Synthetic microbial consortia are conglomerations of genetically engineered microbes programmed to cooperatively bring about population-level phenotypes. By coordinating their activity, the constituent strains can display emergent behaviors that are difficult to engineer into isogenic populations. To do so, strains are engineered to communicate with one another through intercellular signaling pathways that depend on cell density.
Here, we used computational modeling to examine how the behavior of synthetic microbial consortia results from the interplay between population dynamics governed by cell growth and internal transcriptional dynamics governed by cell-cell signaling. Specifically, we examined a synthetic microbial consortium in which two strains each produce signals that down-regulate transcription in the other. Within a single strain this regulatory topology is called a “co-repressive toggle switch” and can lead to bistability.
We found that in co-repressive synthetic microbial consortia the existence and stability of different states depend on population-level dynamics. As the two strains passively compete for space within the colony, their relative fractions fluctuate and thus alter the strengths of intercellular signals. These fluctuations drive the consortium to alternative equilibria. Additionally, if the growth rates of the strains depend on their transcriptional states, an additional feedback loop is created that can generate oscillations.
Our findings demonstrate that the dynamics of microbial consortia cannot be predicted from their regulatory topologies alone, but are also determined by interactions between the strains. Therefore, when designing synthetic microbial consortia that use intercellular signaling, one must account for growth variations caused by the production of protein.
Xie, Z., Wroblewska, L., Prochazka, L., Weiss, R. and Benenson, Y. (2011) Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science, 333, 1307–1311
Zhang, F., Carothers, J. and Keasling, J. D. (2012) Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol., 30, 354–359
Masiello, C. A., Chen, Y., Gao, X., Liu, S., Cheng, H.-Y., Bennett, M. R., Rudgers, J. A., Wagner, D. S., Zygourakis, K. Z. and Silberg, J. J. (2013) Biochar and microbial signaling: production conditions determine effects on microbial communication. Environ. Sci. Technol., 47, 11496–11503
Sprinzak, D. and Elowitz, M. B. (2005) Reconstruction of genetic circuits. Nature, 438, 443–448
Wintermute, E. H. and Silver, P. A. (2010) Dynamics in the mixed microbial concourse. Genes Dev., 24, 2603–2614
Chen, Y., Kim, J. K., Hirning, A. J., Josic, K. and Bennett, M. R. (2015) Emergent genetic oscillations in a synthetic microbial consortium. Science, 349, 986–989
González, C., Ray, J. C., Manhart, M., Adams, R. M., Nevozhay, D., Morozov, A. V. and Balázsi, G. (2015) Stress-response balance drives the evolution of a network module and its host genome. Mol. Syst. Biol., 11, 827
Regot, S., Macia, J., Conde, N., Furukawa, K., Kjellen, J., Peeters, T., Hohmann, S., de Nadal, E., Posas, F. and Sole, R. (2011) Distributed biological computation with multicellular engineered networks. Nature, 469, 207–211
Kong, W., Celik, V., Liao, C., Hua, Q. and Lu, T. (2014) Programming the group behaviors of bacterial communities with synthetic cellular communication. Bioresour. Bioprocess., 1, 24
Kanakov, O., Laptyeva, T., Tsimring, L. and Ivanchenko, M. (2016) Spatiotemporal dynamics of distributed synthetic genetic circuits. Physica D, 318–319, 116–123
Blanchard, A. E., Liao, C. and Lu, T. (2016) An ecological understanding of quorum sensing-controlled bacteriocin synthesis. Cell. Mol. Bioeng., 9, 443–454
Tan, C., Marguet, P. and You, L. (2009) Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol., 5, 842–848
Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. and Hwa, T. (2010) Interdependence of cell growth and gene expression: origins and consequences. Science, 330, 1099–1102
Nevozhay, D., Adams, R. M., Van Itallie, E., Bennett, M. R. and Balá zsi, G. (2012) Mapping the environmental fitness landscape of a synthetic gene circuit. PLoS Comput. Biol., 8, e1002480
Gardner, T. S., Cantor, C. R. and Collins, J. J. (2000) Construction of a genetic toggle switch in Escherichia Coli. Nature, 403, 339–342
Miller, M. B. and Bassler, B. L. (2001) Quorum sensing in bacteria. Annu. Rev. Microbiol., 333, 1315–1319
Wu, F., Menn, D. J. and Wang, X. (2014) Quorum-sensing crosstalkdriven synthetic circuits: from unimodality to trimodality. Chem. Biol., 21, 1629–1638
Tabor, J. J., Salis, H. M., Simpson, Z. B., Chevalier, A. A., Levskaya, A., Marcotte, E. M., Voigt, C. A. and Ellington, A. D. (2009) A synthetic genetic edge detection program. Cell, 137, 1272–1281
Bennett, M. R. and Hasty, J. (2009) Overpowering the component problem. Nat. Biotechnol., 27, 450–451
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., 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
Hek, G. (2010) Geometric singular perturbation theory in biological practice. J. Math. Biol., 60, 347–386
Krupa, M. and Szmolyan, P. (2001) Relaxation oscillation and canard explosion. J. Differ. Equ., 174, 312–368
Moran, P. A. P. (1958) Random processes in genetics. Math. Proc. Camb. Philos. Soc., 54, 60–71
Nowak, M. A. (2006) Evolutionary Dynamics: Exploring the Equations of Life. Brighton: Harvard University Press
van der Pol, B. (1926) LXXXVIII. On “relaxation-oscillations”. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2, 978–992
Veliz-Cuba, A., Gupta, C., Bennett, M. R., Josic, K. and Ott, W. (2016) Effects of cell cycle noise on excitable gene circuits. Phys. Biol., 13, 066007
This work was funded by the National Institutes of Health, through the joint NSF/NIGMS grant R01GM104974 (MRB, KJ), the National Science Foundation grant DMS-1122094 (KJ), the Robert A. Welch Foundation grant C-1729 (MRB), and the National Science Foundation grant 1300319 (GO).
This article is dedicated to the Special Collection of Synthetic Biology, Aiming for Quantitative Control of Cellular Systems (Eds. Cheemeng Tan and Haiyan Liu).
These authors contributed equally to this work.
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Sadeghpour, M., Veliz-Cuba, A., Orosz, G. et al. Bistability and oscillations in co-repressive synthetic microbial consortia. Quant Biol 5, 55–66 (2017). https://doi.org/10.1007/s40484-017-0100-y