Mammalian synthetic gene circuits for biopharmaceutical development & manufacture

mammalian-synthetic-gene-circuits-for-biopharmaceutical-development-&-manufacture
Mammalian synthetic gene circuits for biopharmaceutical development & manufacture
  • Statista. Projected size of the biopharmaceuticals market worldwide from 2020 to 2030*. 2022; Available from: https://www.statista.com/statistics/1293077/global-biopharmaceuticals-market-size/.

  • Walsh, G. & Walsh, E. Biopharmaceutical benchmarks 2022. Nat. Biotechnol. 40, 1722–1760 (2022).

    Google Scholar 

  • FDA, Guidance for Industry PAT – A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance, F.A.D. Administration, Editor. 2004.

  • Osbourn, A. E. et al. Synthetic biology. N. Phytol. 196, 671–677 (2012).

    Google Scholar 

  • Weber, W. & Fussenegger, M. Engineering of synthetic mammalian gene networks. Chem. Biol. 16, 287–297 (2009).

    Google Scholar 

  • Baldwin, G. et al. Synthetic Biology—A Primer (World Scientific Connect, 2012).

  • Hong, J. K. et al. Towards next generation CHO cell line development and engineering by systems approaches. Curr. Opin. Chem. Eng. 22, 1–10 (2018).

    Google Scholar 

  • Liu, Y. et al. Towards next-generation model microorganism chassis for biomanufacturing. Appl. Microbiol. Biotechnol. 104, 1–14 (2020).

    Google Scholar 

  • Doshi, A. et al. Small-molecule inducible transcriptional control in mammalian cells. Crit. Rev. Biotechnol. 40, 1131–1150 (2020).

    Google Scholar 

  • Kallunki, T. et al. How to choose the right inducible gene expression system for mammalian studies? Cells 8, 796 (2019).

  • Goldberger, R. F. Autogenous regulation of gene expression. Science 183, 810–816 (1974).

    Google Scholar 

  • Rosenfeld, N., Elowitz, M. B. & Alon, U. Negative autoregulation speeds the response times of transcription networks. J. Mol. Biol. 323, 785–793 (2002).

    Google Scholar 

  • Ferrell, J. E. Jr. et al. Simple, realistic models of complex biological processes: positive feedback and bistability in a cell fate switch and a cell cycle oscillator. FEBS Lett. 583, 3999–4005 (2009).

    Google Scholar 

  • Osborn, D. P. et al. Cdkn1c drives muscle differentiation through a positive feedback loop with Myod. Dev. Biol. 350, 464–475 (2011).

    Google Scholar 

  • Lee, K. E. et al. Positive feedback loop between Sox2 and Sox6 inhibits neuronal differentiation in the developing central nervous system. Proc. Natl Acad. Sci. USA 111, 2794–2799 (2014).

    Google Scholar 

  • Kueh, H. Y. et al. Positive feedback between PU.1 and the cell cycle controls myeloid differentiation. Science 341, 670–673 (2013).

    Google Scholar 

  • Savageau, M. A. Comparison of classical and autogenous systems of regulation in inducible operons. Nature 252, 546–549 (1974).

    Google Scholar 

  • Dublanche, Y. et al. Noise in transcription negative feedback loops: simulation and experimental analysis. Mol. Syst. Biol. 2, 41 (2006).

    Google Scholar 

  • Nevozhay, D., Zal, T. & Balázsi, G. Transferring a synthetic gene circuit from yeast to mammalian cells. Nat. Commun. 4, 1451 (2013).

    Google Scholar 

  • Nevozhay, D. et al. Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. Proc. Natl. Acad. Sci. USA 106, 5123–5128 (2009).

    Google Scholar 

  • Shimoga, V. et al. Synthetic mammalian transgene negative autoregulation. Mol. Syst. Biol. 9, 670 (2013).

    Google Scholar 

  • Weber, W., Kramer, B. P. & Fussenegger, M. A genetic time-delay circuitry in mammalian cells. Biotechnol. Bioeng. 98, 894–902 (2007).

    Google Scholar 

  • Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000).

    Google Scholar 

  • Stricker, J. et al. A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519 (2008).

    Google Scholar 

  • Tsai, T. Y. et al. Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science 321, 126–129 (2008).

    Google Scholar 

  • Del Vecchio, D., Dy, A. J. & Qian, Y. Control theory meets synthetic biology. J. R. Soc. Interface. 13, 20160380 (2016).

  • Lillacci, G., Benenson, Y. & Khammash, M. Synthetic control systems for high performance gene expression in mammalian cells. Nucleic Acids Res. 46, 9855–9863 (2018).

    Google Scholar 

  • De Carluccio, G., Fusco, V. & di Bernardo, D. Engineering a synthetic gene circuit for high-performance inducible expression in mammalian systems. Nat. Commun. 15, 3311 (2024).

    Google Scholar 

  • Perry, N. & Ninfa, A. J. Synthetic networks: oscillators and toggle switches for Escherichia coli. Methods Mol. Biol. 813, 287–300 (2012).

    Google Scholar 

  • Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    Google Scholar 

  • Auslander, S. & Fussenegger, M. Synthetic RNA-based switches for mammalian gene expression control. Curr. Opin. Biotechnol. 48, 54–60 (2017).

    Google Scholar 

  • Kramer, B. P. et al. An engineered epigenetic transgene switch in mammalian cells. Nat. Biotechnol. 22, 867–870 (2004).

    Google Scholar 

  • Lebar, T. et al. A bistable genetic switch based on designable DNA-binding domains. Nat. Commun. 5, 5007 (2014).

    Google Scholar 

  • Kightlinger, W. et al. Synthetic glycobiology: parts, systems, and applications. ACS Synth. Biol. 9, 1534–1562 (2020).

    Google Scholar 

  • Galvan, S., Teixeira, A. P. & Fussenegger, M. Enhancing cell-based therapies with synthetic gene circuits responsive to molecular stimuli. Biotechnol. Bioeng. 121, 2987–3000 (2024).

    Google Scholar 

  • Teixeira, A. P. & Fussenegger, M. Synthetic gene circuits for regulation of next-generation cell-based therapeutics. Adv. Sci. 11, e2309088 (2024).

    Google Scholar 

  • Teixeira, A. P. & Fussenegger, M. Synthetic macromolecular switches for precision control of therapeutic cell functions. Nat. Rev. Bioeng. 2, 1005–1022 (2024).

    Google Scholar 

  • Tigges, M. et al. A tunable synthetic mammalian oscillator. Nature 457, 309–312 (2009).

    Google Scholar 

  • Rey, G. et al. Genome-wide and phase-specific DNA-binding rhythms of BMAL1 control circadian output functions in mouse liver. PLoS Biol. 9, e1000595 (2011).

    Google Scholar 

  • Ueda, H. R. et al. System-level identification of transcriptional circuits underlying mammalian circadian clocks. Nat. Genet. 37, 187–192 (2005).

    Google Scholar 

  • Ukai-Tadenuma, M., Kasukawa, T. & Ueda, H. R. Proof-by-synthesis of the transcriptional logic of mammalian circadian clocks. Nat. Cell Biol. 10, 1154–1163 (2008).

    Google Scholar 

  • Swinburne, I. A. et al. Intron length increases oscillatory periods of gene expression in animal cells. Genes Dev. 22, 2342–2346 (2008).

    Google Scholar 

  • Purcell, O. et al. A comparative analysis of synthetic genetic oscillators. J. R. Soc. Interface 7, 1503–1524 (2010).

    Google Scholar 

  • Krzysztoń, R. et al. Gene-circuit therapy on the horizon: synthetic biology tools for engineered therapeutics. Acta Biochim. Pol. 68, 377–383 (2021).

    Google Scholar 

  • MacDonald, I. C. & Deans, T. L. Tools and applications in synthetic biology. Adv. Drug Deliv. Rev. 105, 20–34 (2016).

    Google Scholar 

  • Szenk, M., Yim, T. & Balazsi, G. Multiplexed gene expression tuning with orthogonal synthetic gene circuits. ACS Synth. Biol. 9, 930–939 (2020).

    Google Scholar 

  • Weber, W. & Fussenegger, M. Inducible product gene expression technology tailored to bioprocess engineering. Curr. Opin. Biotechnol. 18, 399–410 (2007).

    Google Scholar 

  • Jusiak, B. et al. Engineering synthetic gene circuits in living cells with CRISPR technology. Trends Biotechnol. 34, 535–547 (2016).

    Google Scholar 

  • Kramer, B. P., Fischer, C. & Fussenegger, M. BioLogic gates enable logical transcription control in mammalian cells. Biotechnol. Bioeng. 87, 478–484 (2004).

    Google Scholar 

  • Singh, V. Recent advances and opportunities in synthetic logic gates engineering in living cells. Syst. Synth. Biol. 8, 271–282 (2014).

    Google Scholar 

  • Rinaudo, K. et al. A universal RNAi-based logic evaluator that operates in mammalian cells. Nat. Biotechnol. 25, 795–801 (2007).

    Google Scholar 

  • Matsuura, S. et al. Synthetic RNA-based logic computation in mammalian cells. Nat. Commun. 9, 4847 (2018).

    Google Scholar 

  • Nomura, Y. & Yokobayashi, Y. Aptazyme-based riboswitches and logic gates in mammalian cells. Methods Mol. Biol. 2323, 213–220 (2021).

    Google Scholar 

  • Tabor, J. J. et al. A synthetic genetic edge detection program. Cell 137, 1272–1281 (2009).

    Google Scholar 

  • Mills, E. M. et al. Development of mammalian cell logic gates controlled by unnatural amino acids. Cell Rep. Methods 1, 100073 (2021).

    Google Scholar 

  • Toda, S., Frankel, N. W. & Lim, W. A. Engineering cell-cell communication networks: programming multicellular behaviors. Curr. Opin. Chem. Biol. 52, 31–38 (2019).

    Google Scholar 

  • Basu, S. et al. Spatiotemporal control of gene expression with pulse-generating networks. Proc. Natl Acad. Sci. USA 101, 6355–6360 (2004).

    Google Scholar 

  • Chen, M. T. & Weiss, R. Artificial cell-cell communication in yeast Saccharomyces cerevisiae using signaling elements from Arabidopsis thaliana. Nat. Biotechnol. 23, 1551–1555 (2005).

    Google Scholar 

  • Bacchus, W. et al. Synthetic two-way communication between mammalian cells. Nat. Biotechnol. 30, 991–996 (2012).

    Google Scholar 

  • Braselmann, S., Graninger, P. & Busslinger, M. A selective transcriptional induction system for mammalian cells based on Gal4-estrogen receptor fusion proteins. Proc. Natl. Acad. Sci. USA 90, 1657–1661 (1993).

    Google Scholar 

  • No, D., Yao, T. P. & Evans, R. M. Ecdysone-inducible gene expression in mammalian cells and transgenic mice. Proc. Natl. Acad. Sci. USA 93, 3346–3351 (1996).

    Google Scholar 

  • Oehme, I., Bösser, S. & Zörnig, M. Agonists of an ecdysone-inducible mammalian expression system inhibit Fas Ligand- and TRAIL-induced apoptosis in the human colon carcinoma cell line RKO. Cell Death Differ. 13, 189–201 (2006).

    Google Scholar 

  • Constantino, S. et al. The ecdysone inducible gene expression system: unexpected effects of muristerone A and ponasterone A on cytokine signaling in mammalian cells. Eur. Cytokine Netw. 12, 365–367 (2001).

    Google Scholar 

  • Aranda-Díaz, A. et al. Robust synthetic circuits for two-dimensional control of gene expression in yeast. ACS Synth. Biol. 6, 545–554 (2017).

    Google Scholar 

  • McIsaac, R. S. et al. Synthetic gene expression perturbation systems with rapid, tunable, single-gene specificity in yeast. Nucleic Acids Res. 41, e57 (2013).

    Google Scholar 

  • Ohira, M. J. et al. An estradiol-inducible promoter enables fast, graduated control of gene expression in fission yeast. Yeast 34, 323–334 (2017).

    Google Scholar 

  • Beyer, H. M. et al. Optogenetic control of signaling in mammalian cells. Biotechnol. J. 10, 273–283 (2015).

    Google Scholar 

  • Kolar, K. & Weber, W. Synthetic biological approaches to optogenetically control cell signaling. Curr. Opin. Biotechnol. 47, 112–119 (2017).

    Google Scholar 

  • Mansouri, M., Strittmatter, T. & Fussenegger, M. Light-controlled mammalian cells and their therapeutic applications in synthetic biology. Adv. Sci. 6, 1800952 (2019).

    Google Scholar 

  • Rost, B. R. et al. Optogenetic tools for subcellular applications in neuroscience. Neuron 96, 572–603 (2017).

    Google Scholar 

  • Dwijayanti, A. et al. Toward multiplexed optogenetic circuits. Front. Bioeng. Biotechnol. 9, 804563 (2021).

  • Mansouri, M. & Fussenegger, M. Synthetic biology-based optogenetic approaches to control therapeutic designer cells. Curr. Opin. Syst. Biol. 28, 100396 (2021).

    Google Scholar 

  • Zang, J. et al. Circadian regulation of vertebrate cone photoreceptor function. Elife 10, e68903 (2021).

  • Rivera-Cancel, G., Motta-Mena, L. B. & Gardner, K. H. Identification of natural and artificial DNA substrates for light-activated LOV-HTH transcription factor EL222. Biochemistry 51, 10024–10034 (2012).

    Google Scholar 

  • Jayaraman, P. et al. Blue light-mediated transcriptional activation and repression of gene expression in bacteria. Nucleic Acids Res. 44, 6994–7005 (2016).

    Google Scholar 

  • Fernandez-Rodriguez, J. et al. Engineering RGB color vision into Escherichia coli. Nat. Chem. Biol. 13, 706–708 (2017).

    Google Scholar 

  • Baumschlager, A., Aoki, S. K. & Khammash, M. Dynamic blue light-inducible T7 RNA polymerases (Opto-T7RNAPs) for precise spatiotemporal gene expression control. ACS Synth. Biol. 6, 2157–2167 (2017).

    Google Scholar 

  • Lalwani, M. A. et al. Optogenetic control of the lac operon for bacterial chemical and protein production. Nat. Chem. Biol. 17, 71–79 (2021).

    Google Scholar 

  • Zhao, E. M. et al. Optogenetic amplification circuits for light-induced metabolic control. ACS Synth. Biol. 10, 1143–1154 (2021).

    Google Scholar 

  • Zhao, E. M. et al. Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature 555, 683–687 (2018).

    Google Scholar 

  • Gebel, J. et al. Potent optogenetic regulation of gene expression in mammalian cells for bioproduction and basic research. Nucleic Acids Res. 53, gkaf546 (2025).

  • Chang, M. M. et al. Small-molecule control of antibody N-glycosylation in engineered mammalian cells. Nat. Chem. Biol. 15, 730–736 (2019).

    Google Scholar 

  • Mullick, A. et al. The cumate gene-switch: a system for regulated expression in mammalian cells. BMC Biotechnol. 6, 43 (2006).

    Google Scholar 

  • Poulain, A. et al. Rapid protein production from stable CHO cell pools using plasmid vector and the cumate gene-switch. J. Biotechnol. 255, 16–27 (2017).

    Google Scholar 

  • Auslander, S. & Fussenegger, M. Engineering gene circuits for mammalian cell-based applications. Cold Spring Harb Perspect Biol. 8, a023895 (2016).

  • 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).

    Google Scholar 

  • Krueger, C. et al. Tetracycline derivatives: alternative effectors for Tet transregulators. Biotechniques 37, 546, 548, 550 (2004).

  • Stanton, B. C. et al. Systematic transfer of prokaryotic sensors and circuits to mammalian cells. ACS Synth. Biol. 3, 880–891 (2014).

    Google Scholar 

  • Farquhar, K. S. et al. Role of network-mediated stochasticity in mammalian drug resistance. Nat. Commun. 10, 2766 (2019).

    Google Scholar 

  • Mak, A. N. et al. TAL effectors: function, structure, engineering and applications. Curr. Opin. Struct. Biol. 23, 93–99 (2013).

    Google Scholar 

  • Moscou, M. J. & Bogdanove, A. J. A simple cipher governs DNA recognition by TAL effectors. Science 326, 1501 (2009).

    Google Scholar 

  • Mercer, A. C. et al. Regulation of endogenous human gene expression by ligand-inducible TALE transcription factors. ACS Synth. Biol. 3, 723–730 (2014).

    Google Scholar 

  • Konermann, S. et al. Optical control of mammalian endogenous transcription and epigenetic states. Nature 500, 472–476 (2013).

    Google Scholar 

  • Li, Y. et al. Transcription activator-like effector hybrids for conditional control and rewiring of chromosomal transgene expression. Sci. Rep. 2, 897 (2012).

    Google Scholar 

  • Zhao, C. et al. Multiple chemical inducible tal effectors for genome editing and transcription activation. ACS Chem. Biol. 13, 609–617 (2018).

    Google Scholar 

  • Black, J. B., Perez-Pinera, P. & Gersbach, C. A. Mammalian synthetic biology: engineering biological systems. Annu Rev. Biomed. Eng. 19, 249–277 (2017).

    Google Scholar 

  • Malgieri, G. et al. The prokaryotic zinc-finger: structure, function and comparison with the eukaryotic counterpart. FEBS J. 282, 4480–4496 (2015).

    Google Scholar 

  • Martínez-Gálvez, G. et al. Deploying MMEJ using MENdel in precision gene editing applications for gene therapy and functional genomics. Nucleic Acids Res 49, 67–78 (2021).

    Google Scholar 

  • Dent, C. L. et al. Regulation of endogenous gene expression using small molecule-controlled engineered zinc-finger protein transcription factors. Gene Ther. 14, 1362–1369 (2007).

    Google Scholar 

  • Magnenat, L., Schwimmer, L. J. & Barbas, C. F. 3rd, Drug-inducible and simultaneous regulation of endogenous genes by single-chain nuclear receptor-based zinc-finger transcription factor gene switches. Gene Ther. 15, 1223–1232 (2008).

    Google Scholar 

  • Wolfe, S. A., Nekludova, L. & Pabo, C. O. DNA recognition by Cys2His2 zinc finger proteins. Annu. Rev. Biophys. Biomol. Struct. 29, 183–212 (2000).

    Google Scholar 

  • Gersbach, C. A., Gaj, T. & Barbas, C. F. Synthetic zinc finger proteins: the advent of targeted gene regulation and genome modification technologies. Acc. Chem. Res. 47, 2309–2318 (2014).

    Google Scholar 

  • Qi, L. S. et al. Repurposing CRISPR as an RNA-guided platform for sequence-specific control of gene expression. Cell 152, 1173–1183 (2013).

    Google Scholar 

  • Mahas, A., Stewart, C. N. eal Jr. & Mahfouz, M. M. Harnessing CRISPR/Cas systems for programmable transcriptional and post-transcriptional regulation. Biotechnol. Adv. 36, 295–310 (2018). p.

    Google Scholar 

  • Xu, X. & Qi, L. S. A CRISPR-dCas toolbox for genetic engineering and synthetic biology. J. Mol. Biol. 431, 34–47 (2019).

    Google Scholar 

  • Gao, Y. et al. Complex transcriptional modulation with orthogonal and inducible dCas9 regulators. Nat. Methods 13, 1043–1049 (2016).

    Google Scholar 

  • Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

    Google Scholar 

  • Lu, J. et al. Multimode drug inducible CRISPR/Cas9 devices for transcriptional activation and genome editing. Nucleic Acids Res. 46, e25 (2018).

    Google Scholar 

  • Bao, Z. et al. Orthogonal genetic regulation in human cells using chemically induced CRISPR/Cas9 activators. ACS Synth. Biol. 6, 686–693 (2017).

    Google Scholar 

  • Kleinjan, D. A. et al. Drug-tunable multidimensional synthetic gene control using inducible degron-tagged dCas9 effectors. Nat. Commun. 8, 1191 (2017).

    Google Scholar 

  • Chen, W. C. W. et al. A synthetic transcription platform for programmable gene expression in mammalian cells. Nat. Commun. 13, 6167 (2022).

    Google Scholar 

  • Ricci, C. G. et al. Deciphering off-target effects in CRISPR-Cas9 through accelerated molecular dynamics. ACS Cent. Sci. 5, 651–662 (2019).

    Google Scholar 

  • Zhang, X. H. et al. Off-target Effects in CRISPR/Cas9-mediated Genome Engineering. Mol. Ther. Nucleic Acids 4, e264 (2015).

    Google Scholar 

  • Auslander, S. et al. A general design strategy for protein-responsive riboswitches in mammalian cells. Nat. Methods 11, 1154–1160 (2014).

    Google Scholar 

  • Breaker, R. R. Riboswitches: from ancient gene-control systems to modern drug targets. Future Microbiol. 4, 771–773 (2009).

    Google Scholar 

  • Hanson, S. et al. Tetracycline-aptamer-mediated translational regulation in yeast. Mol. Microbiol. 49, 1627–1637 (2003).

    Google Scholar 

  • Mironov, A. S. et al. Sensing small molecules by nascent RNA: a mechanism to control transcription in bacteria. Cell 111, 747–756 (2002).

    Google Scholar 

  • Suess, B. et al. A theophylline responsive riboswitch based on helix slipping controls gene expression in vivo. Nucleic Acids Res. 32, 1610–1614 (2004).

    Google Scholar 

  • Dykstra, P. B., Kaplan, M. & Smolke, C. D. Engineering synthetic RNA devices for cell control. Nat. Rev. Genet 23, 215–228 (2022).

    Google Scholar 

  • Ge, H. & Marchisio, M. A. Aptamers, riboswitches, and ribozymes in S. cerevisiae Synthetic Biology. Life 11, 248 (2021).

  • Wieland, M., Auslander, D. & Fussenegger, M. Engineering of ribozyme-based riboswitches for mammalian cells. Methods 56, 351–357 (2012).

    Google Scholar 

  • Menon, A. et al. miRNA: a promising therapeutic target in cancer. Int. J. Mol. Sci. 2022. 23, 11502 (2022).

  • Klingler, F. et al. A novel system for glycosylation engineering by natural and artificial miRNAs. Metab. Eng. 77, 53–63 (2023).

    Google Scholar 

  • Domin, G. et al. Applicability of a computational design approach for synthetic riboswitches. Nucleic Acids Res. 45, 4108–4119 (2017).

    Google Scholar 

  • Ono, H., Kawasaki, S. & Saito, H. Orthogonal protein-responsive mrna switches for mammalian synthetic biology. ACS Synth. Biol. 9, 169–174 (2020).

    Google Scholar 

  • Wang, L. Z. et al. Build to understand: synthetic approaches to biology. Integr. Biol. 8, 394–408 (2016).

    Google Scholar 

  • Haseltine, E. L. & Arnold, F. H. Synthetic gene circuits: design with directed evolution. Annu Rev. Biophys. Biomol. Struct. 36, 1–19 (2007).

    Google Scholar 

  • MacDonald, J. T. et al. Computational design approaches and tools for synthetic biology. Integr. Biol. 3, 97–108 (2011).

    Google Scholar 

  • Koh, G. & Lee, D. Y. Mathematical modeling and sensitivity analysis of the integrated TNFalpha-mediated apoptotic pathway for identifying key regulators. Comput. Biol. Med. 41, 512–528 (2011).

    Google Scholar 

  • Shao, H. et al. Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effects. PLoS ONE 8, e80832 (2013).

    Google Scholar 

  • Sun, X. et al. Systems modeling of anti-apoptotic pathways in prostate cancer: psychological stress triggers a synergism pattern switch in drug combination therapy. PLoS Comput. Biol. 9, e1003358 (2013).

    Google Scholar 

  • Karlebach, G. & Shamir, R. Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008).

    Google Scholar 

  • Franceschini, G. & Macchietto, S. Model-based design of experiments for parameter precision: state of the art. Chem. Eng. Sci. 63, 4846–4872 (2008).

    Google Scholar 

  • Huang, C., Cattani, F. & Galvanin, F. An optimal experimental design strategy for improving parameter estimation in stochastic models. Comput. Chem. Eng. 170, 108133 (2023).

    Google Scholar 

  • Zheng, Y. & Sriram, G. Mathematical modeling: bridging the gap between concept and realization in synthetic biology. J. Biomed. Biotechnol. 2010, 541609 (2010).

    Google Scholar 

  • Chen, S. et al. Building robust functionality in synthetic circuits using engineered feedback regulation. Curr. Opin. Biotechnol. 24, 790–796 (2013).

    Google Scholar 

  • Bleris, L. et al. Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template. Mol. Syst. Biol. 7, 519 (2011).

    Google Scholar 

  • Jones, R. D. et al. An endoribonuclease-based feedforward controller for decoupling resource-limited genetic modules in mammalian cells. Nat. Commun. 11, 5690 (2020).

    Google Scholar 

  • Frei, T. et al. Characterization and mitigation of gene expression burden in mammalian cells. Nat. Commun. 11, 4641 (2020).

    Google Scholar 

  • Segall-Shapiro, T. H., Sontag, E. D. & Voigt, C. A. Engineered promoters enable constant gene expression at any copy number in bacteria. Nat. Biotechnol. 36, 352–358 (2018).

    Google Scholar 

  • Brown, A. J. & James, D. C. Precision control of recombinant gene transcription for CHO cell synthetic biology. Biotechnol. Adv. 34, 492–503 (2016).

    Google Scholar 

  • Cartwright, J. F. et al. A platform for context-specific genetic engineering of recombinant protein production by CHO cells. J. Biotechnol. 312, 11–22 (2020).

    Google Scholar 

  • Pybus, L. P. et al. Model-directed engineering of “difficult-to-express” monoclonal antibody production by Chinese hamster ovary cells. Biotechnol. Bioeng. 111, 372–385 (2014).

    Google Scholar 

  • Schlatter, S. et al. On the optimal ratio of heavy to light chain genes for efficient recombinant antibody production by CHO cells. Biotechnol. Prog. 21, 122–133 (2005).

    Google Scholar 

  • Ho, S. C. L. et al. Control of IgG LC:HC ratio in stably transfected CHO cells and study of the impact on expression, aggregation, glycosylation and conformational stability. J. Biotechnol. 165, 157–166 (2013).

    Google Scholar 

  • Carillo, S. et al. Intact multi-attribute method (iMAM): a flexible tool for the analysis of monoclonal antibodies. Eur. J. Pharm. Biopharm. 177, 241–248 (2022).

    Google Scholar 

  • Millan-Martin, S. et al. Comprehensive multi-attribute method workflow for biotherapeutic characterization and current good manufacturing practices testing. Nat. Protoc. 18, 1056–1089 (2023).

    Google Scholar 

  • Jimenez Del Val, I., Fan, Y. & Weilguny, D. Dynamics of immature mAb glycoform secretion during CHO cell culture: an integrated modelling framework. Biotechnol. J. 11, 610–623 (2016).

    Google Scholar 

  • Kotidis, P. et al. Model-based optimization of antibody galactosylation in CHO cell culture. Biotechnol. Bioeng. 116, 1612–1626 (2019).

    Google Scholar 

  • Re, A. Synthetic gene expression circuits for designing precision tools in oncology. Front. Cell Dev. Biol. 5, 77 (2017).

  • Sakemura, R. et al. A tet-on inducible system for controlling CD19-chimeric antigen receptor expression upon drug administration. Cancer Immunol. Res. 4, 658–668 (2016).

    Google Scholar 

  • Barrett, J. A. et al. Regulated intratumoral expression of IL-12 using a RheoSwitch Therapeutic System(®) (RTS(®)) gene switch as gene therapy for the treatment of glioma. Cancer Gene Ther. 25, 106–116 (2018).

    Google Scholar 

  • Monteys, A. M. et al. Regulated control of gene therapies by drug-induced splicing. Nature 596, 291–295 (2021).

    Google Scholar 

  • Tang, Q. et al. Two-plasmid packaging system for recombinant adeno-associated virus. Biores Open Access 9, 219–228 (2020).

    Google Scholar 

  • Clement, N. & Grieger, J. C. Manufacturing of recombinant adeno-associated viral vectors for clinical trials. Mol. Ther. Methods Clin. Dev. 3, 16002 (2016).

    Google Scholar 

  • van der Loo, J. C. & Wright, J. F. Progress and challenges in viral vector manufacturing. Hum. Mol. Genet. 25, R42–R52 (2016).

    Google Scholar 

  • Selvaraj, N. et al. Detailed protocol for the novel and scalable viral vector upstream process for AAV gene therapy manufacturing. Hum. Gene Ther. 32, 850–861 (2021).

    Google Scholar 

  • Adamson-Small, L. et al. A scalable method for the production of high-titer and high-quality adeno-associated type 9 vectors using the HSV platform. Mol. Ther. Methods Clin. Dev. 3, 16031 (2016).

    Google Scholar 

  • Nguyen, T. N. T. et al. Mechanistic model for production of recombinant adeno-associated virus via triple transfection of HEK293 cells. Mol. Ther. Methods Clin. Dev. 21, 642–655 (2021).

    Google Scholar 

  • Qin, C. et al. Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system. Nat. Commun. 14, 1500 (2023).

    Google Scholar 

  • Slusarczyk, A. L., Lin, A. & Weiss, R. Foundations for the design and implementation of synthetic genetic circuits. Nat. Rev. Genet. 13, 406–420 (2012).

    Google Scholar 

  • Resch-Genger, U., Hoffmann, K. & Hoffmann, A. Standardization of fluorescence measurements: criteria for the choice of suitable standards and approaches to fit-for-purpose calibration tools. Ann. N. Y. Acad. Sci. 1130, 35–43 (2008).

    Google Scholar 

  • Xie, M. & Fussenegger, M. Designing cell function: assembly of synthetic gene circuits for cell biology applications. Nat. Rev. Mol. Cell Biol. 19, 507–525 (2018).

    Google Scholar 

  • Green, A. A. et al. Complex cellular logic computation using ribocomputing devices. Nature 548, 117–121 (2017).

    Google Scholar 

  • Tamsir, A., Tabor, J. J. & Voigt, C. A. Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires. Nature 469, 212–215 (2011).

    Google Scholar 

  • Gaber, R. et al. Designable DNA-binding domains enable construction of logic circuits in mammalian cells. Nat. Chem. Biol. 10, 203–208 (2014).

    Google Scholar 

  • abm Inc. Gene Regulation with dCas9. https://info.abmgood.com/crispr-cas9-gene-regulation-dCas9 (2017).

  • Zentner, G. E. & Henikoff, S. Epigenome editing made easy. Nat. Biotechnol. 33, 606–607 (2015).

    Google Scholar 

  • Chavez, A. et al. Comparison of Cas9 activators in multiple species. Nat. Methods 13, 563–567 (2016).

    Google Scholar 

  • Xu, X. et al. A CRISPR-based approach for targeted DNA demethylation. Cell Discov. 2, 16009 (2016).

    Google Scholar 

  • Kearns, N. A. et al. Functional annotation of native enhancers with a Cas9-histone demethylase fusion. Nat. Methods 12, 401–403 (2015).

    Google Scholar 

  • Vojta, A. et al. Repurposing the CRISPR-Cas9 system for targeted DNA methylation. Nucleic Acids Res. 44, 5615–5628 (2016).

    Google Scholar