Skip to main content

Prediction of Cellular Burden with Host–Circuit Models

Part of the Methods in Molecular Biology book series (MIMB,volume 2229)

Abstract

Heterologous gene expression draws resources from host cells. These resources include vital components to sustain growth and replication, and the resulting cellular burden is a widely recognized bottleneck in the design of robust circuits. In this tutorial we discuss the use of computational models that integrate gene circuits and the physiology of host cells. Through various use cases, we illustrate the power of host–circuit models to predict the impact of design parameters on both burden and circuit functionality. Our approach relies on a new generation of computational models for microbial growth that can flexibly accommodate resource bottlenecks encountered in gene circuit design. Adoption of this modeling paradigm can facilitate fast and robust design cycles in synthetic biology.

Key words

  • Cellular burden
  • Growth models
  • Whole-cell modeling
  • Gene circuit design
  • Synthetic biology
  • Resource allocation

This is a preview of subscription content, access via your institution.

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-0716-1032-9_13
  • Chapter length: 25 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   119.00
Price excludes VAT (USA)
  • ISBN: 978-1-0716-1032-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   159.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Andrianantoandro E, Basu S, Karig DK, Weiss R (2006) Synthetic biology: new engineering rules for an emerging discipline. Mol Syst Biol 2(1):2006.0028

    PubMed  PubMed Central  Google Scholar 

  2. Canton B, Labno A, Endy D (2008) Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol 26(7):787

    CAS  PubMed  Google Scholar 

  3. Ninfa AJ, Selinsky S, Perry N, Atkins S, Song QX, Mayo A, Arps D, Woolf P, Atkinson MR (2007) Using two-component systems and other bacterial regulatory factors for the fabrication of synthetic genetic devices. Methods Enzymol 422:488–512

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Teo JJ, Woo SS, Sarpeshkar R (2015) Synthetic biology: a unifying view and review using analog circuits. IEEE Trans Biomed Circ Syst 9(4):453–474

    Google Scholar 

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

    CAS  PubMed  Google Scholar 

  6. Hasty J, McMillen D, Collins JJ (2002) Engineered gene circuits. Nature 420(6912):224

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  8. Tabor JJ, Salis HM, Simpson ZB, Chevalier AA, Levskaya A, Marcotte EM, Voigt CA, Ellington AD (2009) A synthetic genetic edge detection program. Cell 137(7):1272–1281

    PubMed  PubMed Central  Google Scholar 

  9. Mannan AA, Liu D, Zhang F, Oyarzún DA (2017) Fundamental design principles for transcription-factor-based metabolite biosensors. ACS Synth. Biol. 6:1851–1859

    CAS  PubMed  Google Scholar 

  10. Oyarzún DA, Stan G-BV (2013) Synthetic gene circuits for metabolic control: design trade-offs and constraints.. J R Soc Interf 10:20120671

    Google Scholar 

  11. Nielsen AA, Der BS, Shin J, Vaidyanathan P, Paralanov V, Strychalski EA, Ross D, Densmore D, Voigt CA (2016) Genetic circuit design automation. Science 352(6281):aac7341

    Google Scholar 

  12. Chaves M, Oyarzún DA (2019) Dynamics of complex feedback architectures in metabolic pathways. Automatica 99:323–332

    Google Scholar 

  13. Carbonell P, Radivojevic T, García Martín H (2019) Opportunities at the intersection of synthetic biology, machine learning, and automation. ACS Synth Biol 8:1474–1477

    CAS  PubMed  Google Scholar 

  14. Hughes RA, Ellington AD (2017) Synthetic DNA synthesis and assembly: putting the synthetic in synthetic biology. Cold Spring Harbor Perspect Biol 9:a023812

    Google Scholar 

  15. Rondelez Y (2012) Competition for catalytic resources alters biological network dynamics. Phys Rev Lett 108(1):018102

    PubMed  Google Scholar 

  16. Cardinale S, Arkin AP (2012) Contextualizing context for synthetic biology–identifying causes of failure of synthetic biological systems. Biotechnol J 7(7):856–866

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Gyorgy A, Del Vecchio D (2014) Limitations and trade-offs in gene expression due to competition for shared cellular resources. In: 2014 IEEE 53rd Annual Conference on Decision and Control (CDC), pp. 5431–5436. IEEE, New York (2014)

    Google Scholar 

  18. Mather WH, Hasty J, Tsimring LS, Williams RJ (2013) Translational cross talk in gene networks. Biophys J 104(11), 2564–2572

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T (2010) Interdependence of cell growth and gene expression: origins and consequences. Science 330(6007):1099–1102

    CAS  PubMed  Google Scholar 

  20. Tan C, Marguet P, You L (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat Chem Biol 5(11):842

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Ceroni F, Algar R, Stan G-B, Ellis T (2015) Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat Methods 12(5):415

    CAS  PubMed  Google Scholar 

  22. An W, Chin JW (2009) Synthesis of orthogonal transcription-translation networks. Proc Natl Acad Sci

    Google Scholar 

  23. Segall-Shapiro TH, Meyer AJ, Ellington AD, Sontag ED, Voigt CA (2014) A resource allocator for transcription based on a highly fragmented T7 RNA polymerase. Mol Syst Biol 10(7):742

    PubMed  PubMed Central  Google Scholar 

  24. Pasini M, Fernández-Castané A, Jaramillo A, de Mas C, Caminal G, Ferrer P (2016) Using promoter libraries to reduce metabolic burden due to plasmid-encoded proteins in recombinant Escherichia coli. New Biotechnol 33(1):78–90

    CAS  Google Scholar 

  25. Shopera T, He L, Oyetunde T, Tang YJ, Moon TS (2017) Decoupling resource-coupled gene expression in living cells. ACS Synth Biol 6(8):1596–1604

    CAS  PubMed  Google Scholar 

  26. Darlington APS, Kim J, Jiménez JI, Bates DG (2018) Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes. Nat Commun 9:695

    PubMed  PubMed Central  Google Scholar 

  27. Rugbjerg P, Sarup-Lytzen K, Nagy M, Sommer MOA (2018) Synthetic addiction extends the productive life time of engineered Escherichia coli populations. Proc Natl Acad Sci 115(10):2347–2352

    PubMed  Google Scholar 

  28. Ceroni F, Boo A, Furini S, Gorochowski TE, Borkowski O, Ladak YN, Awan AR, Gilbert C, Stan G-B, Ellis T (2018) Burden-driven feedback control of gene expression. Nat Methods 15(5):387

    CAS  PubMed  Google Scholar 

  29. Gyorgy A, Jiménez JI, Yazbek J, Huang H-H, Chung H, Weiss R, Del Vecchio D (2015) Isocost lines describe the cellular economy of genetic circuits. Biophys J 109(3):639–646

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Carbonell-Ballestero M, Garcia-Ramallo E, Montañez R, Rodriguez-Caso C, Macía J (2015) Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the ohm’s law. Nucleic Acids Res 44(1):496–507

    PubMed  PubMed Central  Google Scholar 

  31. Gorochowski TE, Avcilar-Kucukgoze I, Bovenberg RA, Roubos JA, Ignatova Z (2016) A minimal model of ribosome allocation dynamics captures trade-offs in expression between endogenous and synthetic genes. ACS Synth Biol 5(7):710–720

    CAS  PubMed  Google Scholar 

  32. Karr JR, Sanghvi JC, Macklin DN, Gutschow MV, Jacobs JM, Bolival Jr B, Assad-Garcia N, Glass JI, Covert MW (2012) A whole-cell computational model predicts phenotype from genotype. Cell 150(2):389–401

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Purcell O, Jain B, Karr JR, Covert MW, Lu TK (2013) Towards a whole-cell modeling approach for synthetic biology. Chaos 23(2):025112

    PubMed  PubMed Central  Google Scholar 

  34. Klumpp S, Zhang Z, Hwa T (2009) Growth rate-dependent global effects on gene expression in bacteria. Cell 139:1366–1375

    PubMed  PubMed Central  Google Scholar 

  35. Weiße AY, Oyarzún DA, Danos V, Swain PS (2015) Mechanistic links between cellular trade-offs, gene expression, and growth. Proc Natl Acad Sci 112(9):E1038–E1047

    PubMed  Google Scholar 

  36. Liao C, Blanchard AE, Lu T (2017) An integrative circuit–host modelling framework for predicting synthetic gene network behaviours. Nat. Microbiol. 2(12):1658

    CAS  PubMed  Google Scholar 

  37. Thomas P, Terradot G, Danos V, Weiße AY (2018) Sources, propagation and consequences of stochasticity in cellular growth. Nat Commun 9(1):1–11

    Google Scholar 

  38. Nikolados E-M, Weiße AY, Ceroni F, Oyarzún DA (2019) Growth defects and loss-of-function in synthetic gene circuits. ACS Synth Biol 8(6):1231–1240

    CAS  PubMed  Google Scholar 

  39. O’Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson B (2013) Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol 9:693

    PubMed  PubMed Central  Google Scholar 

  40. Carrera J, Covert MW (2015) Why build whole-cell models? Trends Cell Biol 25(12):719–722

    PubMed  PubMed Central  Google Scholar 

  41. Karr JR, Takahashi K, Funahashi A (2015) The principles of whole-cell modeling. Curr Opin Microbiol 27:18–24

    CAS  PubMed  Google Scholar 

  42. O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities Cell 161(5):971–987

    PubMed  PubMed Central  Google Scholar 

  43. Monod J (1949) The growth of bacterial cultures. Ann Rev Microbiol 3(1):371–394

    CAS  Google Scholar 

  44. Schaechter M, Maaløe O, Kjeldgaard NO (1958) Dependency on medium and temperature of cell size and chemical composition during balanced growth of Salmonella typhimurium. Microbiology 19(3):592–606

    CAS  Google Scholar 

  45. Neidhardt FC, Magasanik B (1960) Studies on the role of ribonucleic acid in the growth of bacteria. Biochim Biophys Acta 42:99–116

    CAS  PubMed  Google Scholar 

  46. Dennis PP, Ehrenberg M, Bremer H (2004) Control of rRNA synthesis in Escherichia coli: a systems biology approach. Microbiol Mol Biol Rev 68(4):639–668

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Maaløe O (1979) Regulation of the protein-synthesizing machinery—ribosomes, tRNA, factors, and so on. In: Biological Regulation and Development, pp. 487–542. Springer, New York (1979)

    Google Scholar 

  48. Bremer H, Dennis PP, et al (1996) Modulation of chemical composition and other parameters of the cell by growth rate. EcoSal Cell Mol Biol 2(2):1553–1569

    Google Scholar 

  49. Maitra A, Dill KA (2015) Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc Natl Acad Sci 112(2):406–411

    CAS  PubMed  Google Scholar 

  50. Bosdriesz E, Molenaar D, Teusink B, Bruggeman FJ (2015) How fast-growing bacteria robustly tune their ribosome concentration to approximate growth-rate maximization. FEBS J 282(10):2029–2044

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Molenaar D, Van Berlo R, De Ridder D, Teusink B (2009) Shifts in growth strategies reflect tradeoffs in cellular economics. Mol Syst Biol 5(1):323

    PubMed  PubMed Central  Google Scholar 

  52. Russell JB, Cook GM (1995) Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol Mol Biol Rev 59(1):48–62

    CAS  Google Scholar 

  53. McGinness KE, Baker TA, Sauer RT (2006) Engineering controllable protein degradation. Mol Cell 22(5):701–707

    CAS  PubMed  Google Scholar 

  54. Vind J, Sørensen MA, Rasmussen MD, Pedersen S (1993) Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes: expression from reporter genes does not always reflect functional mRNA levels. J Mol Biol 231(3):678–688

    CAS  PubMed  Google Scholar 

  55. Dong H, Nilsson L, Kurland CG (1995) Gratuitous overexpression of genes in Escherichia coli leads to growth inhibition and ribosome destruction. J Bacteriol 177(6):1497–1504

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Lim WA (2010) Designing customized cell signalling circuits. Nat Rev Mol Cell Biol 11(6):393

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Khalil AS, Collins JJ (2010) Synthetic biology: applications come of age. Nat Rev Genet 11(5):367

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Joshi N, Wang X, Montgomery L, Elfick A, French C (2009) Novel approaches to biosensors for detection of arsenic in drinking water. Desalination 248(1–3):517–523

    CAS  Google Scholar 

  59. Paitan Y, Biran I, Shechter N, Biran D, Rishpon J, Ron EZ (2004) Monitoring aromatic hydrocarbons by whole cell electrochemical biosensors. Anal Biochem 335(2):175–183

    CAS  PubMed  Google Scholar 

  60. Saeidi N, Wong CK, Lo T-M, Nguyen HX, Ling H, Leong SSJ, Poh CL, Chang MW (2011) Engineering microbes to sense and eradicate Pseudomonas aeruginosa, a human pathogen. Mol Syst Biol 7(1):521

    PubMed  PubMed Central  Google Scholar 

  61. Wang B, Kitney RI, Joly N, Buck M (2011) Engineering modular and orthogonal genetic logic gates for robust digital-like synthetic biology. Nat Commun 2:508

    PubMed  PubMed Central  Google Scholar 

  62. Hartline CJ, Mannan AA, Liu D, Zhang F, Oyarzún DA (2020) Metabolite sequestration enables rapid recovery from fatty acid depletion in Escherichia coli. mBio 11:e03112–e03119

    Google Scholar 

  63. Cambray G, Guimaraes JC, Arkin AP (2018) Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat Biotechnol 36(10):1005

    CAS  PubMed  Google Scholar 

  64. Borkowski O, Bricio C, Murgiano M, Rothschild-Mancinelli B, Stan GB, Ellis T (2018) Cell-free prediction of protein expression costs for growing cells. Nat Commun 9(1):1457

    PubMed  PubMed Central  Google Scholar 

  65. Liu D, Mannan AA, Han Y, Oyarzún DA, Zhang F (2018) Dynamic metabolic control: towards precision engineering of metabolism. J Ind Microbiol Biotechnol 45:535–543

    CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Diego A. Oyarzún .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Verify currency and authenticity via CrossMark

Cite this protocol

Nikolados, EM., Weiße, A.Y., Oyarzún, D.A. (2021). Prediction of Cellular Burden with Host–Circuit Models. In: Menolascina, F. (eds) Synthetic Gene Circuits . Methods in Molecular Biology, vol 2229. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1032-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1032-9_13

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1031-2

  • Online ISBN: 978-1-0716-1032-9

  • eBook Packages: Springer Protocols