de Boer, C. G. et al. Deciphering eukaryotic gene-regulatory logic with 100 million random promoters. Nat. Biotechnol. 38, 56–65 (2020).
Castillo-Hair, S. et al. Optimizing 5′UTRs for mRNA-delivered gene editing using deep learning. Nat. Commun. 15, 5284 (2024).
Angenent-Mari, N. M., Garruss, A. S., Soenksen, L. R., Church, G. & Collins, J. J. A deep learning approach to programmable RNA switches. Nat. Commun. 11, 5057 (2020).
Sahu, B. et al. Sequence determinants of human gene regulatory elements. Nat. Genet. 54, 283–294 (2022).
Jones, E. M. et al. Structural and functional characterization of G protein-coupled receptors with deep mutational scanning. eLife 9, e54895 (2020).
Zhang, C., Tsoi, R. & You, L. Addressing biological uncertainties in engineering gene circuits. Integr. Biol. 8, 456–464 (2016).
Kitano, S., Lin, C., Foo, J. L. & Chang, M. W. Synthetic biology: learning the way toward high-precision biological design. PLoS Biol. 21, e3002116 (2023).
English, M. A., Gayet, R. V. & Collins, J. J. Designing biological circuits: synthetic biology within the operon model and beyond. Annu. Rev. Biochem. 90, 221–244 (2021).
Mahata, B. et al. Compact engineered human mechanosensitive transactivation modules enable potent and versatile synthetic transcriptional control. Nature Methods 20, 1716–1728 (2023).
Slusarczyk, A. L., Lin, A. & Weiss, R. Foundations for the design and implementation of synthetic genetic circuits. Nat. Rev. Genet. 13, 406–420 (2012).
Bashor, C. J. & Collins, J. J. Understanding biological regulation through synthetic biology. Annu. Rev. Biophys. 47, 399–423 (2018).
Bashor, C. J., Hilton, I. B., Bandukwala, H., Smith, D. M. & Veiseh, O. Engineering the next generation of cell-based therapeutics. Nat. Rev. Drug Discov. 21, 655–675 (2022).
Beitz, A. M., Oakes, C. G. & Galloway, K. E. Synthetic gene circuits as tools for drug discovery. Trends Biotechnol. 40, 210–225 (2022).
Kitada, T., DiAndreth, B., Teague, B. & Weiss, R. Programming gene and engineered-cell therapies with synthetic biology. Science 359, eaad1067 (2018).
Cameron, D. E., Bashor, C. J. & Collins, J. J. A brief history of synthetic biology. Nat. Rev. Microbiol. 12, 381–390 (2014).
Yeung, E. et al. Biophysical constraints arising from compositional context in synthetic gene networks. Cell Syst. 5, 11–24 (2017).
Lou, C., Stanton, B., Chen, Y. J., Munsky, B. & Voigt, C. A. Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nat. Biotechnol. 30, 1137–1142 (2012).
Muller, I. E. et al. Gene networks that compensate for crosstalk with crosstalk. Nat. Commun. 10, 4028 (2019).
Kinney, J. B., Murugan, A., Callan, C. G. Jr. & Cox, E. C. Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence. Proc. Natl Acad. Sci. USA 107, 9158–9163 (2010).
Sharon, E. et al. Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat. Biotechnol. 30, 521–530 (2012).
Kosuri, S. et al. Composability of regulatory sequences controlling transcription and translation in Escherichia coli. Proc. Natl Acad. Sci. USA 110, 14024–14029 (2013).
Taskiran, I. I. et al. Cell-type-directed design of synthetic enhancers. Nature 626, 212–220 (2024).
Gosai, S. J. et al. Machine-guided design of cell-type-targeting cis-regulatory elements. Nature 634, 1211–1220 (2024).
Agarwal, V. et al. Massively parallel characterization of transcriptional regulatory elements. Nature 639, 411–420 (2025).
Bogard, N., Linder, J., Rosenberg, A. B. & Seelig, G. A deep neural network for predicting and engineering alternative polyadenylation. Cell 178, 91–106 (2019).
Khoroshkin, M. et al. A generative framework for enhanced cell-type specificity in rationally designed mRNAs. Preprint at bioRxiv https://doi.org/10.1101/2024.12.31.630783 (2024).
Gera, T., Jonas, F., More, R. & Barkai, N. Evolution of binding preferences among whole-genome duplicated transcription factors. eLife 11, e73225 (2022).
DelRosso, N. et al. Large-scale mapping and mutagenesis of human transcriptional effector domains. Nature 616, 365–372 (2023).
Zhou, Y. et al. Encoding genetic circuits with DNA barcodes paves the way for machine learning-assisted metabolite biosensor response curve profiling in yeast. ACS Synth. Biol. 11, 977–989 (2022).
Wong, A. S., Choi, G. C., Cheng, A. A., Purcell, O. & Lu, T. K. Massively parallel high-order combinatorial genetics in human cells. Nat. Biotechnol. 33, 952–961 (2015).
Matreyek, K. A. et al. Multiplex assessment of protein variant abundance by massively parallel sequencing. Nat. Genet. 50, 874–882 (2018).
Liu, H. et al. Magic pools: parallel assessment of transposon delivery vectors in bacteria. mSystems 3, e00143-17 (2018).
Weber, E., Engler, C., Gruetzner, R., Werner, S. & Marillonnet, S. A modular cloning system for standardized assembly of multigene constructs. PLoS ONE 6, e16765 (2011).
Duportet, X. et al. A platform for rapid prototyping of synthetic gene networks in mammalian cells. Nucleic Acids Res. 42, 13440–13451 (2014).
Petitclerc, D. et al. The effect of various introns and transcription terminators on the efficiency of expression vectors in various cultured cell lines and in the mammary gland of transgenic mice. J. Biotechnol. 40, 169–178 (1995).
Khalil, A. S. et al. A synthetic biology framework for programming eukaryotic transcription functions. Cell 150, 647–658 (2012).
Maeder, M. L., Thibodeau-Beganny, S., Sander, J. D., Voytas, D. F. & Joung, J. K. Oligomerized pool engineering (OPEN): an ‘open-source’ protocol for making customized zinc-finger arrays. Nat. Protoc. 4, 1471–1501 (2009).
Li, H. S. et al. Multidimensional control of therapeutic human cell function with synthetic gene circuits. Science 378, 1227–1234 (2022).
Feil, R., Wagner, J., Metzger, D. & Chambon, P. Regulation of Cre recombinase activity by mutated estrogen receptor ligand-binding domains. Biochem. Biophys. Res. Commun. 237, 752–757 (1997).
Bashor, C. J. et al. Complex signal processing in synthetic gene circuits using cooperative regulatory assemblies. Science 364, 593–597 (2019).
Donahue, P. S. et al. The COMET toolkit for composing customizable genetic programs in mammalian cells. Nat. Commun. 11, 779 (2020).
Muldoon, J. J. et al. Model-guided design of mammalian genetic programs. Sci. Adv. 7, eabe9375 (2021).
Kabadi, A. M. & Gersbach, C. A. Engineering synthetic TALE and CRISPR/Cas9 transcription factors for regulating gene expression. Methods 69, 188–197 (2014).
La Russa, M. F. & Qi, L. S. The new state of the art: Cas9 for gene activation and repression. Mol. Cell. Biol. 35, 3800–3809 (2015).
Sadowski, I., Ma, J., Triezenberg, S. & Ptashne, M. GAL4-VP16 is an unusually potent transcriptional activator. Nature 335, 563–564 (1988).
Shin, Y. et al. Spatiotemporal control of intracellular phase transitions using light-activated optoDroplets. Cell 168, 159–171 (2017).
Schneider, N. et al. Liquid-liquid phase separation of light-inducible transcription factors increases transcription activation in mammalian cells and mice. Sci. Adv. 7, eabd3568 (2021).
Gossen, M. & Bujard, H. Tight control of gene expression in mammalian cells by tetracycline-responsive promoters. Proc. Natl Acad. Sci. USA 89, 5547–5551 (1992).
Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).
Tycko, J. et al. Development of compact transcriptional effectors using high-throughput measurements in diverse contexts. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02442-6 (2024).
Tague, E. P., Dotson, H. L., Tunney, S. N., Sloas, D. C. & Ngo, J. T. Chemogenetic control of gene expression and cell signaling with antiviral drugs. Nat. Methods 15, 519–522 (2018).
Jiang, K. et al. Rapid in silico directed evolution by a protein language model with EVOLVEpro. Science 387, eadr6006 (2025).
Lin, J., Luo, R. & Pinello, L. EPInformer: a scalable deep learning framework for gene expression prediction by integrating promoter-enhancer sequences with multimodal epigenomic data. Preprint at bioRxiv https://doi.org/10.1101/2024.08.01.606099 (2024).
Wimmer, E., Mueller, S., Tumpey, T. M. & Taubenberger, J. K. Synthetic viruses: a new opportunity to understand and prevent viral disease. Nat. Biotechnol. 27, 1163–1172 (2009).
Brophy, J. A. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).
Pinglay, S. et al. Synthetic regulatory reconstitution reveals principles of mammalian Hox cluster regulation. Science 377, eabk2820 (2022).
Voigt, C. A. Synthetic biology 2020-2030: six commercially-available products that are changing our world. Nat. Commun. 11, 6379 (2020).
Valeri, J. A. et al. Sequence-to-function deep learning frameworks for engineered riboregulators. Nat. Commun. 11, 5058 (2020).
Hollerer, S. et al. Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping. Nat. Commun. 11, 3551 (2020).
Rai, K., Wang, Y., O’Connell, R. W., Patel, A. B. & Bashor, C. J. Using machine learning to enhance and accelerate synthetic biology. Curr. Opin. Biomed. Eng. 31, 100553 (2024).
Karst, S. M. et al. High-accuracy long-read amplicon sequences using unique molecular identifiers with Nanopore or PacBio sequencing. Nat. Methods 18, 165–169 (2021).
Chung, C. T., Niemela, S. L. & Miller, R. H. One-step preparation of competent Escherichia coli: transformation and storage of bacterial cells in the same solution. Proc. Natl Acad. Sci. USA 86, 2172–2175 (1989).
Parrish, J. R. et al. High-throughput cloning of Campylobacter jejuni ORfs by in vivo recombination in Escherichia coli. J. Proteome Res. 3, 582–586 (2004).
Currin, A. et al. Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries. Synth. Biol. 4, ysz025 (2019).
De Coster, W., Weissensteiner, M. H. & Sedlazeck, F. J. Towards population-scale long-read sequencing. Nat. Rev. Genet. 22, 572–587 (2021).
Smith, T. F. & Waterman, M. S. Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981).
Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).
Hermann, M. et al. Binary recombinase systems for high-resolution conditional mutagenesis. Nucleic Acids Res. 42, 3894–3907 (2014).
