[en] Microbial populations can adapt to adverse environmental conditions either by appropriately sensing and responding to the changes in their surroundings or by stochastically switching to an alternative phenotypic state. Recent data point out that these two strategies can be exhibited by the same cellular system, depending on the amplitude/frequency of the environmental perturbations and on the architecture of the genetic circuits involved in the adaptation process. Accordingly, several mitigation strategies have been designed for the effective control of microbial populations in different contexts, ranging from biomedicine to bioprocess engineering. Technically, such control strategies have been made possible by the advances made at the level of computational and synthetic biology combined with control theory. However, these control strategies have been applied mostly to synthetic gene circuits, impairing the applicability of the approach to natural circuits. In this review, we argue that it is possible to expand these control strategies to any cellular system and gene circuits based on a metric derived from this information theory, i.e., mutual information (MI). Indeed, based on this metric, it should be possible to characterize the natural frequency of any gene circuits and use it for controlling gene circuits within a population of cells.
Precision for document type :
Review article
Disciplines :
Biotechnology
Author, co-author :
Henrion, Lucas ✱; Université de Liège - ULiège > Département GxABT > Microbial technologies
Delvenne, Mathéo ✱; Université de Liège - ULiège > TERRA Research Centre > Microbial technologies
LH is supported by the FRS-FNRS (Fond National pour la Recherche Scientifique, Belgium) through a FRIA PhD grant. MD is supported by a FNRS PhD grant in the context of an Era-Net Aquatic Pollutant project (ARENA).
Acar M. Mettetal J. T. van Oudenaarden A. (2008). Stochastic switching as a survival strategy in fluctuating environments. Nat. Genet. 40, 471–475. doi: 10.1038/ng.110, PMID: 18362885
Ackermann M. (2015). A functional perspective on phenotypic heterogeneity in microorganisms. Nat. Rev. Microbiol. 13, 497–508. doi: 10.1038/nrmicro3491, PMID: 26145732
Balazsi G. van Oudenaarden A. Collins J. J. (2011). Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925. doi: 10.1016/j.cell.2011.01.030, PMID: 21414483
Banderas A. Le Bec M. Cordier C. Hersen P. (2020). Autonomous and assisted control for synthetic microbiology. Int. J. Mol. Sci. 21:9223. doi: 10.3390/ijms21239223, PMID: 33287299
Benzinger D. Khammash M. (2018). Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation. Nat. Commun. 9:3521. doi: 10.1038/s41467-018-05882-2, PMID: 30166548
Binder D. Drepper T. Jaeger K.-E. Delvigne F. Wiechert W. Kohlheyer D. et al. (2017). Homogenizing bacterial cell factories: analysis and engineering of phenotypic heterogeneity. Metab. Eng. 42, 145–156. doi: 10.1016/j.ymben.2017.06.009, PMID: 28645641
Briat C. Khammash M. (2018). Perfect adaptation and optimal equilibrium productivity in a simple microbial biofuel metabolic pathway using dynamic integral control. ACS Synth. Biol. 7, 419–431. doi: 10.1021/acssynbio.7b00188, PMID: 29343065
Carrasco-López C. García-Echauri S. A. Kichuk T. Avalos J. L. (2020). Optogenetics and biosensors set the stage for metabolic cybergenetics. Curr. Opin. Biotechnol. 65, 296–309. doi: 10.1016/j.copbio.2020.07.012, PMID: 32932048
Chait R. Ruess J. Bergmiller T. Tkacik G. Guet C. C. (2017). Shaping bacterial population behavior through computer-interfaced control of individual cells. Nat. Commun. 8:1535. doi: 10.1038/s41467-017-01683-1, PMID: 29142298
Charvin G. Cross F. R. Siggia E. D. (2009). Forced periodic expression of G1 cyclins phase-locks the budding yeast cell cycle. Proc. Natl. Acad. Sci. U. S. A. 106, 6632–6637. doi: 10.1073/pnas.0809227106, PMID: 19346485
Cheong R. Rhee A. Wang C. J. Nemenman I. Levchenko A. (2011). Information transduction capacity of noisy biochemical signaling networks. Science 334, 354–358. doi: 10.1126/science.1204553, PMID: 21921160
Davidson E. A. Basu A. S. Bayer T. S. (2013). Programming microbes using pulse width modulation of optical signals. J. Mol. Biol. 425, 4161–4166. doi: 10.1016/j.jmb.2013.07.036, PMID: 23928560
Delvigne F. Baert J. Sassi H. Fickers P. Grünberger A. Dusny C. (2017). Taking control over microbial populations: current approaches for exploiting biological noise in bioprocesses. Biotechnol. J. 12:549. doi: 10.1002/biot.201600549, PMID: 28544731
Din M. O. Martin A. Razinkov I. Csicsery N. Hasty J. (2020). Interfacing gene circuits with microelectronics through engineered population dynamics. Sci. Adv. 6:eaaz8344. doi: 10.1126/sciadv.aaz8344, PMID: 32494744
Dusny C. Grunberger A. Probst C. Wiechert W. Kohlheyer D. Schmid A. (2015). Technical bias of microcultivation environments on single-cell physiology. Lab Chip 15, 1822–1834. doi: 10.1039/C4LC01270D, PMID: 25710324
Dusny C. Schmid A. (2014). Microfluidic single-cell analysis links boundary environments and individual microbial phenotypes. Environ. Microbiol. 17, 1839–1856. doi: 10.1111/1462-2920.12667, PMID: 25330456
Elowitz M. B. Leibler S. (2000). A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338. doi: 10.1038/35002125, PMID: 10659856
Fiore G. Matyjaszkiewicz A. Annunziata F. Grierson C. Savery N. J. Marucci L. et al. (2017). In-silico analysis and implementation of a multicellular feedback control strategy in a synthetic bacterial consortium. ACS Synth. Biol. 6, 507–517. doi: 10.1021/acssynbio.6b00220, PMID: 27997140
Fiore G. Perrino G. di Bernardo M. di Bernardo D. (2016). In vivo real-time control of gene expression: a comparative analysis of feedback control strategies in yeast. ACS Synth. Biol. 5, 154–162. doi: 10.1021/acssynbio.5b00135, PMID: 26554583
Fracassi C. Postiglione L. Fiore G. di Bernardo D. (2016). Automatic control of gene expression in mammalian cells. ACS Synth. Biol. 5, 296–302. doi: 10.1021/acssynbio.5b00141, PMID: 26414746
Freddolino P. L. Tavazoie S. (2012). Beyond homeostasis: a predictive-dynamic framework for understanding cellular behavior. Annu. Rev. Cell Dev. Biol. 28, 363–384. doi: 10.1146/annurev-cellbio-092910-154129, PMID: 22559263
Ghusinga K. R. Dennehy J. J. Singh A. (2017). First-passage time approach to controlling noise in the timing of intracellular events. Proc. Natl. Acad. Sci. U. S. A. 114, 693–698. doi: 10.1073/pnas.1609012114, PMID: 28069947
Gonze D. Abou-Jaoudé W. (2013). The goodwin model: behind the hill function. PLoS One 8:e69573. doi: 10.1371/journal.pone.0069573, PMID: 23936338
Gonze D. Ruoff P. (2021). The goodwin oscillator and its legacy. Acta Biotheor. 69, 857–874. doi: 10.1007/s10441-020-09379-8, PMID: 32212037
Goodwin B. C. (1969). Synchronization of Escherichia coli in a chemostat by periodic phosphate feeding. Eur. J. Biochem. 10, 511–514. doi: 10.1111/j.1432-1033.1969.tb00718.x, PMID: 4899926
Grunberger A. Wiechert W. Kohlheyer D. (2014). Single-cell microfluidics: opportunity for bioprocess development. Curr. Opin. Biotechnol. 29, 15–23. doi: 10.1016/j.copbio.2014.02.008, PMID: 24642389
Hansen A. S. O’Shea E. K. (2015). Limits on information transduction through amplitude and frequency regulation of transcription factor activity. Elife 4:e06559. doi: 10.7554/eLife.06559, PMID: 25985085
Isomura A. Kageyama R. (2014). Ultradian oscillations and pulses: coordinating cellular responses and cell fate decisions. Development 141, 3627–3636. doi: 10.1242/dev.104497, PMID: 25249457
Kussell E. Kishony R. Balaban N. Q. Leibler S. (2005). Bacterial persistence: a model of survival in changing environments. Genetics 169, 1807–1814. doi: 10.1534/genetics.104.035352, PMID: 15687275
Kussell E. Leibler S. (2005). Phenotypic diversity, population growth, and information in fluctuating environments. Science 309, 2075–2078. doi: 10.1126/science.1114383, PMID: 16123265
Levchenko A. Nemenman I. (2014). Cellular noise and information transmission. Curr. Opin. Biotechnol. 28, 156–164. doi: 10.1016/j.copbio.2014.05.002, PMID: 24922112
Levine J. H. Lin Y. Elowitz M. B. (2013). Functional roles of pulsing in genetic circuits. Science 342, 1193–1200. doi: 10.1126/science.1239999, PMID: 24311681
Liao M. J. Din M. O. Tsimring L. Hasty J. (2019). Rock-paper-scissors: engineered population dynamics increase genetic stability. Science 365, 1045–1049. doi: 10.1126/science.aaw0542, PMID: 31488693
Lugagne J.-B. Carrillo S. S. Kirch M. Kohler A. Batt G. Hersen P. (2017). Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nat. Commun. 8:1671. doi: 10.1038/s41467-017-01498-0, PMID: 29150615
Lugagne J.-B. Dunlop M. J. (2019). Cell-machine interfaces for characterizing gene regulatory network dynamics. Curr. Opin. Syst. Biol. 14, 1–8. doi: 10.1016/j.coisb.2019.01.001, PMID: 31579842
Mangan S. Alon U. (2003). Structure and function of the feed-forward loop network motif. Proc. Natl. Acad. Sci. U. S. A. 100, 11980–11985. doi: 10.1073/pnas.2133841100, PMID: 14530388
Mangan S. Zaslaver A. Alon U. (2003). The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. J. Mol. Biol. 334, 197–204. doi: 10.1016/j.jmb.2003.09.049, PMID: 14607112
Megerle J. A. Fritz G. Gerland U. Jung K. Rädler J. O. (2008). Timing and dynamics of single cell gene expression in the arabinose utilization system. Biophys. J. 95, 2103–2115. doi: 10.1529/biophysj.107.127191, PMID: 18469087
Mehta P. Goyal S. Long T. Bassler B. L. Wingreen N. S. (2009). Information processing and signal integration in bacterial quorum sensing. Mol. Syst. Biol. 5:325. doi: 10.1038/msb.2009.79, PMID: 19920810
Milias-Argeitis A. Rullan M. Aoki S. K. Buchmann P. Khammash M. (2016). Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat. Commun. 7:12546. doi: 10.1038/ncomms12546, PMID: 27562138
Milias-Argeitis A. Summers S. Stewart-Ornstein J. Zuleta I. Pincus D. El-Samad H. et al. (2011). In silico feedback for in vivo regulation of a gene expression circuit. Nat. Biotechnol. 29, 1114–1116. doi: 10.1038/nbt.2018, PMID: 22057053
Mitchell A. Romano G. H. Groisman B. Yona A. Dekel E. Kupiec M. et al. (2009). Adaptive prediction of environmental changes by microorganisms. Nature 460, 220–224. doi: 10.1038/nature08112, PMID: 19536156
Mondragón-Palomino O. Danino T. Selimkhanov J. Tsimring L. Hasty J. (2011). Entrainment of a population of synthetic genetic oscillators. Science 333, 1315–1319. doi: 10.1126/science.1205369, PMID: 21885786
Nguyen T. M. Telek S. Zicler A. Martinez J. A. Zacchetti B. Kopp J. et al. (2021). Reducing phenotypic instabilities of a microbial population during continuous cultivation based on cell switching dynamics. Biotechnol. Bioeng. 118, 3847–3859. doi: 10.1002/bit.27860, PMID: 34129251
Nikolic N. Schreiber F. Dal Co A. Kiviet D. J. Bergmiller T. Littmann S. et al. (2017). Cell-to-cell variation and specialization in sugar metabolism in clonal bacterial populations. PLoS Genet. 13:e1007122. doi: 10.1371/journal.pgen.1007122, PMID: 29253903
Norman T. M. Lord N. D. Paulsson J. Losick R. (2015). Stochastic switching of cell fate in microbes. Annu. Rev. Microbiol. 69, 381–403. doi: 10.1146/annurev-micro-091213-112852, PMID: 26332088
Pedone E. de Cesare I. Zamora-Chimal C. G. Haener D. Postiglione L. La Regina A. et al. (2021). Cheetah: a computational toolkit for cybergenetic control. ACS Synth. Biol. 10, 979–989. doi: 10.1021/acssynbio.0c00463, PMID: 31578371
Perkins T. J. Swain P. S. (2009). Strategies for cellular decision-making. Mol. Syst. Biol. 5:326. doi: 10.1038/msb.2009.83, PMID: 19920811
Perrino G. Napolitano S. Galdi F. La Regina A. Fiore D. Giuliano T. et al. (2021). Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control. Nat. Commun. 12:2452. doi: 10.1038/s41467-021-22689-w, PMID: 33907191
Perrino G. Wilson C. Santorelli M. di Bernardo D. (2019). Quantitative characterization of α-synuclein aggregation in living cells through automated microfluidics feedback control. Cell Rep. 27, 916.e5–927.e5. doi: 10.1016/j.celrep.2019.03.081, PMID: 30995486
Pilpel Y. (2011). Noise in biological systems: pros, cons, and mechanisms of control. Methods Mol. Biol. 759, 407–425. doi: 10.1007/978-1-61779-173-4_23, PMID: 21863500
Purvis J. E. Lahav G. (2013). Encoding and decoding cellular information through signaling dynamics. Cell 152, 945–956. doi: 10.1016/j.cell.2013.02.005, PMID: 23452846
Rullan M. Benzinger D. Schmidt G. W. Milias-Argeitis A. Khammash M. (2018). An optogenetic platform for real-time, single-cell interrogation of stochastic transcriptional regulation. Mol. Cell 70, 745.e6–756.e6. doi: 10.1016/j.molcel.2018.04.012, PMID: 29775585
Ruoff P. Vinsjevik M. Monnerjahn C. Rensing L. (2001). The Goodwin model: simulating the effect of light pulses on the circadian sporulation rhythm of Neurospora crassa. J. Theor. Biol. 209, 29–42. doi: 10.1006/jtbi.2000.2239, PMID: 11237568
Sagmeister P. Schimek C. Meitz A. Herwig C. Spadiut O. (2014). Tunable recombinant protein expression with E. coli in a mixed-feed environment. Appl. Microbiol. Biotechnol. 98, 2937–2945. doi: 10.1007/s00253-013-5445-1, PMID: 24337346
Sarkar S. Tack D. Ross D. (2020). Sparse estimation of mutual information landscapes quantifies information transmission through cellular biochemical reaction networks. Commun. Biol. 3:203. doi: 10.1038/s42003-020-0901-9, PMID: 32355194
Sassi H. Nguyen T. M. Telek S. Gosset G. Grünberger A. Delvigne F. (2019). Segregostat: a novel concept to control phenotypic diversification dynamics on the example of gram-negative bacteria. Microb. Biotechnol. 12, 1064–1075. doi: 10.1111/1751-7915.13442, PMID: 31141840
Schreiber F. Ackermann M. (2020). Environmental drivers of metabolic heterogeneity in clonal microbial populations. Curr. Opin. Biotechnol. 62, 202–211. doi: 10.1016/j.copbio.2019.11.018, PMID: 31874388
Shoval O. Alon U. (2010). Snap shot: network motifs. Cell 143, 326–326.e1. doi: 10.1016/j.cell.2010.09.050, PMID: 20946989
Stricker J. Cookson S. Bennett M. R. Mather W. H. Tsimring L. S. Hasty J. (2008). A fast, robust and tunable synthetic gene oscillator. Nature 456, 516–519. doi: 10.1038/nature07389, PMID: 18971928
Swain P. S. Elowitz M. B. Siggia E. D. (2002). Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl. Acad. Sci. U. S. A. 99, 12795–12800. doi: 10.1073/pnas.162041399, PMID: 12237400
Tagkopoulos I. Liu Y.-C. Tavazoie S. (2008). Predictive behavior within microbial genetic networks. Science 320, 1313–1317. doi: 10.1126/science.1154456, PMID: 18467556
Tan C. Reza F. You L. (2007). Noise-limited frequency signal transmission in gene circuits. Biophys. J. 93, 3753–3761. doi: 10.1529/biophysj.107.110403, PMID: 17704155
Thattai M. van Oudenaarden A. (2001). Intrinsic noise in gene regulatory networks. Proc. Natl. Acad. Sci. U. S. A. 98, 8614–8619. doi: 10.1073/pnas.151588598, PMID: 11438714
Thattai M. van Oudenaarden A. (2004). Stochastic gene expression in fluctuating environments. Genetics 167, 523–530. doi: 10.1534/genetics.167.1.523, PMID: 15166174
Tian Y. Luo C. Lu Y. Tang C. Ouyang Q. (2012). Cell cycle synchronization by nutrient modulation. Integr. Biol. 4, 328–334. doi: 10.1039/c2ib00083k, PMID: 22262285
Uhlendorf J. Miermont A. Delaveau T. Charvin G. Fages F. Bottani S. et al. (2012). Long-term model predictive control of gene expression at the population and single-cell levels. Proc. Natl. Acad. Sci. U. S. A. 109, 14271–14276. doi: 10.1073/pnas.1206810109, PMID: 22893687
Voigt R. M. Forsyth C. B. Green S. J. Engen P. A. Keshavarzian A. (2016). Circadian rhythm and the gut microbiome. Int. Rev. Neurobiol. 131, 193–205. doi: 10.1016/bs.irn.2016.07.002, PMID: 27793218
Westerwalbesloh C. Grünberger A. Wiechert W. Kohlheyer D. von Lieres E. (2017). Coarse-graining bacteria colonies for modelling critical solute distributions in picolitre bioreactors for bacterial studies on single-cell level. Microb. Biotechnol. 10, 845–857. doi: 10.1111/1751-7915.12708, PMID: 28371389
Wiener N. (1961). Cybernetics: Or Control and Communication in the Animal and the Machine. 2nd Edn. Cambridge, MA, USA: MIT Press.
Wong W. W. Liao J. C. (2006). The design of intracellular oscillators that interact with metabolism. Cell. Mol. Life Sci. 63, 1215–1220. doi: 10.1007/s00018-005-5611-4, PMID: 16649146
Yurkovsky E. Nachman I. (2013). Event timing at the single-cell level. Brief Funct. Genomics 12, 90–98. doi: 10.1093/bfgp/els057, PMID: 23196852
Zhao E. M. Lalwani M. A. Chen J.-M. Orillac P. Toettcher J. E. Avalos J. L. (2021). Optogenetic amplification circuits for light-induced metabolic control. ACS Synth. Biol. 10, 1143–1154. doi: 10.1021/acssynbio.0c00642, PMID: 33835777
Zhao E. M. Zhang Y. Mehl J. Park H. Lalwani M. A. Toettcher J. E. et al. (2018). Optogenetic regulation of engineered cellular metabolism for microbial chemical production. Nature 555, 683–687. doi: 10.1038/nature26141, PMID: 22893687
