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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
Canton B, Labno A, Endy D (2008) Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol 26(7):787
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
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
Elowitz MB, Leibler S (2000) A synthetic oscillatory network of transcriptional regulators. Nature 403(6767):335
Hasty J, McMillen D, Collins JJ (2002) Engineered gene circuits. Nature 420(6912):224
Gardner TS, Cantor CR, Collins JJ (2000) Construction of a genetic toggle switch in Escherichia coli. Nature 403(6767):339
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
Mannan AA, Liu D, Zhang F, Oyarzún DA (2017) Fundamental design principles for transcription-factor-based metabolite biosensors. ACS Synth. Biol. 6:1851–1859
Oyarzún DA, Stan G-BV (2013) Synthetic gene circuits for metabolic control: design trade-offs and constraints.. J R Soc Interf 10:20120671
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
Chaves M, Oyarzún DA (2019) Dynamics of complex feedback architectures in metabolic pathways. Automatica 99:323–332
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
Hughes RA, Ellington AD (2017) Synthetic DNA synthesis and assembly: putting the synthetic in synthetic biology. Cold Spring Harbor Perspect Biol 9:a023812
Rondelez Y (2012) Competition for catalytic resources alters biological network dynamics. Phys Rev Lett 108(1):018102
Cardinale S, Arkin AP (2012) Contextualizing context for synthetic biology–identifying causes of failure of synthetic biological systems. Biotechnol J 7(7):856–866
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)
Mather WH, Hasty J, Tsimring LS, Williams RJ (2013) Translational cross talk in gene networks. Biophys J 104(11), 2564–2572
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
Tan C, Marguet P, You L (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat Chem Biol 5(11):842
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
An W, Chin JW (2009) Synthesis of orthogonal transcription-translation networks. Proc Natl Acad Sci
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
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
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
Darlington APS, Kim J, Jiménez JI, Bates DG (2018) Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes. Nat Commun 9:695
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
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
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
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
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
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
Purcell O, Jain B, Karr JR, Covert MW, Lu TK (2013) Towards a whole-cell modeling approach for synthetic biology. Chaos 23(2):025112
Klumpp S, Zhang Z, Hwa T (2009) Growth rate-dependent global effects on gene expression in bacteria. Cell 139:1366–1375
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
Liao C, Blanchard AE, Lu T (2017) An integrative circuit–host modelling framework for predicting synthetic gene network behaviours. Nat. Microbiol. 2(12):1658
Thomas P, Terradot G, Danos V, Weiße AY (2018) Sources, propagation and consequences of stochasticity in cellular growth. Nat Commun 9(1):1–11
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
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
Carrera J, Covert MW (2015) Why build whole-cell models? Trends Cell Biol 25(12):719–722
Karr JR, Takahashi K, Funahashi A (2015) The principles of whole-cell modeling. Curr Opin Microbiol 27:18–24
O’Brien EJ, Monk JM, Palsson BO (2015) Using genome-scale models to predict biological capabilities Cell 161(5):971–987
Monod J (1949) The growth of bacterial cultures. Ann Rev Microbiol 3(1):371–394
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
Neidhardt FC, Magasanik B (1960) Studies on the role of ribonucleic acid in the growth of bacteria. Biochim Biophys Acta 42:99–116
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
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)
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
Maitra A, Dill KA (2015) Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc Natl Acad Sci 112(2):406–411
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
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
Russell JB, Cook GM (1995) Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol Mol Biol Rev 59(1):48–62
McGinness KE, Baker TA, Sauer RT (2006) Engineering controllable protein degradation. Mol Cell 22(5):701–707
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
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
Lim WA (2010) Designing customized cell signalling circuits. Nat Rev Mol Cell Biol 11(6):393
Khalil AS, Collins JJ (2010) Synthetic biology: applications come of age. Nat Rev Genet 11(5):367
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
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
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
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
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
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
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
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