assaying, and modifying genetic circuits to achieve desired phenotypes. However, unlike more established forms of engineering, we do not have a quantitative set of core principles that describe the complexities of all biological activity. This limitation is pronounced in mammalian synthetic biology, where lengthy design campaigns and an incomplete understanding of the system make precisely programming cellular functions difficult. One approach to addressing challenges in synthetic biology is to increase the pace and scale of data acquisition and allow experimental data to replace hypotheses as the cornerstone of decision-making. Here, I present a suite of molecular biology and cell engineering tools that lay the foundations of a novel platform designed to enable high-throughput construction and quantitative assessment of large and complex genetic design spaces. This platform, named CLASSIC (combinatorial large-scale assembly and short-range sequencing for investigating genetic complexity), offers a novel opportunity to generate genotype- to-phenotype (G2P) maps for hundreds of thousands of multi-kilobase genetic circuits in a single experiment. We show proof-of-concept for this platform and leverage the unique ability to assay genetic diversity to optimize the performance of single-input genetic switches in mammalian cells. Additionally, we show that the CLASSIC platform can be adapted to enable image-based G2P mapping of diverse features of cellular identity and phenotype, including protein compartmentalization and cell morphology, and interactions between engineered cells in multi-cellular environments, such as T cell killing.