Modular Scalable Synthetic Gene Circuits for Complex Functions Within Minimal Computational Layers in Human Cells

Engineering mammalian cells to execute complex genetic programs remains a significant challenge in synthetic biology. Synthetic gene circuits typically implement sophisticated programs through cascaded computational layers. However, these architectures require numerous orthogonal parts, increase genetic payload, and deplete cellular resources, thereby limiting functionality and scalability. ‏ ‏We present a modular design framework for engineering scalable gene circuits that execute complex functions within fewer computational layers. The platform integrates orthogonal trans-splicing-based AND gates, native-synthetic hybrid promoters for tunable regulation, and synthetic microRNAs that implement inhibitory logic. Using this approach, we engineer complex circuits, including a three-input combinatorial logic gate, a half adder, a full adder, and a dynamic 3-to-1 multiplexer with a dedicated Selector Overload Status output, generated only when both selector inputs are activated. By minimizing the number of computational layers while maintaining functionality, this strategy addresses scalability barriers in gene circuit engineering and expands applicability for biomedicine, biotechnology, and fundamental biology. Synthetic gene circuits enable cellular programming, but executing complex functions in mammalian cells remains challenging. Here, the authors introduce RNA-based circuit designs that enable logic gates, adders, and a multiplexer in fewer computational layers, addressing scalability barriers.

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Publication Details

Journal
Nature Communications
Published
2026-06-16
DOI
https://doi.org/10.1038/s41467-026-74408-y
Primary Topic
Gene Regulatory Network Analysis
Type
article
Field-Weighted Citation Impact
0.00

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article

Modular Scalable Synthetic Gene Circuits for Complex Functions Within Minimal Computational Layers in Human Cells

Odelia Mouhadeb, Tamar Aminov, Hadas Weinstein-Marom, Lior Nissim et al.
Nature Communications
Gene Regulatory Network Analysis
article

Modular Scalable Synthetic Gene Circuits for Complex Functions Within Minimal Computational Layers in Human Cells

Odelia Mouhadeb, Tamar Aminov, Hadas Weinstein-Marom, Lior Nissim, Ilanit Kovalski, Keren Roas
article en

Abstract

Engineering mammalian cells to execute complex genetic programs remains a significant challenge in synthetic biology. Synthetic gene circuits typically implement sophisticated programs through cascaded computational layers. However, these architectures require numerous orthogonal parts, increase genetic payload, and deplete cellular resources, thereby limiting functionality and scalability. ‏ ‏We present a modular design framework for engineering scalable gene circuits that execute complex functions within fewer computational layers. The platform integrates orthogonal trans-splicing-based AND gates, native-synthetic hybrid promoters for tunable regulation, and synthetic microRNAs that implement inhibitory logic. Using this approach, we engineer complex circuits, including a three-input combinatorial logic gate, a half adder, a full adder, and a dynamic 3-to-1 multiplexer with a dedicated Selector Overload Status output, generated only when both selector inputs are activated. By minimizing the number of computational layers while maintaining functionality, this strategy addresses scalability barriers in gene circuit engineering and expands applicability for biomedicine, biotechnology, and fundamental biology. Synthetic gene circuits enable cellular programming, but executing complex functions in mammalian cells remains challenging. Here, the authors introduce RNA-based circuit designs that enable logic gates, adders, and a multiplexer in fewer computational layers, addressing scalability barriers.

Nature Communications
Hebrew University of Jerusalem (IL), Migal - Galilee Technology Center (IL), Tel Hai Academic College (IL)
Israel Cancer Research Fund, Israel Science Foundation
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Gene Regulatory Network Analysis
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