Skip to main content

Negative Regulation Gene Circuits for Efflux Pump Control

  • Protocol
  • First Online:
Synthetic Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1772))

Abstract

Synthetic biologists aim to design biological systems for a variety of industrial and medical applications, ranging from biofuel to drug production. Synthetic gene circuits regulating efflux pump protein expression can achieve this by driving desired substrates such as biofuels, pharmaceuticals, or other chemicals out of the cell in a precisely controlled manner. However, efflux pumps may introduce implicit negative feedback by pumping out intracellular inducer molecules that control gene circuits, which then can alter gene circuit function. Therefore, synthetic gene circuits must be carefully designed and constructed for precise efflux control. Here, we provide protocols for quantitatively modeling and building synthetic gene constructs for efflux pump regulation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Stephanopoulos G (2012) Synthetic biology and metabolic engineering. ACS Synth Biol 1:514–525

    Article  CAS  Google Scholar 

  2. Nielsen J et al (2014) Engineering synergy in biotechnology. Nat Chem Biol 10:319–322

    Article  CAS  Google Scholar 

  3. Khalil AS, Collins JJ (2010) Synthetic biology: applications come of age. Nat Rev Genet 11:367–379

    Article  CAS  Google Scholar 

  4. Way JC et al (2014) Integrating biological redesign: where synthetic biology came from and where it needs to go. Cell 157:151–161

    Article  CAS  Google Scholar 

  5. Purnick PEM, Weiss R (2009) The second wave of synthetic biology: from modules to systems. Nat Rev Mol Cell Biol 10:410–422

    Article  CAS  Google Scholar 

  6. Collins JJ, Gardner TS, Cantor CR (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339–342

    Article  Google Scholar 

  7. Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335–338

    Article  CAS  Google Scholar 

  8. Anderson JC, Voigt CA, Arkin AP (2007) Environmental signal integration by a modular AND gate. Mol Syst Biol 3:133

    Article  Google Scholar 

  9. Tamsir A, Tabor JJ, Voigt CA (2011) Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature 469:212–215

    Article  CAS  Google Scholar 

  10. Dunlop MJ et al (2011) Engineering microbial biofuel tolerance and export using efflux pumps. Mol Syst Biol 7:487–487

    Article  Google Scholar 

  11. Diao J et al (2016) Efflux pump control alters synthetic gene circuit function. ACS Synth Biol 5:619–631

    Article  Google Scholar 

  12. Nevozhay D et al (2009) Negative autoregulation linearizes the dose-response and suppresses the heterogeneity of gene expression. Proc Natl Acad Sci U S A 106:5123–5128

    Article  CAS  Google Scholar 

  13. Blake WJ et al (2006) Phenotypic consequences of promoter-mediated transcriptional noise. Mol Cell 24:853–865

    Article  CAS  Google Scholar 

  14. Murphy KF, Balazsi G, Collins JJ (2007) Combinatorial promoter design for engineering noisy gene expression. Proc Natl Acad Sci U S A 104:12726–12731

    Article  CAS  Google Scholar 

  15. Kaern M et al (2005) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6(6):451–464

    Article  CAS  Google Scholar 

  16. Charlebois DA, Kaern M (2012) What all the noise is about: the physical basis of cellular individuality. Can J Phys 90:919–923

    Article  CAS  Google Scholar 

  17. Sanchez A, Golding I (2013) Genetic determinants and cellular constraints in noisy gene expression. Science 342:1188–1193

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  19. Charlebois DA, Balazsi G, Kaern M (2014) Coherent feedforward transcriptional regulatory motifs enhance drug resistance. Phys Rev E 89:052708

    Article  Google Scholar 

  20. Lage H (2003) ABC-transporters: implications on drug resistance from microorganisms to human cancers. Int J Antimicrob Agents 22:188−199

    Article  Google Scholar 

  21. Balzi E, Goffeau A (1995) Yeast multidrug resistance: the PDR network. J Bioenerg Biomembr 27:71–76

    Article  CAS  Google Scholar 

  22. Huh W-K et al (2003) Global analysis of protein localization in budding yeast. Nature 425:686–691

    Article  CAS  Google Scholar 

  23. Eaton JW, Bateman D, Hauberg S (2008) GNU octave manual. Network Theory Ltd., Surrey

    Google Scholar 

  24. Ramsey S, Orrell D, Bolouri H (2005) Dizzy: stochastic simulation of large-scale genetic regulatory networks. J Bioinforma Comput Biol 3:415–436

    Article  CAS  Google Scholar 

  25. Nevozhay D et al (2012) Mapping the environmental fitness landscape of a synthetic gene circuit. PLoS Comput Biol 8:e1002480

    Article  CAS  Google Scholar 

  26. Nevozhay D, Adams R, Balázsi G (2011) Linearizer gene circuits with negative feedback regulation. Methods Mol Biol 734:81–100

    Article  CAS  Google Scholar 

  27. MATLAB 2016 The MathWorks Inc.: Natick, MA

    Google Scholar 

  28. Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors would like to thank W.-K. Huh for kindly sharing the PDR5::GFP fusion, and M. Bennett, M. Lorenz, G. May, T. F. Cooper, G. Peng for helpful discussions, and Matthew Wu for editing the modeling section of the chapter. This research was supported by the NIH Director’s New Innovator Award (1DP2 OD006481− 01) and an NIGMS Maximizing Investigators’ Research Award (MIRA, 1R35GM122561) to G.B.; by an NSERC Postdoctoral Fellowship [Grant no: PDF-453977−2014] to D.C.; by the University of Texas Graduate School of Biomedical Sciences at Houston to J.D.; by Program # 1326 of the Ministry of Education and Science, Russian Federation to D.N.; and by the Laufer Center for Physical & Quantitative Biology. Daniel A. Charlebois and Junchen Diao contributed equally to this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gábor Balázsi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Charlebois, D.A., Diao, J., Nevozhay, D., Balázsi, G. (2018). Negative Regulation Gene Circuits for Efflux Pump Control. In: Braman, J. (eds) Synthetic Biology. Methods in Molecular Biology, vol 1772. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7795-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7795-6_2

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7794-9

  • Online ISBN: 978-1-4939-7795-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics