Ultra-high throughput mapping of genetic design space

Area of Science:

• Synthetic Biology
• Genomics
• Molecular Biology

Background:

• Current genetic screening methods are limited to short DNA sequences, hindering the analysis of complex, multi-kilobase genetic constructs.
• Understanding sequence-to-function relationships across large scales is crucial for advancing synthetic biology and designing novel gene circuits.

Purpose of the Study:

• To introduce CLASSIC, a novel genetic screening platform designed for high-throughput (HT) assessment of DNA constructs with arbitrary lengths.
• To enable quantitative analysis of gene circuit designs at scales previously inaccessible to HT assays, facilitating the discovery of composition-to-function mappings.

Main Methods:

• CLASSIC integrates long- and short-read next-generation sequencing (NGS) technologies.
• The platform quantitatively assesses pooled libraries of DNA constructs, including complex gene circuits ranging from 6-9 kb.
• Statistical inference and machine learning (ML) approaches are employed to analyze the generated data.

Main Results:

• CLASSIC successfully measured expression profiles for over 10^5 drug-inducible gene circuit designs in a single experiment in human cells.
• The data enabled predictive modeling of the entire circuit design landscape, revealing underlying genetic design principles.
• The platform demonstrated significant augmentation of throughput and understanding within design-build-test-learn (DBTL) cycles.

Conclusions:

• CLASSIC significantly expands the scale and pace of synthetic biology research by enabling HT screening of large genetic constructs.
• The platform provides an experimental foundation for data-driven design of complex genetic systems and establishes genetic part composability rules.
• CLASSIC accelerates the discovery of optimized gene circuit variants and advances the field of synthetic biology.