Ecological effects of cellular computing in microbial populations

Abstract

Gene regulatory networks allow single cells to adopt a wide range of different phenotypes in response to changes in environmental conditions. The ecological implications of these cellular computations are poorly understood, and they are largely absent from models of microbial community assembly. Here, we highlight a number of examples where ecological interactions are or may be affected by cellular computations. Our review identifies specific opportunities for integrating cellular decision-making into mathematical models of microbe-microbe interactions and community assembly. We argue that incorporating cellular decision-making into microbial ecology will be critical in order to gain a quantitative understanding of microbial biogeography.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. Acar M, Mettetal JT, van Oudenaarden A (2008) Stochastic switching as a survival strategy in fluctuating environments. Nat Genet 40:471–475

    Article  Google Scholar 

  2. Ahmer BMM, Gunn JS (2011) Interaction of Salmonella spp. with the Intestinal Microbiota. Front Microbiol 2:101

    Article  Google Scholar 

  3. Aidelberg G, Towbin BD, Rothschild D et al (2014) Hierarchy of non-glucose sugars in Escherichia coli. BMC Syst Biol 8:133

    Article  Google Scholar 

  4. Arnoldini M, Vizcarra IA, Peña-Miller R et al (2014) Bistable expression of virulence genes in salmonella leads to the formation of an antibiotic-tolerant subpopulation. PLoS Biol 12:e1001928

    Article  Google Scholar 

  5. Axelrod K, Sanchez A, Gore J (2015) Phenotypic states become increasingly sensitive to perturbations near a bifurcation in a synthetic gene network. Elife 4:e07935. https://doi.org/10.7554/eLife.07935

    Article  Google Scholar 

  6. Barrick JE, Lenski RE (2013) Genome dynamics during experimental evolution. Nat Rev Genet 14:827–839

    Article  Google Scholar 

  7. Bashan A, Gibson TE, Friedman J et al (2016) Universality of human microbial dynamics. Nature 534:259–262

    Article  Google Scholar 

  8. Beardmore RE, Gudelj I, Lipson DA, Hurst LD (2011) Metabolic trade-offs and the maintenance of the fittest and the flattest. Nature 472:342–346

    Article  Google Scholar 

  9. Beisel CL, Afroz T (2015) Rethinking the hierarchy of sugar utilization in bacteria. J Bacteriol 198:374–376

    Article  Google Scholar 

  10. Blount ZD, Borland CZ, Lenski RE (2008) Historical contingency and the evolution of a key innovation in an experimental population of Escherichia coli. Proc Natl Acad Sci U S A 105:7899–7906

    Article  Google Scholar 

  11. Blount ZD, Barrick JE, Davidson CJ, Lenski RE (2012) Genomic analysis of a key innovation in an experimental Escherichia coli population. Nature 489:513–518

    Article  Google Scholar 

  12. Bohannan BJM, Kerr B, Jessup CM et al (2002) Trade-offs and coexistence in microbial microcosms. Antonie Van Leeuwenhoek 81:107–115

    Article  Google Scholar 

  13. Cavaliere M, Sanchez A (2016) The evolutionary resilience of distributed cellular computing. In: Membrane computing. Springer, Cham, pp 3–15

    Google Scholar 

  14. Chen A, Sanchez A, Dai L, Gore J (2014) Dynamics of a producer-freeloader ecosystem on the brink of collapse. Nat Commun 5:3713

    Article  Google Scholar 

  15. Chesson P (1990) MacArthur’s consumer-resource model. Theor Popul Biol 37:26–38

    MathSciNet  Article  Google Scholar 

  16. Desai TA, Rao CV (2010) Regulation of arabinose and xylose metabolism in Escherichia coli. Appl Environ Microbiol 76:1524–1532

    Article  Google Scholar 

  17. Diard M, Garcia V, Maier L et al (2013) Stabilization of cooperative virulence by the expression of an avirulent phenotype. Nature 494:353–356

    Article  Google Scholar 

  18. Diard M, Sellin ME, Dolowschiak T et al (2014) Antibiotic treatment selects for cooperative virulence of Salmonella typhimurium. Curr Biol 24:2000–2005

    Article  Google Scholar 

  19. Dickens B, Fisher CK, Mehta P (2016) Analytically tractable model for community ecology with many species. Phys Rev E 94:022423

    Article  Google Scholar 

  20. Escalante-Chong R, Savir Y, Carroll SM et al (2015) Galactose metabolic genes in yeast respond to a ratio of galactose and glucose. Proc Natl Acad Sci U S A 112:1636–1641

    Article  Google Scholar 

  21. Friesen ML, Saxer G, Travisano M, Doebeli M (2004) Experimental evidence for sympatric ecological diversification due to frequency-dependent competition in Escherichia coli. Evolution 58:245–260

    Article  Google Scholar 

  22. Goldford JE, Lu N, Bajic D et al (2018) Emergent simplicity in microbial community assembly. Science 361:469–474

    Article  Google Scholar 

  23. Granados AA, Crane MM, Montano-Gutierrez LF et al (2017) Distributing tasks via multiple input pathways increases cellular survival in stress. Elife 6:e21415. https://doi.org/10.7554/eLife.21415

    Article  Google Scholar 

  24. Gutiérrez M, Gregorio-Godoy P, Pérez Del Pulgar G et al (2017) A new improved and extended version of the multicell bacterial simulator gro. ACS Synth Biol 6:1496–1508

    Article  Google Scholar 

  25. Hall EK, Singer GA, Kainz MJ, Lennon JT (2010) Evidence for a temperature acclimation mechanism in bacteria: an empirical test of a membrane-mediated trade-off. Funct Ecol 24:898–908

    Article  Google Scholar 

  26. Harcombe WR, Delaney NF, Leiby N et al (2013) The ability of flux balance analysis to predict evolution of central metabolism scales with the initial distance to the optimum. PLoS Comput Biol 9:e1003091

    MathSciNet  Article  Google Scholar 

  27. Harrington KI, Sanchez A (2014) Eco-evolutionary dynamics of complex social strategies in microbial communities. Commun Integr Biol 7:e28230

    Article  Google Scholar 

  28. Herron MD, Doebeli M (2013) Parallel evolutionary dynamics of adaptive diversification in Escherichia coli. PLoS Biol 11:e1001490

    Article  Google Scholar 

  29. Hilker M, Schwachtje J, Baier M et al (2016) Priming and memory of stress responses in organisms lacking a nervous system. Biol Rev Camb Philos Soc 91:1118–1133

    Article  Google Scholar 

  30. Høyland-Kroghsbo NM, Maerkedahl RB, Svenningsen SL (2013) A quorum-sensing-induced bacteriophage defense mechanism. MBio 4:e00362–12

    Article  Google Scholar 

  31. Jang SS, Oishi KT, Egbert RG, Klavins E (2012) Specification and simulation of synthetic multicelled behaviors. ACS Synth Biol 1:365–374

    Article  Google Scholar 

  32. Koirala S, Wang X, Rao CV (2015) Reciprocal regulation of l-arabinose and d-xylose metabolism in Escherichia coli. J Bacteriol 198:386–393

    Article  Google Scholar 

  33. Kotte O, Volkmer B, Radzikowski JL, Heinemann M (2014) Phenotypic bistability in Escherichia coli’s central carbon metabolism. Mol Syst Biol 10:736

    Article  Google Scholar 

  34. Lawley TD, Bouley DM, Hoy YE et al (2008) Host transmission of Salmonella enterica serovar Typhimurium is controlled by virulence factors and indigenous intestinal microbiota. Infect Immun 76:403–416

    Article  Google Scholar 

  35. Le Gac M, Brazas MD, Bertrand M et al (2008) Metabolic changes associated with adaptive diversification in Escherichia coli. Genetics 178:1049–1060

    Article  Google Scholar 

  36. Leiby N, Marx CJ (2014) Metabolic erosion primarily through mutation accumulation, and not tradeoffs, drives limited evolution of substrate specificity in Escherichia coli. PLoS Biol 12:e1001789

    Article  Google Scholar 

  37. Levine JM, Bascompte J, Adler PB, Allesina S (2017) Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546:56–64

    Article  Google Scholar 

  38. Li G, Kesler BK, Thiemicke A et al (2017) Linearly changing stress environment causes cellular growth phenotype. bioRxiv 155267

  39. Litchman E, Klausmeier CA, Schofield OM, Falkowski PG (2007) The role of functional traits and trade-offs in structuring phytoplankton communities: scaling from cellular to ecosystem level. Ecol Lett 10:1170–1181

    Article  Google Scholar 

  40. Litchman E, Edwards KF, Klausmeier CA (2015) Microbial resource utilization traits and trade-offs: implications for community structure, functioning, and biogeochemical impacts at present and in the future. Front Microbiol 6:254

    Article  Google Scholar 

  41. Mayfield MM, Stouffer DB (2017) Higher-order interactions capture unexplained complexity in diverse communities. Nat Ecol Evol 1:62

    Article  Google Scholar 

  42. Mehta P, Schwab DJ (2012) Energetic costs of cellular computation. Proc Natl Acad Sci U S A 109:17978–17982

    Article  Google Scholar 

  43. Mitchell A, Pilpel Y (2011) A mathematical model for adaptive prediction of environmental changes by microorganisms. Proc Natl Acad Sci U S A 108:7271–7276

    Article  Google Scholar 

  44. Mitchell A, Romano GH, Groisman B et al (2009) Adaptive prediction of environmental changes by microorganisms. Nature 460:220–224

    Article  Google Scholar 

  45. Mitchell A, Wei P, Lim WA (2015) Oscillatory stress stimulation uncovers an Achilles’ heel of the yeast MAPK signaling network. Science 350:1379–1383

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Monod J (1966) From enzymatic adaptation to allosteric transitions. Science 154:475–483

    Article  Google Scholar 

  48. New AM, Cerulus B, Govers SK et al (2014) Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol 12:e1001764

    Article  Google Scholar 

  49. Perkins TJ, Swain PS (2009) Strategies for cellular decision-making. Mol Syst Biol 5:326

    Article  Google Scholar 

  50. Pos KM, Dimroth P, Bott M (1998) The Escherichia coli citrate carrier CitT: a member of a novel eubacterial transporter family related to the 2-oxoglutarate/malate translocator from spinach chloroplasts. J Bacteriol 180:4160–4165

    Google Scholar 

  51. Posfai A, Taillefumier T, Wingreen NS (2017) Metabolic trade-offs promote diversity in a model ecosystem. Phys Rev Lett 118:028103

    Article  Google Scholar 

  52. Qin X, Sun Q, Yang B et al (2017) Quorum sensing influences phage infection efficiency via affecting cell population and physiological state. J Basic Microbiol 57:162–170

    Article  Google Scholar 

  53. Quandt EM, Gollihar J, Blount ZD et al (2015) Fine-tuning citrate synthase flux potentiates and refines metabolic innovation in the Lenski evolution experiment. Elife 4:e09696. https://doi.org/10.7554/eLife.09696

    Article  Google Scholar 

  54. Ranea JAG, Grant A, Thornton JM, Orengo CA (2005) Microeconomic principles explain an optimal genome size in bacteria. Trends Genet 21:21–25

    Article  Google Scholar 

  55. Regot S, Macia J, Conde N et al (2011) Distributed biological computation with multicellular engineered networks. Nature 469:207–211

    Article  Google Scholar 

  56. Rillig MC, Rolff J, Tietjen B et al (2015) Community priming—effects of sequential stressors on microbial assemblages. FEMS Microbiol Ecol. https://doi.org/10.1093/femsec/fiv040

    Article  Google Scholar 

  57. Robert L, Paul G, Chen Y et al (2010) Pre-dispositions and epigenetic inheritance in the Escherichia coli lactose operon bistable switch. Mol Syst Biol 6:357

    Article  Google Scholar 

  58. Rojo F (2010) Carbon catabolite repression in Pseudomonas: optimizing metabolic versatility and interactions with the environment. FEMS Microbiol Rev 34:658–684

    Article  Google Scholar 

  59. Sanchez A, Gore J (2013) Feedback between population and evolutionary dynamics determines the fate of social microbial populations. PLoS Biol 11:e1001547

    Article  Google Scholar 

  60. Sani E, Herzyk P, Perrella G et al (2013) Hyperosmotic priming of Arabidopsis seedlings establishes a long-term somatic memory accompanied by specific changes of the epigenome. Genome Biol 14:R59

    Article  Google Scholar 

  61. Shank EA, Klepac-Ceraj V, Collado-Torres L et al (2011) Interspecies interactions that result in Bacillus subtilis forming biofilms are mediated mainly by members of its own genus. Proc Natl Acad Sci U S A 108:E1236–43

    Article  Google Scholar 

  62. Singh KD, Schmalisch MH, Stülke J, Görke B (2008) Carbon catabolite repression in Bacillus subtilis: quantitative analysis of repression exerted by different carbon sources. J Bacteriol 190:7275–7284

    Article  Google Scholar 

  63. Skerker JM, Perchuk BS, Siryaporn A et al (2008) Rewiring the specificity of two-component signal transduction systems. Cell 133:1043–1054

    Article  Google Scholar 

  64. Spencer CC, Bertrand M, Travisano M, Doebeli M (2007) Adaptive diversification in genes that regulate resource use in Escherichia coli. PLoS Genet 3:e15

    Article  Google Scholar 

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

    Article  Google Scholar 

  66. Tagkopoulos I, Liu Y-C, Tavazoie S (2008) Predictive behavior within microbial genetic networks. Science 320:1313–1317

    Article  Google Scholar 

  67. Tan D, Dahl A, Middelboe M (2015) Vibriophages differentially influence biofilm formation by Vibrio anguillarum strains. Appl Environ Microbiol 81:4489–4497

    Article  Google Scholar 

  68. Taylor TB, Mulley G, Dills AH et al (2015) Evolution. evolutionary resurrection of flagellar motility via rewiring of the nitrogen regulation system. Science 347:1014–1017

    Article  Google Scholar 

  69. Thiennimitr P, Winter SE, Winter MG et al (2011) Intestinal inflammation allows Salmonella to use ethanolamine to compete with the microbiota. Proc Natl Acad Sci U S A 108:17480–17485

    Article  Google Scholar 

  70. Toll-Riera M, San Millan A, Wagner A, MacLean RC (2016) The genomic basis of evolutionary innovation in Pseudomonas aeruginosa. PLoS Genet 12:e1006005

    Article  Google Scholar 

  71. Turkarslan S, Reiss DJ, Gibbins G et al (2011) Niche adaptation by expansion and reprogramming of general transcription factors. Mol Syst Biol 7:554

    Article  Google Scholar 

  72. Turner CB, Blount ZD, Mitchell DH, Lenski RE (2015) Evolution and coexistence in response to a key innovation in a long-term evolution experiment with Escherichia coli. bioRxiv 020958

  73. van Nimwegen E (2003) Scaling laws in the functional content of genomes. Trends Genet 19:479–484

    Article  Google Scholar 

  74. Venturelli OS, Zuleta I, Murray RM, El-Samad H (2015) Population diversification in a yeast metabolic program promotes anticipation of environmental shifts. PLoS Biol 13:e1002042

    Article  Google Scholar 

  75. Wang J, Atolia E, Hua B et al (2015) Natural variation in preparation for nutrient depletion reveals a cost-benefit tradeoff. PLoS Biol 13:e1002041

    Article  Google Scholar 

  76. Xie L, Wu X-L (2014) Bacterial motility patterns reveal importance of exploitation over exploration in marine microhabitats. Part I: theory. Biophys J 107:1712–1720

    Article  Google Scholar 

  77. Yawata Y, Cordero OX, Menolascina F et al (2014) Competition-dispersal tradeoff ecologically differentiates recently speciated marine bacterioplankton populations. Proc Natl Acad Sci U S A 111:5622–5627

    Article  Google Scholar 

  78. Yi X, Dean AM (2016) Phenotypic plasticity as an adaptation to a functional trade-off. Elife 5:e19307. https://doi.org/10.7554/eLife.19307

    Article  Google Scholar 

  79. Young JW, Locke JCW, Elowitz MB (2013) Rate of environmental change determines stress response specificity. Proc Natl Acad Sci U S A 110:4140–4145

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Alvaro Sanchez.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Baskerville, M., Biro, A., Blazanin, M. et al. Ecological effects of cellular computing in microbial populations. Nat Comput 17, 811–822 (2018). https://doi.org/10.1007/s11047-018-9708-8

Download citation

Keywords

  • Cellular computations
  • Cellular decision-making
  • Microbial interactions
  • Microbial communities
  • Microbial ecology