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  • Review Article
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Predictive biology: modelling, understanding and harnessing microbial complexity

Abstract

Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.

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Fig. 1: Factors that contribute to growth dynamics.
Fig. 2: A next-generation paradigm of predictive biology.

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References

  1. Blattner, F. R. et al. The complete genome sequence of Escherichia coli K-12. Science 277, 1453–1462 (1997).

    CAS  PubMed  Google Scholar 

  2. Ghatak, S., King, Z. A., Sastry, A. & Palsson, B. O. The y-ome defines the 35% of Escherichia coli genes that lack experimental evidence of function. Nucleic Acids Res. 47, 2446–2454 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000). Along with the repressilator (reference 7), this study was the first implementation of synthetic biology circuits in living cells.

    CAS  PubMed  Google Scholar 

  4. Balázsi, G., Van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).

    PubMed  PubMed Central  Google Scholar 

  5. Kobayashi, H. et al. Programmable cells: interfacing natural and engineered gene networks. Proc. Natl Acad. Sci. USA 101, 8414–8419 (2004).

    CAS  PubMed  Google Scholar 

  6. Wu, M. et al. Engineering of regulated stochastic cell fate determination. Proc. Natl Acad. Sci. USA 110, 10610–10615 (2013).

    CAS  PubMed  Google Scholar 

  7. Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000). Along with the toggle switch (reference 3), this study was the first implementation of synthetic biology circuits in living cells.

    CAS  PubMed  Google Scholar 

  8. Palsson, B. & Zengler, K. The challenges of integrating multi-omic data sets. Nat. Chem. Biol. 6, 787–789 (2010).

    PubMed  Google Scholar 

  9. Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. Mannan, A. A. et al. Integrating kinetic model of E. coli with genome scale metabolic fluxes overcomes its open system problem and reveals bistability in central metabolism. PLoS One 10, e0139507 (2015).

    PubMed  PubMed Central  Google Scholar 

  11. Ocone, A., Haghverdi, L., Mueller, N. S. & Theis, F. J. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics 31, i89–i96 (2015).

  12. Ellis, T., Wang, X. & Collins, J. J. Diversity-based, model-guided construction of synthetic gene networks with predicted functions. Nat. Biotechnol. 27, 465–471 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Tan, C., Marguet, P. & You, L. Emergent bistability by a growth-modulating positive feedback circuit. Nat. Chem. Biol. 5, 842–848 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Smolen, P., Baxter, D. A. & Byrne, J. H. Mathematical modeling of gene networks. Neuron 26, 567–580 (2000).

    CAS  PubMed  Google Scholar 

  15. Ferrell, J. E. Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr. Opin. Cell Biol. 14, 140–148 (2002).

    CAS  PubMed  Google Scholar 

  16. Marguet, P., Tanouchi, Y., Spitz, E., Smith, C. & You, L. Oscillations by minimal bacterial suicide circuits reveal hidden facets of host-circuit physiology. PLoS One 5, e11909 (2010).

    PubMed  PubMed Central  Google Scholar 

  17. Veliz-Cuba, A. et al. Sources of variability in a synthetic gene oscillator. PLoS Comput. Biol. 11, e1004674 (2015).

    PubMed  PubMed Central  Google Scholar 

  18. Lugagne, J. B. et al. Balancing a genetic toggle switch by real-time feedback control and periodic forcing. Nat. Commun. 8, 1671 (2017).

    PubMed  PubMed Central  Google Scholar 

  19. Wu, F., Menn, D. J. & Wang, X. Quorum-sensing crosstalk-driven synthetic circuits: From Unimodality to trimodality. Chem. Biol. 21, 1629–1638 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Melke, P., Sahlin, P., Levchenko, A. & Jönsson, H. A cell-based model for quorum sensing in heterogeneous bacterial colonies. PLoS Comput. Biol. 6, 1–13 (2010).

    Google Scholar 

  21. De Gelder, L. et al. Combining mathematical models and statistical methods to understand and predict the dynamics of antibiotic-sensitive mutants in a population of resistant bacteria during experimental evolution. Genetics 168, 1131–1144 (2004).

    PubMed  PubMed Central  Google Scholar 

  22. Didelot, X. & Maiden, M. C. J. Impact of recombination on bacterial evolution. Trends Microbiol. 18, 315–322 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Shapiro, B. J. et al. Population genomics of early events in the ecological differentiation of bacteria. Science 336, 48–51 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Chalancon, G. et al. Interplay between gene expression noise and regulatory network architecture. Trends Genet. 28, 221–232 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Hooshangi, S., Thiberge, S. & Weiss, R. Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. Proc. Natl Acad. Sci. USA 102, 3581–3586 (2005).

    CAS  PubMed  Google Scholar 

  26. Vind, J., Sørensen, M. A., Rasmussen, M. D. & Pedersen, S. Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes. J. Mol. Biol. 231, 678–688 (1993).

    CAS  PubMed  Google Scholar 

  27. Gorochowski, T. E., Avcilar-Kucukgoze, I., Bovenberg, R. A. L., Roubos, J. A. & Ignatova, Z. A minimal model of ribosome allocation dynamics captures trade-offs in expression between endogenous and synthetic genes. ACS Synth. Biol. 5, 710–720 (2016).

    CAS  PubMed  Google Scholar 

  28. Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1103 (2010).

    CAS  PubMed  Google Scholar 

  29. Carrera, J., Rodrigo, G., Singh, V., Kirov, B. & Jaramillo, A. Empirical model and in vivo characterization of the bacterial response to synthetic gene expression show that ribosome allocation limits growth rate. Biotechnol. J. 6, 773–783 (2011).

    CAS  PubMed  Google Scholar 

  30. Ceroni, F., Algar, R., Stan, G. B. & Ellis, T. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12, 415–418 (2015).

    CAS  PubMed  Google Scholar 

  31. Acar, M., Mettetal, J. T. & Van Oudenaarden, A. Stochastic switching as a survival strategy in fluctuating environments. Nat. Genet. 40, 471–475 (2008).

    CAS  PubMed  Google Scholar 

  32. Keren, L. et al. Promoters maintain their relative activity levels under different growth conditions. Mol. Syst. Biol. 9, 701 (2013). Measured changes in activity levels of 1,800 E. coli promoters and demonstrated that a given promoter’s activity levels in any two different conditions were directly proportional to one another.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Kochanowski, K. et al. Few regulatory metabolites coordinate expression of central metabolic genes in Escherichia coli. Mol. Syst. Biol. 13, 903 (2017).

    PubMed  PubMed Central  Google Scholar 

  34. Fang, X. et al. Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. Proc. Natl Acad. Sci. USA 114, 10286–10291 (2017).

    CAS  PubMed  Google Scholar 

  35. Nikolic, N. et al. Autoregulation of mazEF expression underlies growth heterogeneity in bacterial populations. Nucleic Acids Res. 46, 2918–2931 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Carbonell-Ballestero, M., Garcia-Ramallo, E., Montañez, R., Rodriguez-Caso, C. & Macía, J. Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm’s law. Nucleic Acids Res. 44, 496–507 (2016).

    CAS  PubMed  Google Scholar 

  37. Darlington, A. P. S., Kim, J., Jiménez, J. I. & Bates, D. G. Dynamic allocation of orthogonal ribosomes facilitates uncoupling of co-expressed genes. Nat. Commun. 9, 695 (2018).

    PubMed  PubMed Central  Google Scholar 

  38. Qian, Y., Huang, H. H., Jiménez, J. I. & Del Vecchio, D. Resource competition shapes the response of genetic circuits. ACS Synth. Biol. 6, 1263–1272 (2017).

    CAS  PubMed  Google Scholar 

  39. Gyorgy, A. et al. Isocost lines describe the cellular economy of genetic circuits. Biophys. J. 109, 639–646 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Venturelli, O. S. et al. Programming mRNA decay to modulate synthetic circuit resource allocation. Nat. Commun. 8, 15128 (2017).

    PubMed  PubMed Central  Google Scholar 

  41. Shopera, T., He, L., Oyetunde, T., Tang, Y. J. & Moon, T. S. Decoupling resource-coupled gene expression in living cells. ACS Synth. Biol. 6, 1596–1604 (2017). Computationally and experimentally identified strategies that reduce indirect coupling between gene circuits within a cell.

    CAS  PubMed  Google Scholar 

  42. Cao, Y. et al. Collective space-sensing coordinates pattern scaling in engineered bacteria. Cell 165, 620–630 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Russell, J. B. & Cook, G. M. Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol. Rev. 59, 48–62 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Kempes, C. P. et al. Drivers of bacterial maintenance and minimal energy requirements. Front. Microbiol. 8, 31 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. Brandman, O. et al. A ribosome-bound quality control complex triggers degradation of nascent peptides and signals translation stress. Cell 151, 1042–1054 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Li, G. W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014). Quantification of absolute protein synthesis rates, including l-methionine biosynthesis, revealed that cells optimize protein production to maximize growth efficiency.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Eames, M. & Kortemme, T. Cost-benefit tradeoffs in engineered lac operons. Science 336, 911–915 (2012).

    CAS  PubMed  Google Scholar 

  48. Borkowski, O. et al. Cell-free prediction of protein expression costs for growing cells. Nat. Commun. 9, 1457 (2018). Incorporated cell-free estimates of translation efficiencies into models accounting for growth burden to predict efficient construct designs.

  49. Basan, M. et al. Overflow metabolism in Escherichia coli results from efficient proteome allocation. Nature 528, 99–104 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Macario, A. J. L. & Conway de Macario, E. Molecular chaperones: multiple functions, pathologies, and potential applications. Front. Biosci. 12, 2588–2600 (2007).

    CAS  PubMed  Google Scholar 

  51. Chang, L. et al. High-throughput screen for small molecules that modulate the ATPase activity of the molecular chaperone DnaK. Anal. Biochem. 372, 167–176 (2008).

    CAS  PubMed  Google Scholar 

  52. Kragol, G. et al. The antibacterial peptide pyrrhocoricin inhibits the ATPase actions of DnaK and prevents chaperone-assisted protein folding. Biochemistry 40, 3016–3026 (2001).

    CAS  PubMed  Google Scholar 

  53. Klumpp, S., Scott, M., Pedersen, S. & Hwa, T. Molecular crowding limits translation and cell growth. Proc. Natl Acad. Sci. USA 110, 16754–16759 (2013).

    CAS  PubMed  Google Scholar 

  54. Kafri, M., Metzl-Raz, E., Jona, G. & Barkai, N. The cost of protein production. Cell Rep. 14, 22–31 (2016).

    CAS  PubMed  Google Scholar 

  55. Gerosa, L., Kochanowski, K., Heinemann, M. & Sauer, U. Dissecting specific and global transcriptional regulation of bacterial gene expression. Mol. Syst. Biol. 9, 658 (2013).

    PubMed  PubMed Central  Google Scholar 

  56. Dai, X. et al. Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat. Microbiol. 2, 16231 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. Dai, Z., Huang, M., Chen, Y., Siewers, V. & Nielsen, J. Global rewiring of cellular metabolism renders Saccharomyces cerevisiae Crabtree negative. Nat. Commun. 9, 3059 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Martínez, J. L., Bordel, S., Hong, K. K. & Nielsen, J. Gcn4p and the Crabtree effect of yeast: drawing the causal model of the Crabtree effect in Saccharomyces cerevisiae and explaining evolutionary trade-offs of adaptation to galactose through systems biology. FEMS Yeast Res. 14, 654–662 (2014).

    PubMed  Google Scholar 

  59. Tokic, M., Hatzimanikatis, V. & Miskovic, L. Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies. Biotechnol. Biofuels 13, 33 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Salvy, P. & Hatzimanikatis, V. The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models. Nat. Commun. 11, 30 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Vasilakou, E. et al. Current state and challenges for dynamic metabolic modeling. Curr. Opin. Microbiol. 33, 97–104 (2016).

    CAS  PubMed  Google Scholar 

  62. Bertaux, F., Marguerat, S. & Shahrezaei, V. Division rate, cell size and proteome allocation: Impact on gene expression noise and implications for the dynamics of genetic circuits. R. Soc. Open Sci. 5, 172234 (2018).

    PubMed  PubMed Central  Google Scholar 

  63. Patanè, A., Santoro, A., Costanza, J., Carapezza, G. & Nicosia, G. Pareto optimal design for synthetic biology. IEEE Trans. Biomed. Circuits Syst. 9, 555–571 (2015).

    PubMed  Google Scholar 

  64. Fung, E. et al. A synthetic gene-metabolic oscillator. Nature 435, 118–122 (2005).

    CAS  PubMed  Google Scholar 

  65. Lipson, D. A. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front. Microbiol. 6, 615 (2015).

    PubMed  PubMed Central  Google Scholar 

  66. Monod, J. The growth of bacterial cultures. Annu. Rev. Microbiol. 3, 371–394 (1949).

    CAS  Google Scholar 

  67. Nevozhay, D., Adams, R. M., van Itallie, E., Bennett, M. R. & Balázsi, G. Mapping the environmental fitness landscape of a synthetic gene circuit. PLoS Comput. Biol. 8, e1002480 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Beeftink, H. H., van der Heijden, R. T. J. M. & Heijnen, J. J. Maintenance requirements: energy supply from simultaneous endogenous respiration and substrate consumption. FEMS Microbiol. Lett. 73, 203–209 (1990).

    CAS  Google Scholar 

  69. Kovárová-Kovar, K. & Egli, T. Growth kinetics of suspended microbial cells: from single-substrate-controlled growth to mixed-substrate kinetics. Microbiol. Mol. Biol. Rev. 62, 646–666 (1998).

    PubMed  PubMed Central  Google Scholar 

  70. Luhring, T. M. & DeLong, J. P. Scaling from metabolism to population growth rate to understand how acclimation temperature alters thermal performance. Integr. Comp. Biol. 57, 103–111 (2017).

    CAS  PubMed  Google Scholar 

  71. García-Carreras, B. et al. Role of carbon allocation efficiency in the temperature dependence of autotroph growth rates. Proc. Natl Acad. Sci. USA 115, E7361–E7368 (2018).

    PubMed  Google Scholar 

  72. Lopatkin, A. J. et al. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate. Nat. Microbiol. 4, 2109–2117 (2019). Modelling and experiments revealed that antibiotic lethality can be better predicted by the metabolic state of the cell than the growth rate.

    PubMed  Google Scholar 

  73. Maitra, A. & Dill, K. A. Bacterial growth laws reflect the evolutionary importance of energy efficiency. Proc. Natl Acad. Sci. USA 112, 406–411 (2015).

    CAS  PubMed  Google Scholar 

  74. Aidelberg, G. et al. Hierarchy of non-glucose sugars in Escherichia coli. BMC Syst. Biol. 8, 133 (2014).

    PubMed  PubMed Central  Google Scholar 

  75. Erickson, D. W. et al. A global resource allocation strategy governs growth transition kinetics of Escherichia coli. Nature 551, 119–123 (2017). Developed a quantitative model of bacterial growth during nutrient transitions using a coarse-grained approach.

    PubMed  PubMed Central  Google Scholar 

  76. Hui, S. et al. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol. Syst. Biol. 11, 784 (2015).

    PubMed  PubMed Central  Google Scholar 

  77. Waschina, S., D’Souza, G., Kost, C. & Kaleta, C. Metabolic network architecture and carbon source determine metabolite production costs. FEBS J. 283, 2149–2163 (2016).

    CAS  PubMed  Google Scholar 

  78. Madar, D. et al. Promoter activity dynamics in the lag phase of Escherichia coli. BMC Syst. Biol. 7, 136 (2013).

    PubMed  PubMed Central  Google Scholar 

  79. Berthoumieux, S. et al. Shared control of gene expression in bacteria by transcription factors and global physiology of the cell. Mol. Syst. Biol. 9, 634 (2013).

    PubMed  PubMed Central  Google Scholar 

  80. Wang, X., Xia, K., Yang, X. & Tang, C. Growth strategy of microbes on mixed carbon sources. Nat. Commun. 10, 1279 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).

    CAS  PubMed  Google Scholar 

  82. Boon, E. et al. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol. Rev. 38, 90–118 (2014).

    CAS  PubMed  Google Scholar 

  83. Heyland, J., Blank, L. M. & Schmid, A. Quantification of metabolic limitations during recombinant protein production in Escherichia coli. J. Biotechnol. 155, 178–184 (2011).

    CAS  PubMed  Google Scholar 

  84. Hottes, A. K. et al. Bacterial Adaptation through Loss of Function. PLoS Genet. 9, e1003617 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Morris, J. J., Lenski, R. E. & Zinser, E. R. The Black Queen Hypothesis: evolution of dependencies through adaptive gene loss. mBio 3, e00036-12 (2012).

    PubMed  PubMed Central  Google Scholar 

  86. Tsoi, R. et al. Metabolic division of labor in microbial systems. Proc. Natl Acad. Sci. USA 115, 2526–2531 (2018).

    CAS  PubMed  Google Scholar 

  87. Thommes, M., Wang, T., Zhao, Q., Paschalidis, I. C. & Segrè, D. Designing metabolic division of labor in microbial communities. mSystems 4, e00263-18 (2019).

    PubMed  PubMed Central  Google Scholar 

  88. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002).

    CAS  PubMed  Google Scholar 

  89. Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nat. Genet. 31, 69–73 (2002).

    CAS  PubMed  Google Scholar 

  90. Lopatkin, A. J. et al. Persistence and reversal of plasmid-mediated antibiotic resistance. Nat. Commun. 8, 1689 (2017).

    PubMed  PubMed Central  Google Scholar 

  91. Harcombe, W. R. et al. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep. 7, 1104–1115 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Knudsen, G. M., Ng, Y. & Gram, L. Survival of bactericidal antibiotic treatment by a persister subpopulation of Listeria monocytogenes. Appl. Environ. Microbiol. 79, 7390–7397 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Finkelshtein, A., Roth, D., Jacob, E. Ben & Ingham, C. J. Bacterial swarms recruit cargo bacteria to pave the way in toxic environments. mBio 6, e00074-15 (2015).

    PubMed  PubMed Central  Google Scholar 

  94. La Sarre, B., McCully, A. L., Lennon, J. T. & McKinlay, J. B. Microbial mutualism dynamics governed by dose-dependent toxicity of cross-fed nutrients. ISME J. 11, 337–348 (2017).

    Google Scholar 

  95. Adamowicz, E. M., Flynn, J., Hunter, R. C. & Harcombe, W. R. Cross-feeding modulates antibiotic tolerance in bacterial communities. ISME J. 12, 2723–2735 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Wilson, C. E. et al. Cooperation and competition shape ecological resistance during periodic spatial disturbance of engineered bacteria. Sci. Rep. 7, 440 (2017).

  97. Song, H., Payne, S., Gray, M. & You, L. Spatiotemporal modulation of biodiversity in a synthetic chemical-mediated ecosystem. Nat. Chem. Biol. 5, 929–935 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Tanouchi, Y., Pai, A., Buchler, N. E. & You, L. Programming stress-induced altruistic death in engineered bacteria. Mol. Syst. Biol. 8, 626 (2012).

    PubMed  PubMed Central  Google Scholar 

  99. Harrison, E. & Brockhurst, M. A. Plasmid-mediated horizontal gene transfer is a coevolutionary process. Trends Microbiol. 20, 262–267 (2012).

    CAS  PubMed  Google Scholar 

  100. Blake, W. J., Kærn, M., Cantor, C. R. & Collins, J. J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003).

    CAS  PubMed  Google Scholar 

  101. Kærn, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    PubMed  Google Scholar 

  102. Taheri-Araghi, S. et al. Cell-size control and homeostasis in bacteria. Curr. Biol. 25, 385–391 (2015).

    CAS  PubMed  Google Scholar 

  103. Tanouchi, Y. et al. A noisy linear map underlies oscillations in cell size and gene expression in bacteria. Nature 523, 357–360 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Campos, M. et al. A constant size extension drives bacterial cell size homeostasis. Cell 159, 1433–1446 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Kleijn, I. T., Krah, L. H. J. & Hermsen, R. Noise propagation in an integrated model of bacterial gene expression and growth. PLoS Comput. Biol. 14, 1–18 (2018).

    Google Scholar 

  106. Hasty, J., Pradines, J., Dolnik, M. & Collins, J. J. Noise-based switches and amplifiers forgene expression. Proc. Natl Acad. Sci. USA 97, 2075–2080 (2000).

    CAS  PubMed  Google Scholar 

  107. Isaacs, F. J., Hasty, J., Cantor, C. R. & Collins, J. J. Prediction and measurement of an autoregulatory genetic module. Proc. Natl Acad. Sci. USA 100, 7714–7719 (2003).

    CAS  PubMed  Google Scholar 

  108. Cerulus, B., New, A. M., Pougach, K. & Verstrepen, K. J. Noise and epigenetic inheritance of single-cell division times influence population fitness. Curr. Biol. 26, 1138–1147 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Ni, M. et al. Pre-disposition and epigenetics govern variation in bacterial survival upon stress. PLoS Genet. 8, e1003148 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. El Meouche, I., Siu, Y. & Dunlop, M. J. Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells. Sci. Rep. 6, 19538 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    CAS  PubMed  Google Scholar 

  112. Blake, W. J. et al. Phenotypic consequences of promoter-mediated transcriptional noise. Mol. Cell 24, 853–865 (2006).

    CAS  PubMed  Google Scholar 

  113. Stokes, J. M., Lopatkin, A. J., Lobritz, M. A. & Collins, J. J. Bacterial metabolism and antibiotic efficacy. Cell Metab. 30, 251–259 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Yang, J. H., Bening, S. C. & Collins, J. J. Antibiotic efficacy — context matters. Curr. Opin. Microbiol. 39, 73–80 (2017).

    PubMed  PubMed Central  Google Scholar 

  115. Brauner, A., Fridman, O., Gefen, O. & Balaban, N. Q. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat. Rev. Microbiol. 14, 320–330 (2016).

    CAS  PubMed  Google Scholar 

  116. Narula, J., Devi, S. N., Fujita, M. & Igoshin, O. A. Ultrasensitivity of the Bacillus subtilis sporulation decision. Proc. Natl Acad. Sci. USA 109, E3513–E3522 (2012).

    CAS  PubMed  Google Scholar 

  117. Chastanet, A. et al. Broadly heterogeneous activation of the master regulator for sporulation in Bacillus subtilis. Proc. Natl Acad. Sci. USA 107, 8486–8491 (2010).

    CAS  PubMed  Google Scholar 

  118. Schultz, D., Wolynes, P. G., Jacob, E. Ben & Onuchic, J. N. Deciding fate in adverse times: Sporulation and competence in Bacillus subtilis. Proc. Natl Acad. Sci. USA 106, 21027–21034 (2009).

    CAS  PubMed  Google Scholar 

  119. Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).

    PubMed  Google Scholar 

  120. Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018). Trained a computational model that could predict a synthetic multi-species gut community based on monoculture and pairwise growth data.

    PubMed  PubMed Central  Google Scholar 

  121. Enke, T. N. et al. Modular assembly of polysaccharide-degrading marine microbial communities. Curr. Biol. 29, 1528–1535.e6 (2019).

    CAS  PubMed  Google Scholar 

  122. Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Lawrence, D. et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 10, e1001330 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Campbell, K. et al. Self-establishing communities enable cooperative metabolite exchange in a eukaryote. eLife 4, e09943 (2015).

    PubMed  PubMed Central  Google Scholar 

  125. Sanchez, A. & Gore, J. Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLoS Biol. 11, 1001547 (2013).

    Google Scholar 

  126. Hekstra, D. R. & Leibler, S. Contingency and statistical laws in replicate microbial closed ecosystems. Cell 149, 1164–1173 (2012).

    CAS  PubMed  Google Scholar 

  127. Jackson, M. A. et al. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations. PeerJ 6, e4303 (2018).

    PubMed  PubMed Central  Google Scholar 

  128. Hsu, R. H. et al. Microbial Interaction network inference in microfluidic droplets. Cell Syst. 9, 229–242.e4 (2019).

    CAS  PubMed  Google Scholar 

  129. Mettetal, J. T., Muzzey, D., Pedraza, J. M., Ozbudak, E. M. & Van Oudenaarden, A. Predicting stochastic gene expression dynamics in single cells. Proc. Natl Acad. Sci. USA 103, 7304–7309 (2006).

    CAS  PubMed  Google Scholar 

  130. Lord, N. D. et al. Stochastic antagonism between two proteins governs a bacterial cell fate switch. Science 366, 116–120 (2019).

    CAS  PubMed  Google Scholar 

  131. Fridman, O., Goldberg, A., Ronin, I., Shoresh, N. & Balaban, N. Q. Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature 513, 418–421 (2014).

    CAS  PubMed  Google Scholar 

  132. Lee, H., Popodi, E., Tang, H. & Foster, P. L. Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing. Proc. Natl Acad. Sci. USA 109, E2774–E2783 (2012).

    CAS  PubMed  Google Scholar 

  133. Chevereau, G. et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biol. 13, e1002299 (2015).

    PubMed  PubMed Central  Google Scholar 

  134. Chan, C. T., Lee, J. W., Cameron, D. E., Bashor, C. J. & Collins, J. J. ‘Deadman’ and ‘Passcode’ microbial kill switches for bacterial containment. Nat. Chem. Biol. 12, 82–86 (2016).

    CAS  PubMed  Google Scholar 

  135. Liao, M. J., Din, M. O., Tsimring, L. & Hasty, J. Rock-paper-scissors: engineered population dynamics increase genetic stability. Science 365, 1045–1049 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Scott, M., Klumpp, S., Mateescu, E. M. & Hwa, T. Emergence of robust growth laws from optimal regulation of ribosome synthesis. Mol. Syst. Biol. 10, 747 (2014).

    PubMed  PubMed Central  Google Scholar 

  137. English, M. A. et al. Programmable CRISPR-responsive smart materials. Science 365, 780–785 (2019).

    CAS  PubMed  Google Scholar 

  138. Bothfeld, W., Kapov, G. & Tyo, K. E. J. A glucose-sensing toggle switch for autonomous, high productivity genetic control. ACS Synth. Biol. 6, 1296–1304 (2017).

    CAS  PubMed  Google Scholar 

  139. Perez-Carrasco, R. et al. Combining a toggle switch and a repressilator within the AC-DC circuit generates distinct dynamical behaviors. Cell Syst. 6, 521–530.e3 (2018). Modelling of the integrated toggle switch and repressilator circuits revealed complex emergent behaviour inaccessible to either circuit individually.

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Luro, S., Potvin-Trottier, L., Okumus, B. & Paulsson, J. Isolating live cells after high-throughput, long-term, time-lapse microscopy. Nat. Methods 17, 93–100 (2020).

    CAS  PubMed  Google Scholar 

  141. Yeung, E. et al. Biophysical constraints arising from compositional context in synthetic gene networks. Cell Syst. 5, 11–24.e12 (2017).

    CAS  PubMed  Google Scholar 

  142. Briat, C., Gupta, A. & Khammash, M. Antithetic integral feedback ensures robust perfect adaptation in noisy bimolecular networks. Cell Syst. 2, 15–26 (2016).

    CAS  PubMed  Google Scholar 

  143. Potvin-Trottier, L., Lord, N. D., Vinnicombe, G. & Paulsson, J. Synchronous long-term oscillations in a synthetic gene circuit. Nature 538, 514–517 (2016).

    PubMed  PubMed Central  Google Scholar 

  144. Meyer, A. J., Segall-Shapiro, T. H., Glassey, E., Zhang, J. & Voigt, C. A. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat. Chem. Biol. 15, 196–204 (2019).

    CAS  PubMed  Google Scholar 

  145. Chen, Y. J. et al. Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nat. Methods 10, 659–664 (2013).

    CAS  PubMed  Google Scholar 

  146. Niederholtmeyer, H. & Sun, Z. Z. Rapid cell-free forward engineering of novel genetic ring oscillators. eLife 4, e09771 (2015).

    PubMed  PubMed Central  Google Scholar 

  147. Moon, T. S., Lou, C., Tamsir, A., Stanton, B. C. & Voigt, C. A. Genetic programs constructed from layered logic gates in single cells. Nature 491, 249–253 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  148. Tomazou, M., Barahona, M., Polizzi, K. M. & Stan, G.-B. Computational re-design of synthetic genetic oscillators for independent amplitude and frequency modulation. Cell Syst. 6, 508–520.e5 (2018).

    CAS  PubMed  Google Scholar 

  149. Kong, W., Meldgin, D. R., Collins, J. J. & Lu, T. Designing microbial consortia with defined social interactions. Nat. Chem. Biol. 14, 821–829 (2018).

    CAS  PubMed  Google Scholar 

  150. Solé, R. V., Montañez, R. & Duran-Nebreda, S. Synthetic circuit designs for earth terraformation. Biol. Direct 10, 37 (2015).

    PubMed  PubMed Central  Google Scholar 

  151. Hiscock, T. W. Adapting machine-learning algorithms to design gene circuits. BMC Bioinformatics 20, 214 (2019).

    PubMed  PubMed Central  Google Scholar 

  152. Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C. & Collins, J. J. Next-generation machine learning for biological networks. Cell 173, 1581–1592 (2018).

    CAS  PubMed  Google Scholar 

  153. Wu, F. et al. A unifying framework for interpreting and predicting mutualistic systems. Nat. Commun. 10, 242 (2019). Derived a simplified criterion using a support vector machine approach that predicts the outcome of a mutualistic bacterial community regardless of the underlying interactions.

    PubMed  PubMed Central  Google Scholar 

  154. Zhang, P.-Y. et al. Combined treatment with the antibiotics kanamycin and streptomycin promotes the conjugation of Escherichia coli. FEMS Microbiol. Lett. 348, 149–156 (2013).

    CAS  PubMed  Google Scholar 

  155. Schuurmans, J. M. et al. Effect of growth rate and selection pressure on rates of transfer of an antibiotic resistance plasmid between E. coli strains. Plasmid 72, 1–8 (2014).

    CAS  PubMed  Google Scholar 

  156. Lopatkin, A. J., Sysoeva, T. A. & You, L. Dissecting the effects of antibiotics on horizontal gene transfer: analysis suggests a critical role of selection dynamics. Bioessays 38, 1283–1292 (2016). Demonstrated that antibiotics do not promote horizontal gene transfer as previously thought by combining quantitative measurements of plasmid conjugation and mathematical modelling of population dynamics.

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Lopatkin, A. J. et al. Antibiotics as a selective driver for conjugation dynamics. Nat. Microbiol. 1, 16044 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Headd, B. & Bradford, S. A. Physicochemical factors that favor conjugation of an antibiotic resistant plasmid in non-growing bacterial cultures in the absence and presence of antibiotics. Front. Microbiol. 9, 2122 (2018).

    PubMed  PubMed Central  Google Scholar 

  159. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Scott, S. R. et al. A stabilized microbial ecosystem of self-limiting bacteria using synthetic quorum-regulated lysis. Nat. Microbiol. 2, 17083 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Schaerli, Y. et al. Synthetic circuits reveal how mechanisms of gene regulatory networks constrain evolution. Mol. Syst. Biol. 14, e8102 (2018).

    PubMed  PubMed Central  Google Scholar 

  162. Rodriguez de Evgrafov, M., Gumpert, H., Munck, C., Thomsen, T. T. & Sommer, M. O. A. Collateral resistance and sensitivity modulate evolution of high-level resistance to drug combination treatment in Staphylococcus aureus. Mol. Biol. Evol. 32, 1175–1185 (2015).

    CAS  PubMed  Google Scholar 

  163. Wood, K., Nishida, S., Sontag, E. D. & Cluzel, P. Mechanism-independent method for predicting response to multidrug combinations in bacteria. Proc. Natl Acad. Sci. USA 109, 12254–12259 (2012).

    CAS  PubMed  Google Scholar 

  164. Meredith, H. R., Lopatkin, A. J., Anderson, D. J. & You, L. Bacterial temporal dynamics enable optimal design of antibiotic treatment. PLoS Comput. Biol. 11, e1004201 (2015).

    PubMed  PubMed Central  Google Scholar 

  165. Yang, J. H. et al. A white-box machine learning approach for revealing antibiotic mechanisms of action. Cell 177, 1649–1661.e9 (2019). Integrated machine learning with metabolic network modelling to uncover novel metabolism-related mechanisms of action for bactericidal antibiotics.

    CAS  PubMed  PubMed Central  Google Scholar 

  166. Nichol, D. et al. Antibiotic collateral sensitivity is contingent on the repeatability of evolution. Nat. Commun. 10, 334 (2019).

    PubMed  PubMed Central  Google Scholar 

  167. Nguyen, M. et al. Using machine learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. J. Clin. Microbiol. 57, e01260-18 (2019).

    PubMed  PubMed Central  Google Scholar 

  168. Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. van der Ark, K. C. H. et al. Model-driven design of a minimal medium for Akkermansia muciniphila confirms mucus adaptation. Microb. Biotechnol. 11, 476–485 (2018).

    PubMed  PubMed Central  Google Scholar 

  170. Wang, S. et al. Massive computational acceleration by using neural networks to emulate mechanism-based biological models. Nat. Commun. 10, 4354 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Hart, S. F. M. et al. Uncovering and resolving challenges of quantitative modeling in a simplified community of interacting cells. PLoS Biol. 17, e3000135 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Cremer, J., Arnoldini, M. & Hwa, T. Effect of water flow and chemical environment on microbiota growth and composition in the human colon. Proc. Natl Acad. Sci. USA 114, 6438–6443 (2017).

    CAS  PubMed  Google Scholar 

  173. Heckmann, D. et al. Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models. Nat. Commun. 9, 5252 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Friedman, N. Inferring cellular networks using probabilistic graphical models. Science 303, 799–805 (2004).

    CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank Lingchong You for his feedback on the manuscript. This work was supported by the Defence Threat Reduction Agency (HDTRA1-15-1-0051), the Broad Institute of MIT and Harvard, and a generous gift from Anita and Josh Bekenstein.

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A.J.L. researched data for the manuscript. A.J.L. and J.J.C. outlined the manuscript. A.J.L. wrote the manuscript. A.J.L. and J.J.C. reviewed and edited the manuscript before submission.

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Correspondence to James J. Collins.

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Glossary

Genetic toggle switch

A synthetic gene circuit consisting of two mutually inhibitory repressor genes, their associated promoters and a reporter gene; bistable feedback results in the circuit stably assuming one of two states (that is, toggling between reporter gene ON and OFF) in response to the transient application of exogenous inducers.

Bistable

A system that can exhibit two distinct stable states.

Repressilator

A synthetic gene circuit consisting of three repressor genes, arranged such that gene A represses gene B, gene B represses gene C, and gene C represses gene A; this architecture enables the circuit to produce oscillatory outputs (for example, a reporter gene will cycle between ON and OFF states).

Discrete dynamical models

Changes in the state variable(s) occur at particular time points and remain fixed in between time points.

Continuous dynamical models

Changes in the state variable(s) occur uninterrupted (for example, continuously) over an arbitrary time window.

Ordinary differential equations

(ODEs). A set of equations describing the relationship between the derivative of one or more dependent variables with respect to one independent variable.

Deterministic

A type of process whereby the future state of a system involves no randomness and depends entirely on its initial state along with the parameters that govern its change.

Stochastic

A type of process whereby the future state of a system involves a certain degree of randomness.

Dynamical models

Mathematical models that describe the change in one or more variables of interest over time.

Deep learning

A class of artificial intelligence frameworks that are able to learn in an unsupervised manner, typically using structured layers of artificial neural networks.

Quorum sensing

(QS). A control strategy wherein individual cells secrete a signal molecule into their environment; the collective concentration serves as a proxy for local cell density and enables cells to trigger downstream gene expression based on population size.

Crabtree effect

The phenomenon wherein yeast produce ethanol rather than biomass under high-glucose aerobic conditions.

Chemostat

A type of bioreactor that uses inflow and outflow of new or spent media to continuously culture microorganisms at a specified growth rate under chemically defined conditions.

Support vector machine

A trained machine-learning methodology that uses classification algorithms to separate data into multiple groupings.

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Lopatkin, A.J., Collins, J.J. Predictive biology: modelling, understanding and harnessing microbial complexity. Nat Rev Microbiol 18, 507–520 (2020). https://doi.org/10.1038/s41579-020-0372-5

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