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Control of synthetic gene networks and its applications |
David J Menn, Ri-Qi Su, Xiao Wang( ) |
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA |
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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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Author Summary Controlling the behavior of gene networks is the basis of much of synthetic biology. Here we review major theoretical concepts underpinning gene regulatory network (GRN) control and how these concepts are implemented to organize biological parts into functional and predictable synthetic GRNs. We present several contexts in which theory and practice have been synthesized in constructed GRNs to generate biologically relevant behaviors: multistability, designed temporal dynamics, and spatial patterning. These proof-of-concept works set researchers up to engineer more complex and controllable circuits in the future. |
Keywords
synthetic biology
gene regulatory networks
modeling
GRN control
stochasticity
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Corresponding Author(s):
Xiao Wang
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Online First Date: 25 May 2017
Issue Date: 07 June 2017
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1 |
Antoni, D., Zverlov, V. V. and Schwarz, W. H. (2007) Biofuels from microbes. Appl. Microbiol. Biotechnol., 77, 23–35
https://doi.org/10.1007/s00253-007-1163-x
|
2 |
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
https://doi.org/10.1186/1475-2859-9-3
|
3 |
Harrison, M. E. and Dunlop, M. J. (2012) Synthetic feedback loop model forincreasing microbial biofuel production using a biosensor. Front. Microbio., 3, 360
https://doi.org/10.3389/fmicb.2012.00360
|
4 |
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
https://doi.org/10.1021/acs.nanolett.5b01943
|
5 |
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
https://doi.org/10.1046/j.1365-2672.2003.02079.x
|
6 |
de Lorenzo, V. (2008) Systems biology approaches to bioremediation. Curr. Opin. Biotechnol., 19, 579–589
https://doi.org/10.1016/j.copbio.2008.10.004
|
7 |
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
https://doi.org/10.1038/nbt833
|
8 |
Ellis, T., Adie, T. and Baldwin, G. S. (2011) DNA assembly for synthetic biology: from parts to pathways and beyond. Integr. Biol., 3, 109
https://doi.org/10.1039/c0ib00070a
|
9 |
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
https://doi.org/10.1038/nmeth.1318
|
10 |
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
https://doi.org/10.1093/nar/gkq165
|
11 |
Shendure, J. and Ji, H. (2008) Next-generation DNA sequencing. Nat. Biotechnol., 26, 1135–1145
https://doi.org/10.1038/nbt1486
|
12 |
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
https://doi.org/10.1038/nmeth.3312
|
13 |
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
https://doi.org/10.1038/nmeth.2969
|
14 |
Standage-Beier, K., Zhang, Q. and Wang, X. (2015) Targeted large-scale deletion of bacterial genomes using CRISPR-nickases. ACS Synth. Biol., 4, 1217–1225
https://doi.org/10.1021/acssynbio.5b00132
|
15 |
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
https://doi.org/10.1038/nature04473
|
16 |
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
https://doi.org/10.1038/nature07389
|
17 |
Elowitz, M. B. and Leibler, S. (2000) A synthetic oscillatory network of transcriptional regulators. Nature, 403, 335–338
https://doi.org/10.1038/35002125
|
18 |
Gardner, T. S., Cantor, C. R. and Collins, J. J. (2000) Construction of a genetic toggle switch in Escherichia coli. Nature, 403, 339–342
https://doi.org/10.1038/35002131
|
19 |
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
https://doi.org/10.1039/C5IB00252D
|
20 |
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
https://doi.org/10.1038/90802
|
21 |
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
https://doi.org/10.1016/j.cell.2016.04.059
|
22 |
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
https://doi.org/10.1126/science.1070919
|
23 |
Wu, F., Menn, D. J. and Wang, X. (2014) Quorum-sensing crosstalk-driven synthetic circuits: from unimodality to trimodality. Chem. Biol., 21, 1629–1638
https://doi.org/10.1016/j.chembiol.2014.10.008
|
24 |
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
https://doi.org/10.1073/pnas.1305423110
|
25 |
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
https://doi.org/10.1038/nature02089
|
26 |
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
https://doi.org/10.1038/nbt.1536
|
27 |
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
https://doi.org/10.1038/nature19841
|
28 |
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
https://doi.org/10.1038/nature03461
|
29 |
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
https://doi.org/10.1126/science.1209042
|
30 |
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
https://doi.org/10.1038/msb.2013.55
|
31 |
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
https://doi.org/10.1126/science.1172005
|
32 |
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
https://doi.org/10.1038/nature09565
|
33 |
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
https://doi.org/10.1038/nmeth.3147
|
34 |
Nielsen, A. A. and Voigt, C. A. (2014) Multi-input CRISPR/Cas genetic circuits that interface host regulatory networks. Mol. Syst. Biol., 10, 763
https://doi.org/10.15252/msb.20145735
|
35 |
Ishimatsu, K., Hata, T., Mochizuki, A., Sekine, R., Yamamura, M. and Kiga, D. (2014) General applicability of synthetic gene-overexpression for cell-type ratio control via reprogramming. ACS Synth. Biol., 3, 638–644
https://doi.org/10.1021/sb400102w
|
36 |
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
https://doi.org/10.1021/sb4000838
|
37 |
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
https://doi.org/10.1073/pnas.1316298111
|
38 |
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
https://doi.org/10.1038/nature04405
|
39 |
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
https://doi.org/10.1038/nature08446
|
40 |
Jogler, C. and Schüler, D. (2009) Genomics, genetics, and cell biology of magnetosome formation. Annu. Rev. Microbiol., 63, 501–521
https://doi.org/10.1146/annurev.micro.62.081307.162908
|
41 |
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
https://doi.org/10.1038/ncomms11323
|
42 |
Shin, Y.-J. and Bleris, L. (2010) Linear control theory for gene network modeling. PLoS One, 5, e12785
https://doi.org/10.1371/journal.pone.0012785
|
43 |
Del Vecchio, D., Dy, A. J. and Qian, Y. (2016) Control theory meets synthetic biology. J. R. Soc. Interface, 13, 20160380
https://doi.org/10.1098/rsif.2016.0380
|
44 |
Kalman, R. E. (1963) Mathematical description of linear dynamical systems. J. Soc. Ind. Appl. Math. Ser. Control, 1, 152–192
https://doi.org/10.1137/0301010
|
45 |
Lin, C.-T. (1974) Structural controllability. IEEE Trans. Automat. Contr., 19, 201–208
https://doi.org/10.1109/TAC.1974.1100557
|
46 |
Keasling, J. D. (2010) Manufacturing molecules through metabolic engineering. Science, 330, 1355–1358
https://doi.org/10.1126/science.1193990
|
47 |
Koffas, M., Roberge, C., Lee, K. and Stephanopoulos, G. (1999) Metabolic engineering. Annu. Rev. Biomed. Eng., 1, 535–557
https://doi.org/10.1146/annurev.bioeng.1.1.535
|
48 |
Polynikis, A., Hogan, S. and di Bernardo, M. (2009) Comparing different ODE modelling approaches for gene regulatory networks. J. Theor. Biol., 261, 511–530
https://doi.org/10.1016/j.jtbi.2009.07.040
|
49 |
Liu, Y.-Y., Slotine, J.-J. and Barabási, A.-L. (2011) Controllability of complex networks. Nature, 473, 167–173
https://doi.org/10.1038/nature10011
|
50 |
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
https://doi.org/10.1101/gr.202648.115
|
51 |
Strogatz, S. H. (2014) Nonlinear Dynamics and Chaos: with Applications to Physics, Biology, Chemistry, and Engineering. Boulder: Westview Press
|
52 |
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
https://doi.org/10.1371/journal.pone.0102873
|
53 |
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
https://doi.org/10.1038http://www.nature.com/ng/journal/v45/n11/abs/ng.2763.html/ng.2763
|
54 |
Thorsley, D. and Klavins, E. (2012) Estimation and discrimination of stochastic biochemical circuits from time-lapse microscopy data. PLoS One, 7, e47151
https://doi.org/10.1371/journal.pone.0047151
|
55 |
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
https://doi.org/10.1073/pnas.162041399
|
56 |
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
https://doi.org/10.1073/pnas.0800579105
|
57 |
Gillespie, D. T. (1977) Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem., 81, 2340–2361
https://doi.org/10.1021/j100540a008
|
58 |
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
https://doi.org/10.1038/msb.2008.24
|
59 |
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
https://doi.org/10.1038/nchembio.244
|
60 |
Song, H. and You, L. (2012) Modeling Spatiotemporal Dynamics of Bacterial Populations. In Computational Modeling of Signaling Networks. New Jersey: Humana Press
|
61 |
Kim, K.-Y. and Wang, J. (2007) Potential energy landscape and robustness of a gene regulatory network: toggle switch. PLoS Comput. Biol., 3, e60
https://doi.org/10.1371/journal.pcbi.0030060
|
62 |
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
https://doi.org/10.1103/PhysRevLett.94.128701
|
63 |
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
https://doi.org/10.1126/science.298.5594.824
|
64 |
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
https://doi.org/10.1016/j.cell.2009.06.013
|
65 |
Mallet, D. G. and De Pillis, L. G. (2006) A cellular automata model of tumor — immune system interactions. J. Theor. Biol., 239, 334–350
https://doi.org/10.1016/j.jtbi.2005.08.002
|
66 |
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
https://doi.org/10.1016/j.semcdb.2009.07.003
|
67 |
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
https://doi.org/10.1103/PhysRevX.5.031036
|
68 |
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
https://doi.org/10.1038/nature02298
|
69 |
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
https://doi.org/10.1016/j.bpj.2008.10.034
|
70 |
Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. and Leibler, S. (2004) Bacterial persistence as a phenotypic switch. Science, 305, 1622–1625
https://doi.org/10.1126/science.1099390
|
71 |
Dhar, N. and McKinney, J. D. (2007) Microbial phenotypic heterogeneity and antibiotic tolerance. Curr. Opin. Microbiol., 10, 30–38
https://doi.org/10.1016/j.mib.2006.12.007
|
72 |
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
https://doi.org/10.1126/science.1069883
|
73 |
Lipshtat, A., Loinger, A., Balaban, N. Q. and Biham, O. (2006) Genetic toggle switch without cooperative binding. Phys. Rev. Lett., 96, 188101
https://doi.org/10.1103/PhysRevLett.96.188101
|
74 |
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
https://doi.org/10.1073/pnas.1332628100
|
75 |
Singh, V. (2014) Recent advancements in synthetic biology: current status and challenges. Gene, 535, 1–11
https://doi.org/10.1016/j.gene.2013.11.025
|
76 |
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
https://doi.org/10.1093/nar/gkn443
|
77 |
Smits, W. K., Eschevins, C. C., Susanna, K. A., Bron, S., Kuipers, O. P. and Hamoen, L. W. (2005) Stripping Bacillus: ComK auto-stimulation is responsible for the bistable response in competence development. Mol. Microbiol., 56, 604–614
https://doi.org/10.1111/j.1365-2958.2005.04488.x
|
78 |
Tan, C., Marguet, P. and You, L. (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol., 5, 842–848
https://doi.org/10.1038/nchembio.218
|
79 |
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
https://doi.org/10.1038/msb.2011.19
|
80 |
Prill, R. J., Iglesias, P. A. and Levchenko, A. (2005) Dynamic properties of network motifs contribute to biological network organization. PLoS Biol., 3, e343
https://doi.org/10.1371/journal.pbio.0030343
|
81 |
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
https://doi.org/10.1126/science.aac7341
|
82 |
Ausländer, S., Ausländer, D., Müller, M., Wieland, M. and Fussenegger, M. (2012) Programmable single-cell mammalian biocomputers. Nature, 487, 123–127
|
83 |
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
https://doi.org/10.1038/nchembio.1433
|
84 |
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
https://doi.org/10.1038/nbt.3044
|
85 |
Del Vecchio, D. (2013) A control theoretic framework for modular analysis and design of biomolecular networks. Annu. Rev. Contr., 37, 333–345
https://doi.org/10.1016/j.arcontrol.2013.09.011
|
86 |
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
https://doi.org/10.1126/science.1261253
|
87 |
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
https://doi.org/10.1073/pnas.1320474111
|
88 |
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
https://doi.org/10.1093/nar/gku233
|
89 |
Ostojic, S. (2014) Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons. Nat. Neurosci., 17, 594–600
https://doi.org/10.1038/nn.3658
|
90 |
Comb, M., Hyman, S. E. and Goodman, H. M. (1987) Mechanisms of trans-synaptic regulation of gene expression. Trends Neurosci., 10, 473–478
https://doi.org/10.1016/0166-2236(87)90103-2
|
91 |
Tigges, M., Marquez-Lago, T. T., Stelling, J. and Fussenegger, M. (2009) A tunable synthetic mammalian oscillator. Nature, 457, 309–312
https://doi.org/10.1038/nature07616
|
92 |
Xiao, M. and Cao, J. (2008) Genetic oscillation deduced from Hopf bifurcation in a genetic regulatory network with delays. Math. Biosci., 215, 55–63
https://doi.org/10.1016/j.mbs.2008.05.004
|
93 |
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
https://doi.org/10.1103/PhysRevE.81.011106
|
94 |
Lewis, J. (2003) Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator. Curr. Biol., 13, 1398–1408
https://doi.org/10.1016/S0960-9822(03)00534-7
|
95 |
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
https://doi.org/10.1101/gad.1696108
|
96 |
Izhikevich, E. M. (2000) Neural excitability, spiking and bursting. Int. J. Bifurcat. Chaos, 10, 1171–1266
https://doi.org/10.1142/S0218127400000840
|
97 |
Fuqua, W. C., Winans, S. C. and Greenberg, E. P. (1994) Quorum sensing in bacteria: the LuxR-LuxI family of cell density-responsive transcriptional regulators. J. Bacteriol., 176, 269–275
https://doi.org/10.1128/jb.176.2.269-275.1994
|
98 |
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
https://doi.org/10.1242/dev.063735
|
99 |
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
https://doi.org/10.1038/nature02491
|
100 |
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
https://doi.org/10.1126/science.1109173
|
101 |
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
https://doi.org/10.1016/j.cell.2016.03.006
|
102 |
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
https://doi.org/10.1073/pnas.1315954111
|
103 |
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
https://doi.org/10.1126/science.aab2956
|
104 |
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
https://doi.org/10.1016/j.cell.2014.04.047
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