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A Computational Modeling Approach for the Design of Genetic Control Systems that Respond to Transcriptional Activity

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Mammalian Synthetic Systems

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2774))

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Abstract

Recent progress in synthetic biology has enabled the design of complex genetic circuits that interface with innate cellular functions, such as gene transcription, and control user-defined outputs. Implementing these genetic networks in mammalian cells, however, is a cumbersome process that requires several steps of optimization and benefits from the use of predictive modeling. Combining deterministic mathematical models with software-based numerical computing platforms allows researchers to quickly design, evaluate, and optimize multiple circuit topologies to establish experimental constraints that generate the desired control systems. In this chapter, we present a systematic approach based on predictive mathematical modeling to guide the design and construction of gene activity-based sensors. This approach enables user-driven circuit optimization through iterations of sensitivity analyses and parameter scans, providing a universal method to engineer sense and respond cells for diverse applications.

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References

  1. Tastanova A, Folcher M, Müller M et al (2018) Synthetic biology-based cellular biomedical tattoo for detection of hypercalcemia associated with cancer. Sci Transl Med 10:eaap8562. https://doi.org/10.1126/scitranslmed.aap8562

    Article  CAS  Google Scholar 

  2. Scheller L, Strittmatter T, Fuchs D et al (2018) Generalized extracellular molecule sensor platform for programming cellular behavior. Nat Chem Biol 14:723–729. https://doi.org/10.1038/s41589-018-0046-z

    Article  CAS  Google Scholar 

  3. Stefanov B-A, Fussenegger M (2022) Biomarker-driven feedback control of synthetic biology systems for next-generation personalized medicine. Front Bioeng Biotechnol 10:986210. https://doi.org/10.3389/fbioe.2022.986210

    Article  PubMed Central  Google Scholar 

  4. Manhas J, Edelstein HI, Leonard JN, Morsut L (2022) The evolution of synthetic receptor systems. Nat Chem Biol 18:244–255. https://doi.org/10.1038/s41589-021-00926-z

    Article  CAS  PubMed Central  Google Scholar 

  5. de Rossi J, Arefeayne Y, Robinson A, Segatori L (2022) Emerging technologies for genetic control systems in cellular therapies. Curr Opin Biotechnol 78:102833. https://doi.org/10.1016/j.copbio.2022.102833

    Article  CAS  PubMed Central  Google Scholar 

  6. Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403:335–338. https://doi.org/10.1038/35002125

    Article  CAS  Google Scholar 

  7. Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403:339–342. https://doi.org/10.1038/35002131

    Article  CAS  Google Scholar 

  8. Mizuguchi H, Xu Z, Ishii-Watabe A et al (2000) IRES-dependent second gene expression is significantly lower than cap-dependent first gene expression in a bicistronic vector. Mol Ther 1:376–382. https://doi.org/10.1006/mthe.2000.0050

    Article  CAS  Google Scholar 

  9. Shaimardanova AA, Kitaeva KV, Abdrakhmanova II et al (2019) Production and application of multicistronic constructs for various human disease therapies. Pharmaceutics 11:580. https://doi.org/10.3390/pharmaceutics11110580

    Article  CAS  PubMed Central  Google Scholar 

  10. Nevozhay D, Adams RM, Murphy KF et al (2009) Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression. Proc Natl Acad Sci USA 106:5123–5128. https://doi.org/10.1073/pnas.0809901106

    Article  PubMed Central  Google Scholar 

  11. Zhao W, Bonem M, McWhite C et al (2014) Sensitive detection of proteasomal activation using the Deg-On mammalian synthetic gene circuit. Nat Commun 5:3612. https://doi.org/10.1038/ncomms4612

    Article  CAS  Google Scholar 

  12. Kim H, Gelenbe E (2012) Stochastic gene expression modeling with Hill function for switch-like gene responses. IEEE/ACM Trans Comput Biol and Bioinf 9:973–979. https://doi.org/10.1109/TCBB.2011.153

    Article  Google Scholar 

  13. Rosenfeld N, Young JW, Alon U et al (2005) Gene regulation at the single-cell level. Science 307:1962–1965. https://doi.org/10.1126/science.1106914

    Article  CAS  Google Scholar 

  14. Nevozhay D, Zal T, Balázsi G (2013) Transferring a synthetic gene circuit from yeast to mammalian cells. Nat Commun 4:1451. https://doi.org/10.1038/ncomms2471

    Article  CAS  Google Scholar 

  15. Zhao W, Pferdehirt L, Segatori L (2018) Quantitatively predictable control of cellular protein levels through proteasomal degradation. ACS Synth Biol 7:540–552. https://doi.org/10.1021/acssynbio.7b00325

    Article  CAS  Google Scholar 

  16. Sanz-Leon P, Stuart-knock (2021) Brain-modelling-group/neural-flows: NIMH-DiDViz-2021

    Google Scholar 

  17. Origel Marmolejo CA, Bachhav B, Patibandla SD et al (2020) A gene signal amplifier platform for monitoring the unfolded protein response. Nat Chem Biol 16:520–528. https://doi.org/10.1038/s41589-020-0497-x

    Article  CAS  Google Scholar 

  18. Yamamoto Y, Gerbi SA (2018) Making ends meet: targeted integration of DNA fragments by genome editing. Chromosoma 127:405–420. https://doi.org/10.1007/s00412-018-0677-6

    Article  CAS  PubMed Central  Google Scholar 

  19. Qin JY, Zhang L, Clift KL et al (2010) Systematic comparison of constitutive promoters and the doxycycline-inducible promoter. PLoS ONE 5:e10611. https://doi.org/10.1371/journal.pone.0010611

    Article  CAS  PubMed Central  Google Scholar 

  20. Bachhav B, de Rossi J, Llanos CD, Segatori L (2023) Cell factory engineering: challenges and opportunities for synthetic biology applications. Biotech Bioeng bit.28365. https://doi.org/10.1002/bit.28365

  21. Matsuzawa S, Cuddy M, Fukushima T, Reed JC (2005) Method for targeting protein destruction by using a ubiquitin-independent, proteasome-mediated degradation pathway. Proc Natl Acad Sci USA 102:14982–14987. https://doi.org/10.1073/pnas.0507512102

    Article  CAS  PubMed Central  Google Scholar 

  22. Gilon T, Chomsky O, Kulka RG (2000) Degradation signals recognized by the Ubc6p-Ubc7p ubiquitin-conjugating enzyme pair. Mol Cell Biol 20:7214–7219. https://doi.org/10.1128/MCB.20.19.7214-7219.2000

    Article  CAS  PubMed Central  Google Scholar 

  23. Jayanthi BEK, Zhao W, Segatori L (2019) Input-dependent post-translational control of the reporter output enhances dynamic resolution of mammalian signaling systems. In: Methods in enzymology. Elsevier, pp 1–27

    Google Scholar 

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Acknowledgments

This work was supported by the National Institutes of Health grant EB030030 and by the National Science Foundation grants 2128370 and 2036109.

Author information

Authors and Affiliations

  1. Systems, Synthetic, and Physical Biology, Rice University, Houston, TX, USA

    Carlos D. Llanos & Laura Segatori

  2. Department of Bioengineering, Rice University, Houston, TX, USA

    Tianyi Xie, Ha Eun Lim & Laura Segatori

  3. Department of Chemical and Biochemical Engineering, Rice University, Houston, TX, USA

    Laura Segatori

  4. Department of Biosciences, Rice University, Houston, TX, USA

    Laura Segatori

Authors
  1. Carlos D. Llanos
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  2. Tianyi Xie
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  3. Ha Eun Lim
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  4. Laura Segatori
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Corresponding author

Correspondence to Laura Segatori .

Editor information

Editors and Affiliations

  1. Department of Chemical Engineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, UK

    Francesca Ceroni

  2. Department of Chemical Engineering and Imperial College Centre for Synthetic Biology, Imperial College London, London, UK

    Karen Polizzi

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© 2024 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

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Llanos, C.D., Xie, T., Lim, H.E., Segatori, L. (2024). A Computational Modeling Approach for the Design of Genetic Control Systems that Respond to Transcriptional Activity. In: Ceroni, F., Polizzi, K. (eds) Mammalian Synthetic Systems. Methods in Molecular Biology, vol 2774. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3718-0_8

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  • DOI: https://doi.org/10.1007/978-1-0716-3718-0_8

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3717-3

  • Online ISBN: 978-1-0716-3718-0

  • eBook Packages: Springer Protocols

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