In a groundbreaking advance that promises to accelerate the engineering of biological systems, researchers have unveiled CLASSIC, a revolutionary platform that marries long- and short-read sequencing technologies to probe genetic circuits of unprecedented complexity. This new approach tackles a key bottleneck in synthetic biology: the ability to systematically analyze the function of multi-kilobase DNA assemblies containing diverse combinations of genetic parts. By enabling ultra-high-throughput functional profiling of extensive gene constructs, CLASSIC opens the door to comprehensive data-driven design of synthetic genetic networks with far-reaching applications.
Biological systems operate through intricate networks of genetic elements—promoters, enhancers, untranslated regions, coding sequences, and terminators—that interact over large genomic distances to dictate cellular behavior. Until now, massively parallel genetic screening techniques have primarily focused on short DNA fragments, usually no longer than a few hundred base pairs. Consequently, existing methods fall short when attempting to understand how combinations of parts arranged over thousands of bases influence gene circuit output. CLASSIC bridges this gap by leveraging the complementary strengths of next-generation sequencing modalities to quantitatively interrogate pools of synthetic constructs spanning 5 to 20 kilobases in length.
The innovation at the heart of the CLASSIC platform lies in its integration of long-read sequencing, which captures full-length construct identity, with high-accuracy short-read sequencing, which measures gene expression outputs associated with each design. This dual approach enables precise linking of complex genetic configurations to their functional phenotypes at an unprecedented scale. The authors demonstrate that CLASSIC can assay over 100,000 distinct gene circuit designs within a single human-cell-based experiment, dramatically expanding the accessible genetic design landscape.
Synthetic biology has long aspired to rationally engineer biological circuits by combining characterized genetic parts; however, scant data exists regarding how parts compose when arranged in elaborate architectures. By generating large datasets capturing expression profiles of diverse multi-part constructs, CLASSIC provides the empirical foundation necessary to train advanced machine learning models. These models can predict gene circuit behavior across vast design spaces, extracting composability rules that govern how part combinations influence overall function. This predictive power stands to transform the trial-and-error nature of genetic circuit design into a more efficient, data-driven engineering discipline.
Importantly, the practical implications of CLASSIC extend beyond basic research. Synthetic biology applications—ranging from metabolic engineering and therapeutic gene circuits to biosensors and programmable cells—require reliable, predictable design of multi-component genetic systems. CLASSIC’s capacity to rapidly evaluate hundreds of thousands of complex designs empowers researchers to explore an expanded design space and identify optimized circuit variants much more swiftly than previously possible. This leap in throughput and resolution could accelerate the development pipeline for synthetic biology innovations that directly impact medicine, agriculture, and biotechnology.
The study also exemplifies how integrating cutting-edge sequencing technologies can unlock new experimental capabilities. Long-read sequencing platforms, such as those pioneered by Pacific Biosciences and Oxford Nanopore Technologies, have matured to reliably read thousands of bases in a single molecule, while short-read sequencing technologies maintain superior accuracy and scale. By combining these complementary strengths, CLASSIC establishes a framework that overcomes limitations inherent to each method when used alone. This strategy serves as a template for future methodological advances across genomics and synthetic biology.
Another compelling aspect of this research is how CLASSIC facilitates a deeper understanding of genetic “composition to function” relationships. The platform’s high-dimensional datasets enable systematic investigation of how genetic parts functionally interact within complex arrangements, revealing context-dependent effects invisible to traditional approaches focused on isolated elements. These insights provide a roadmap to design biological circuits with predictable and tunable behaviors, moving synthetic biology toward the robustness and reliability akin to electronic circuit engineering.
Moreover, the scalability of CLASSIC could democratize access to data-rich genetic circuit design, enhancing collaborative opportunities and innovation. By exponentially scaling the throughput of functional screens without sacrificing resolution, the method reduces the resource barrier for laboratories seeking to analyze complex synthetic constructs. The ability to generate expansive, high-quality functional datasets invites synergistic collaborations between experimentalists and computational modelers, fostering advances in both biological understanding and algorithm development.
The approach outlined in this study not only advances synthetic biology but also has potential ramifications for understanding natural biological complexity. Many natural gene regulatory networks operate via long-range interactions and multi-part compositions that govern gene expression dynamics. CLASSIC’s capacity to experimentally dissect such complexity on a large scale offers a new avenue to explore fundamental questions in gene regulation, chromatin organization, and epigenetics, complementing in vivo studies.
Critical to the success of CLASSIC is the careful design of genetic libraries and experimental workflows that optimize coverage and interpretability. The team employed sophisticated cloning strategies and barcode indexing schemes to maintain fidelity and traceability of complex multi-part constructs through sequencing and expression assays. Their meticulous validation steps and computational pipelines ensured accurate linkage of sequence data with functional readouts, establishing robust genotype-phenotype mappings integral to downstream model training and analysis.
Looking forward, this technology promises to catalyze a new era of high-throughput synthetic biology, integrating experimental and computational modalities into iterative design-build-test-learn cycles of unprecedented scale and precision. By greatly expanding the throughput and length scale of functional screens, CLASSIC sets the stage for accelerated discovery of genetic circuit design principles, ultimately enabling the rational engineering of biological systems with predictable, programmable behaviors for diverse applications.
This study represents a landmark contribution to the field of synthetic biology, demonstrating a powerful new screening platform that unlocks complex genetic design spaces and generates rich datasets to fuel machine learning-guided design. As synthetic biology continues evolving toward increasingly complex and functional biological systems, innovations like CLASSIC will be indispensable tools to harness the full potential of genetic engineering to address diverse challenges in health, environment, and industry.
Subject of Research:
Ultra-high-throughput functional profiling of complex gene circuits through combined long- and short-read sequencing technologies.
Article Title:
Ultra-high-throughput mapping of genetic design space.
Article References:
Rai, K., O’Connell, R.W., Piepergerdes, T.C. et al. Ultra-high-throughput mapping of genetic design space. Nature (2026). https://doi.org/10.1038/s41586-025-09933-9
Image Credits:
AI Generated
DOI:
https://doi.org/10.1038/s41586-025-09933-9
Tags: advancements in synthetic genetic engineeringCLASSIC platform for synthetic biologycomprehensive analysis of gene circuit outputdata-driven design of synthetic networksfunctional profiling of gene constructsgenetic circuits complexity analysisgenetic elements interactionlong-read sequencing technologymassively parallel genetic screening techniquesmulti-kilobase DNA assembliesshort-read sequencing integrationUltra-high-throughput genetic design
