Abstract
Background
One of the underlying assumptions of synthetic biology is that biological processes can be engineered in a controllable way.
Results
Here we discuss this assumption as it relates to synthetic gene regulatory networks (GRNs).We first cover the theoretical basis of GRN control, then address three major areas in which control has been leveraged: engineering and analysis of network stability, temporal dynamics, and spatial aspects.
Conclusion
These areas lay a strong foundation for further expansion of control in synthetic GRNs and pave the way for future work synthesizing these disparate concepts.
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Antoni, D., Zverlov, V. V. and Schwarz, W. H. (2007) Biofuels from microbes. Appl. Microbiol. Biotechnol., 77, 23–35
Dellomonaco, C., Fava, F. and Gonzalez, R. (2010) The path to next generation biofuels: successes and challenges in the era of synthetic biology. Microb. Cell Fact., 9, 3
Harrison, M. E. and Dunlop, M. J. (2012) Synthetic feedback loop model forincreasing microbial biofuel production using a biosensor. Front. Microbio., 3, 360
Krom, R. J., Bhargava, P., Lobritz, M. A. and Collins, J. J. (2015) Engineered phagemids for nonlytic, targeted antibacterial therapies. Nano Lett., 15, 4808–4813
Sufya, N., Allison, D. and Gilbert, P. (2003) Clonal variation in maximum specific growth rate and susceptibility towards antimicrobials. J. Appl. Microbiol., 95, 1261–1267
de Lorenzo, V. (2008) Systems biology approaches to bioremediation. Curr. Opin. Biotechnol., 19, 579–589
Martin, V. J. J., Pitera, D. J., Withers, S. T., Newman, J. D. and Keasling, J. D. (2003) Engineering a mevalonate pathway in Escherichia coli for production of terpenoids. Nat. Biotechnol., 21, 796–802
Ellis, T., Adie, T. and Baldwin, G. S. (2011) DNA assembly for synthetic biology: from parts to pathways and beyond. Integr. Biol., 3, 109
Gibson, D. G., Young, L., Chuang, R.-Y., Venter, J. C., Hutchison, C. A. and Smith, H. O. (2009) Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods, 6, 343–345
Densmore, D., Hsiau, T. H. C., Kittleson, J. T., DeLoache, W., Batten, C. and Anderson, J. C. (2010) Algorithms for automated DNA assembly. Nucleic Acids Res., 38, 2607–2616
Shendure, J. and Ji, H. (2008) Next-generation DNA sequencing. Nat. Biotechnol., 26, 1135–1145
Chavez, A., Scheiman, J., Vora, S., Pruitt, B. W., Tuttle, M., Iyer, E. P. R., Lin, S., Kiani, S., Guzman, C. D., Wiegand, D. J., et al. (2015) Highly efficient Cas9-mediated transcriptional programming. Nat. Methods, 12, 326–328
Kiani, S., Beal, J., Ebrahimkhani, M. R., Huh, J., Hall, R. N., Xie, Z., Li, Y. and Weiss, R. (2014) CRISPR transcriptional repression devices and layered circuits in mammalian cells. Nat. Methods, 11, 723–726
Standage-Beier, K., Zhang, Q. and Wang, X. (2015) Targeted large-scale deletion of bacterial genomes using CRISPRnickases. ACS Synth. Biol., 4, 1217–1225
Guido, N. J., Wang, X., Adalsteinsson, D., McMillen, D., Hasty, J., Cantor, C. R., Elston, T. C. and Collins, J. (2006) A bottom-up approach to gene regulation. Nature, 439, 856–860
Stricker, J., Cookson, S., Bennett, M. R., Mather, W. H., Tsimring, L. S. and Hasty, J. (2008) A fast, robust and tunable synthetic gene oscillator. Nature, 456, 516–519
Elowitz, M. B. and Leibler, S. (2000) A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338
Gardner, T. S., Cantor, C. R. and Collins, J. J. (2000) Construction of a genetic toggle switch in Escherichia coli. Nature, 403, 339–342
Wang, L.-Z., Wu, F., Flores, K., Lai, Y.-C. and Wang, X. (2016) Build to understand: synthetic approaches to biology. Integr. Biol., 8, 394–408
Shimizu, Y., Inoue, A., Tomari, Y., Suzuki, T., Yokogawa, T., Nishikawa, K. and Ueda, T. (2001) Cell-free translation reconstituted with purified components. Nat. Biotechnol., 19, 751–755
Pardee, K., Green, A. A., Takahashi, M. K., Braff, D., Lambert, G., Lee, J. W., Ferrante, T., Ma, D., Donghia, N., Fan, M., et al. (2016) Rapid, low-cost detection of zika virus using programmable biomolecular components. Cell, 165, 1255–1266
Elowitz, M. B., Levine, A. J., Siggia, E. D. and Swain, P. S. (2002) Stochastic gene expression in a single cell. Science, 297, 1183–1186
Wu, F., Menn, D. J. and Wang, X. (2014) Quorum-sensing crosstalk-driven synthetic circuits: from unimodality to trimodality. Chem. Biol., 21, 1629–1638
Wu, M., Su, R.-Q., Li, X., Ellis, T., Lai, Y.-C. and Wang, X. (2013) Engineering of regulated stochastic cell fate determination. Proc. Natl. Acad. Sci. USA, 110, 10610–10615
Xiong, W. and Ferrell, J. E. Jr. (2003) A positive-feedback-based bistable “memory module” that governs a cell fate decision. Nature, 426, 460–465
Ellis, T., Wang, X. and Collins, J. J. (2009) Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol., 27, 465–471
Potvin-Trottier, L., Lord, N. D., Vinnicombe, G. and Paulsson, J. (2016) Synchronous long-term oscillations in a synthetic gene circuit. Nature, 538, 514–517
Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. and Weiss, R. (2005) A synthetic multicellular system for programmed pattern formation. Nature, 434, 1130–1134
Liu, C., Fu, X., Liu, L., Ren, X., Chau, C. K., Li, S., Xiang, L., Zeng, H., Chen, G., Tang, L.-H., et al. (2011) Sequential establishment of stripe patterns in an expanding cell population. Science, 334, 238–241
Payne, S., Li, B., Cao, Y., Schaeffer, D., Ryser, M. D. and You, L. (2013) Temporal control of self-organized pattern formation without morphogen gradients in bacteria. Mol. Syst. Biol., 9, 697
Friedland, A. E., Lu, T. K., Wang, X., Shi, D., Church, G. and Collins, J. J. (2009) Synthetic gene networks that count. Science, 324, 1199–1202
Tamsir, A., Tabor, J. J. and Voigt, C. A. (2011) Robust multicellular computing using genetically encoded NOR gates and chemical ‘wires’. Nature, 469, 212–215
Yang, L., Nielsen, A. A., Fernandez-Rodriguez, J., McClune, C. J., Laub, M. T., Lu, T. K. and Voigt, C. A. (2014) Permanent genetic memory with >1-byte capacity. Nat. Methods, 11, 1261–1266
Nielsen, A. A. and Voigt, C. A. (2014) Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks. Mol. Syst. Biol., 10, 763
Ishimatsu, K., Hata, T., Mochizuki, A., Sekine, R., Yamamura, M. and Kiga, D. (2014) General applicability of synthetic geneoverexpression for cell-type ratio control via reprogramming. ACS Synth. Biol., 3, 638–644
Yamaguchi, M., Ito, A., Ono, A., Kawabe, Y. and Kamihira, M. (2014) Heat-inducible gene expression system by applying alternating magnetic field to magnetic nanoparticles. ACS Synth. Biol., 3, 273–279
Hussain, F., Gupta, C., Hirning, A. J., Ott, W., Matthews, K. S., Josić, K. and Bennett, M. R. (2014) Engineered temperature compensation in a synthetic genetic clock. Proc. Natl. Acad. Sci. USA, 111, 972–977
Levskaya, A., Chevalier, A. A., Tabor, J. J., Simpson, Z. B., Lavery, L. A., Levy, M., Davidson, E. A., Scouras, A., Ellington, A. D., Marcotte, E. M., et al. (2005) Synthetic biology: engineering Escherichia coli to see light. Nature, 438, 441–442
Levskaya, A., Weiner, O. D., Lim, W. A. and Voigt, C. A. (2009) Spatiotemporal control of cell signalling using a light-switchable protein interaction. Nature, 461, 997–1001
Jogler, C. and Schüler, D. (2009) Genomics, genetics, and cell biology of magnetosome formation. Annu. Rev. Microbiol., 63, 501–521
Wang, L.-Z., Su, R.-Q., Huang, Z.-G., Wang, X., Wang, W.-X., Grebogi, C. and Lai, Y.-C. (2016) A geometrical approach to control and controllability of nonlinear dynamical networks. Nat. Commun., 7, 11323
Shin, Y.-J. and Bleris, L. (2010) Linear control theory for gene network modeling. PLoS One, 5, e12785
Del Vecchio, D., Dy, A. J. and Qian, Y. (2016) Control theory meets synthetic biology. J. R. Soc. Interface, 13, 20160380
Kalman, R. E. (1963) Mathematical description of linear dynamical systems. J. Soc. Ind. Appl. Math. Ser. Control, 1, 152–192
Lin, C.-T. (1974) Structural controllability. IEEE Trans. Automat. Contr., 19, 201–208
Keasling, J. D. (2010) Manufacturing molecules through metabolic engineering. Science, 330, 1355–1358
Koffas, M., Roberge, C., Lee, K. and Stephanopoulos, G. (1999) Metabolic engineering. Annu. Rev. Biomed. Eng., 1, 535–557
Polynikis, A., Hogan, S. and di Bernardo, M. (2009) Comparing different ODE modelling approaches for gene regulatory networks. J. Theor. Biol., 261, 511–530
Liu, Y.-Y., Slotine, J.-J. and Barabási, A.-L. (2011) Controllability of complex networks. Nature, 473, 167–173
Basler, G., Nikoloski, Z., Larhlimi, A., Barabási, A.-L. and Liu, Y.-Y. (2016) Control of fluxes in metabolic networks. Genome Res., 26, 956–968
Strogatz, S. H. (2014) Nonlinear Dynamics and Chaos: with Applications to Physics, Biology, Chemistry, and Engineering. Boulder: Westview Press
Faucon, P. C., Pardee, K., Kumar, R. M., Li, H., Loh, Y.-H. and Wang, X. (2014) Gene networks of fully connected triads with complete auto-activation enable multistability and stepwise stochastic transitions. PLoS One, 9, e102873
Kim, D. H., Grün, D. and van Oudenaarden, A. (2013) Dampening of expression oscillations by synchronous regulation of a microRNA and its target. Nat. Genet., 45, 1337–1344
Thorsley, D. and Klavins, E. (2012) Estimation and discrimination of stochastic biochemical circuits from time-lapse microscopy data. PLoS One, 7, e47151
Swain, P. S., Elowitz, M. B. and Siggia, E. D. (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. USA, 99, 12795–12800
Wang, J., Xu, L. and Wang, E. (2008) Potential landscape and flux framework of nonequilibrium networks: robustness, dissipation, and coherence of biochemical oscillations. Proc. Natl. Acad. Sci. USA, 105, 12271–12276
Gillespie, D. T. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem., 81, 2340–2361
Balagaddé, F. K., Song, H., Ozaki, J., Collins, C. H., Barnet, M., Arnold, F. H., Quake, S. R. and You, L. (2008) A synthetic Escherichia coli predator — prey ecosystem. Mol. Syst. Biol., 4, 187
Song, H., Payne, S., Gray, M. and You, L. (2009) Spatiotemporal modulation of biodiversity in a synthetic chemical-mediated ecosystem. Nat. Chem. Biol., 5, 929–935
Song, H. and You, L. (2012) Modeling Spatiotemporal Dynamics of Bacterial Populations. In Computational Modeling of Signaling Networks. New Jersey: Humana Press
Kim, K.-Y. and Wang, J. (2007) Potential energy landscape and robustness of a gene regulatory network: toggle switch. PLoS Comput. Biol., 3, e60
Huang, S., Eichler, G., Bar-Yam, Y. and Ingber, D. E. (2005) Cell fates as high-dimensional attractor states of a complex gene regulatory network. Phys. Rev. Lett., 94, 128701
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D. and Alon, U. (2002) Network motifs: simple building blocks of complex networks. Science, 298, 824–827
Ma, W., Trusina, A., El-Samad, H., Lim, W. A. and Tang, C. (2009) Defining network topologies that can achieve biochemical adaptation. Cell, 138, 760–773
Mallet, D. G. and De Pillis, L. G. (2006) A cellular automata model of tumor—immune system interactions. J. Theor. Biol., 239, 334–350
Huang, S., Ernberg, I. and Kauffman, S. (2009) Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective. Semin. Cell Dev. Biol., 20, 869–876
Wells, D. K., Kath, W. L. and Motter, A. E. (2015) Control of stochastic and induced switching in biophysical networks. Phys. Rev. X, 5, 031036
Ozbudak, E. M., Thattai, M., Lim, H. N., Shraiman, B. I. and Van Oudenaarden, A. (2004) Multistability in the lactose utilization network of Escherichia coli. Nature, 427, 737–740
Leisner, M., Kuhr, J.-T., Rädler, J. O., Frey, E. and Maier, B. (2009) Kinetics of genetic switching into the state of bacterial competence. Biophys. J., 96, 1178–1188
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. and Leibler, S. (2004) Bacterial persistence as a phenotypic switch. Science, 305, 1622–1625
Dhar, N. and McKinney, J. D. (2007) Microbial phenotypic heterogeneity and antibiotic tolerance. Curr. Opin. Microbiol., 10, 30–38
Davidson, E.H., Rast, J.P., Oliveri, P., Ransick, A., Calestani, C., Yuh, C.-H., Minokawa, T., Amore, G., Hinman, V., Arenas-Mena, C., et al. (2002) A genomic regulatory network for development. Science, 295, 1669–1678
Lipshtat, A., Loinger, A., Balaban, N. Q. and Biham, O. (2006) Genetic toggle switch without cooperative binding. Phys. Rev. Lett., 96, 188101
Isaacs, F. J., Hasty, J., Cantor, C. R. and Collins, J. J. (2003) Prediction and measurement of an autoregulatory genetic module. Proc. Natl. Acad. Sci. USA, 100, 7714–7719
Singh, V. (2014) Recent advancements in synthetic biology: current status and challenges. Gene, 535, 1–11
Greber, D., El-Baba, M. D. and Fussenegger, M. (2008) Intronically encoded siRNAs improve dynamic range of mammalian gene regulation systems and toggle switch. Nucleic Acids Res., 36, e101
Smits, W. K., Eschevins, C. C., Susanna, K. A., Bron, S., Kuipers, O. P. and Hamoen, L. W. (2005) Stripping Bacillus: ComK autostimulation is responsible for the bistable response in competence development. Mol. Microbiol., 56, 604–614
Tan, C., Marguet, P. and You, L. (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol., 5, 842–848
Yao, G., Tan, C., West, M., Nevins, J. R. and You, L. (2011) Origin of bistability underlying mammalian cell cycle entry. Mol. Syst. Biol., 7, 485
Prill, R. J., Iglesias, P. A. and Levchenko, A. (2005) Dynamic properties of network motifs contribute to biological network organization. PLoS Biol., 3, e343
Nielsen, A. A., Der, B. S., Shin, J., Vaidyanathan, P., Paralanov, V., Strychalski, E. A., Ross, D., Densmore, D. and Voigt, C. A. (2016) Genetic circuit design automation. Science, 352, aac7341
Ausländer, S., Ausländer, D., Müller, M., Wieland, M. and Fussenegger, M. (2012) Programmable single-cell mammalian biocomputers. Nature, 487, 123–127
Gaber, R., Lebar, T., Majerle, A., Čter, B., Dobnikar, A., Benčina, M. and Jerala, R. (2014) Designable DNA-binding domains enable construction of logic circuits in mammalian cells. Nat. Chem. Biol., 10, 203–208
Mishra, D., Rivera, P. M., Lin, A., Del Vecchio, D. and Weiss, R. (2014) A load driver device for engineering modularity in biological networks. Nat. Biotechnol., 32, 1268–1275
Del Vecchio, D. (2013) A control theoretic framework for modular analysis and design of biomolecular networks. Annu. Rev. Contr., 37, 333–345
Harbauer, A. B., Opalińska, M., Gerbeth, C., Herman, J. S., Rao, S., Schönfisch, B., Guiard, B., Schmidt, O., Pfanner, N. and Meisinger, C. (2014) Cell cycle—dependent regulation of mitochondrial preprotein translocase. Science, 346, 1109–1113
Feillet, C., Krusche, P., Tamanini, F., Janssens, R. C., Downey, M. J., Martin, P., Teboul, M., Saito, S., Lévi, F. A., Bretschneider, T., et al. (2014) Phase locking and multiple oscillating attractors for the coupled mammalian clock and cell cycle. Proc. Natl. Acad. Sci. USA, 111, 9828–9833
Kim, J., Khetarpal, I., Sen, S. and Murray, R. M. (2014) Synthetic circuit for exact adaptation and fold-change detection. Nucleic Acids Res., 42, 6078–6089
Ostojic, S. (2014) Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons. Nat. Neurosci., 17, 594–600
Comb, M., Hyman, S. E. and Goodman, H. M. (1987) Mechanisms of trans-synaptic regulation of gene expression. Trends Neurosci., 10, 473–478
Tigges, M., Marquez-Lago, T. T., Stelling, J. and Fussenegger, M. (2009) A tunable synthetic mammalian oscillator. Nature, 457, 309–312
Xiao, M. and Cao, J. (2008) Genetic oscillation deduced from Hopf bifurcation in a genetic regulatory network with delays. Math. Biosci., 215, 55–63
Zakharova, A., Vadivasova, T., Anishchenko, V., Koseska, A. and Kurths, J. (2010) Stochastic bifurcations and coherencelike resonance in a self-sustained bistable noisy oscillator. Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 81, 011106
Lewis, J. (2003) Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator. Curr. Biol., 13, 1398–1408
Swinburne, I. A., Miguez, D. G., Landgraf, D. and Silver, P. A. (2008) Intron length increases oscillatory periods of gene expression in animal cells. Genes Dev., 22, 2342–2346
Izhikevich, E. M. (2000) Neural excitability, spiking and bursting. Int. J. Bifurcat. Chaos, 10, 1171–1266
Fuqua, W. C., Winans, S. C. and Greenberg, E. P. (1994) Quorum sensing in bacteria: the LuxR-LuxI family of cell densityresponsive transcriptional regulators. J. Bacteriol., 176, 269–275
Oates, A. C., Morelli, L. G. and Ares, S. (2012) Patterning embryos with oscillations: structure, function and dynamics of the vertebrate segmentation clock. Development, 139, 625–639
You, L., Cox, R. S. III, Weiss, R. and Arnold, F. H. (2004) Programmed population control by cell–cell communication and regulated killing. Nature, 428, 868–871
Balagaddé, F. K., You, L., Hansen, C. L., Arnold, F. H. and Quake, S. R. (2005) Long-term monitoring of bacteria undergoing programmed population control in a microchemostat. Science, 309, 137–140
Cao, Y., Ryser, M. D., Payne, S., Li, B., Rao, C. V. and You, L. (2016) Collective space-sensing coordinates pattern scaling in engineered bacteria. Cell, 165, 620–630
Smith, R., Tan, C., Srimani, J. K., Pai, A., Riccione, K. A., Song, H. and You, L. (2014) Programmed Allee effect in bacteria causes a tradeoff between population spread and survival. Proc. Natl. Acad. Sci. USA, 111, 1969–1974
Bintu, L., Yong, J., Antebi, Y. E., McCue, K., Kazuki, Y., Uno, N., Oshimura, M. and Elowitz, M. B. (2016) Dynamics of epigenetic regulation at the single-cell level. Science, 351, 720–724
Keung, A. J., Bashor, C. J., Kiriakov, S., Collins, J. J. and Khalil, A. S. (2014) Using targeted chromatin regulators to engineer combinatorial and spatial transcriptional regulation. Cell, 158, 110–120
Acknowledgements
We thank members of Xiao Wang’s lab for helpful discussions and suggestions. X.W.’s lab is supported by the National Institutes of Health Grant GM106081. D. J. M. is partially supported by the Arizona State University Dean’s Fellowship.
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This article is dedicated to the Special Collection of Synthetic Biology, Aiming for Quantitative Control of Cellular Systems (Eds. Cheemeng Tan and Haiyan Liu).
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Menn, D.J., Su, RQ. & Wang, X. Control of synthetic gene networks and its applications. Quant Biol 5, 124–135 (2017). https://doi.org/10.1007/s40484-017-0106-5
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DOI: https://doi.org/10.1007/s40484-017-0106-5