Engineering wetware and software for the predictive design of compressed genetic circuits for higher-state decision-making

Brian D. Huang, Yongjoon Yu, Junghwan Lee, Matthew W. Repasky, Yao Xie, Matthew J. Realff and Corey J. Wilson ()
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Brian D. Huang: Georgia Institute of Technology
Yongjoon Yu: Georgia Institute of Technology
Junghwan Lee: Georgia Institute of Technology
Matthew W. Repasky: Georgia Institute of Technology
Yao Xie: Georgia Institute of Technology
Matthew J. Realff: Georgia Institute of Technology
Corey J. Wilson: Georgia Institute of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Synthetic genetic circuits enable the reprogramming of cells, advancing the study and application of biology with greater precision. However, quantitative circuit design is hampered by the limited modularity of biological parts. As circuit complexity increases, this imposes a greater metabolic burden on chassis cells, which limits circuit design capacity. Here, we present a generalizable wetware and complementary software to enable the quantitative design of genetic circuits that utilize fewer parts for higher-state decision-making. We term the process of designing smaller genetic circuits as compression. To accomplish this, we develop scalable wetware that leverages sets of synthetic transcription factors (i.e., network capable repressors and anti-repressors) and synthetic promoters that facilitate the full development of 3-input Boolean logic compression circuits. Complementary software enables the design of higher-state circuits with a minimal genetic footprint and quantitatively precise performance setpoints. On average the resulting multi-state compression circuits are approximately 4-times smaller than canonical inverter-type genetic circuits. Our quantitative predictions have an average error below 1.4-fold for >50 test cases. Additionally, we successfully apply this technology toward the predictive design of a recombinase genetic memory circuit, and flux through a metabolic pathway with precise setpoints.

Date: 2025
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DOI: 10.1038/s41467-025-64457-0

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