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Annual Review of Biophysics

Volume 50, 2021

From Bench to Keyboard and Back Again: A Brief History of Lambda Phage Modeling

Abstract

Cellular decision making is the process whereby cells choose one developmental pathway from multiple possible ones, either spontaneously or due to environmental stimuli. Examples in various cell types suggest an almost inexhaustible plethora of underlying molecular mechanisms. In general, cellular decisions rely on the gene regulatory network, which integrates external signals to drive cell fate choice. The search for general principles of such a process benefits from appropriate biological model systems that reveal how and why certain gene regulatory mechanisms drive specific cellular decisions according to ecological context and evolutionary outcomes. In this article, we review the historical and ongoing development of the phage lambda lysis–lysogeny decision as a model system to investigate all aspects of cellular decision making. The unique generality, simplicity, and richness of phage lambda decision making render it a constant source ofmathematical modeling–aided inspiration across all of biology. We discuss the origins and progress of quantitative phage lambda modeling from the 1950s until today, as well as its possible future directions. We provide examples of how modeling enabled methods and theory development, leading to new biological insights by revealing gaps in the theory and pinpointing areas requiring further experimental investigation. Overall, we highlight the utility of theoretical approaches both as predictive tools, to forecast the outcome of novel experiments, and as explanatory tools, to elucidate the natural processes underlying experimental data.

Keyword(s): bistable, cellular decision making, evolution, phage lambda, stochastic, threshold
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2021-05-06
2024-04-25
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Literature Cited

  1. 1. 
    Ackers GK, Johnson AD, Shea MA 1982. Quantitative model for gene regulation by lambda phage repressor. PNAS 79:1129–33
     [Google Scholar]
  2. 2. 
    Albert R. 2005. Scale-free networks in cell biology. J. Cell Sci. 118:4947–57
     [Google Scholar]
  3. 3. 
    Albert R, Thakar J. 2014. Boolean modeling: a logic-based dynamic approach for understanding signaling and regulatory networks and for making useful predictions. Wiley Interdiscip. Rev. Syst. Biol. Med. 6:353–69
     [Google Scholar]
  4. 4. 
    Angeli D, Ferrell JE Jr., Sontag ED. 2004. Detection of multistability, bifurcations, and hysteresis in a large class of biological positive-feedback systems. PNAS 101:1822–27
     [Google Scholar]
  5. 5. 
    Arkin A, Ross J, McAdams HH. 1998. Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells. Genetics 149:1633–48
     [Google Scholar]
  6. 6. 
    Babu MM, Luscombe NM, Aravind L, Gerstein M, Teichmann SA. 2004. Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14:283–91
     [Google Scholar]
  7. 7. 
    Balázsi G, Barabasi AL, Oltvai ZN 2005. Topological units of environmental signal processing in the transcriptional regulatory network of Escherichia coli. PNAS 102:7841–46
     [Google Scholar]
  8. 8. 
    Balázsi G, van Oudenaarden A, Collins JJ. 2011. Cellular decision making and biological noise: from microbes to mammals. Cell 144:910–25
     [Google Scholar]
  9. 9. 
    Becskei A, Serrano L. 2000. Engineering stability in gene networks by autoregulation. Nature 405:590–93
     [Google Scholar]
  10. 10. 
    Bednarz M, Halliday JA, Herman C, Golding I. 2014. Revisiting bistability in the lysis/lysogeny circuit of bacteriophage lambda. PLOS ONE 9:e100876
     [Google Scholar]
  11. 11. 
    Berngruber TW, Froissart R, Choisy M, Gandon S. 2013. Evolution of virulence in emerging epidemics. PLOS Pathog 9:e1003209
     [Google Scholar]
  12. 12. 
    Berngruber TW, Lion S, Gandon S. 2015. Spatial structure, transmission modes and the evolution of viral exploitation strategies. PLOS Pathog 11:e1004810
     [Google Scholar]
  13. 13. 
    Blake WJ, Kærn M, Cantor CR, Collins JJ 2003. Noise in eukaryotic gene expression. Nature 422:633–37
     [Google Scholar]
  14. 14. 
    Bossi L, Fuentes JA, Mora G, Figueroa-Bossi N. 2003. Prophage contribution to bacterial population dynamics. J. Bacteriol. 185:6467–71
     [Google Scholar]
  15. 15. 
    Bradde S, Nourmohammad A, Goyal S, Balasubramanian V 2020. The size of the immune repertoire of bacteria. PNAS 117:5144–51
     [Google Scholar]
  16. 16. 
    Buchler NE, Gerland U, Hwa T 2003. On schemes of combinatorial transcription logic. PNAS 100:5136–41
     [Google Scholar]
  17. 17. 
    Chapon C. 1982. Expression of malT, the regulator gene of the maltose region in Escherichia coli, is limited both at transcription and translation. EMBO J 1:369–74
     [Google Scholar]
  18. 18. 
    Cortes MG, Krog J, Balázsi G. 2019. Optimality of the spontaneous prophage induction rate. J. Theor. Biol 483:110005
     [Google Scholar]
  19. 19. 
    Cortes MG, Trinh JT, Zeng L, Balázsi G. 2017. Late-arriving signals contribute less to cell-fate decisions. Biophys. J. 113:2110–20
     [Google Scholar]
  20. 20. 
    Dawkins R. 2006. The Selfish Gene Oxford, UK: Oxford Univ. Press
  21. 21. 
    Echols H. 1972. Developmental pathways for the temperate phase: lysis vs lysogeny. Annu. Rev. Genet. 6:157–90
     [Google Scholar]
  22. 22. 
    Fang X, Liu Q, Bohrer C, Hensel Z, Han W et al. 2018. Cell fate potentials and switching kinetics uncovered in a classic bistable genetic switch. Nat. Commun. 9:2787
     [Google Scholar]
  23. 23. 
    Faumont S, Lindsay TH, Lockery SR. 2012. Neuronal microcircuits for decision making in C. elegans. Curr. Opin. Neurobiol. 22:580–91
     [Google Scholar]
  24. 24. 
    Ferrell JE Jr., Ha SH. 2014. Ultrasensitivity part III: cascades, bistable switches, and oscillators. Trends Biochem. Sci. 39:612–18
     [Google Scholar]
  25. 25. 
    Fry BA. 1959. Conditions for the infection of Escherichia coli with lambda phage and for the establishment of lysogeny. J. Gen. Microbiol. 21:676–84
     [Google Scholar]
  26. 26. 
    Gandon S. 2016. Why be temperate: lessons from bacteriophage λ. Trends Microbiol 24:356–65
     [Google Scholar]
  27. 27. 
    Gardner TS, Cantor CR, Collins JJ. 2000. Construction of a genetic toggle switch in Escherichia coli. Nature 403:339–42
     [Google Scholar]
  28. 28. 
    Gillespie DT. 1975. An exact method for numerically simulating the stochastic coalescence process in a cloud. J. Atmos. Sci. 32:1977–89
     [Google Scholar]
  29. 29. 
    Gillespie DT. 1976. A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22:403–34
     [Google Scholar]
  30. 30. 
    Gillespie DT. 1977. Exact stochastic simulation of coupled chemical reactions. J. Phys. Chem. 81:2340–61
     [Google Scholar]
  31. 31. 
    Golding I. 2016. Single-cell studies of phage λ: hidden treasures under Occam's rug. Annu. Rev. Virol. 3:453–72
     [Google Scholar]
  32. 32. 
    Gottesman ME, Weisberg RA. 2004. Little lambda, who made thee?. Microbiol. Mol. Biol. Rev. 68:796–813
     [Google Scholar]
  33. 33. 
    Herskowitz I, Hagen D. 1980. The lysis-lysogeny decision of phage λ: explicit programming and responsiveness. Annu. Rev. Genet. 14:399–445
     [Google Scholar]
  34. 34. 
    Jacob F. 2011. The birth of the operon. Science 332:767
     [Google Scholar]
  35. 35. 
    Jacob F, Monod J. 1961. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3:318–56
     [Google Scholar]
  36. 36. 
    Jacob F, Wollman EL. 1953. Induction of phage development in lysogenic bacteria. Cold Spring Harb. Symp. Quant. Biol. 18:101–21
     [Google Scholar]
  37. 37. 
    Joh RI, Weitz JS. 2011. To lyse or not to lyse: transient-mediated stochastic fate determination in cells infected by bacteriophages. PLOS Comput. Biol. 7:e1002006
     [Google Scholar]
  38. 38. 
    Johnson A, Meyer BJ, Ptashne M 1978. Mechanism of action of the cro protein of bacteriophage lambda. PNAS 75:1783–87
     [Google Scholar]
  39. 39. 
    Kaiser AD. 1955. A genetic study of the temperate coliphage λ. Virology 1:424–43
     [Google Scholar]
  40. 40. 
    Kaiser AD. 1957. Mutations in a temperate bacteriophage affecting its ability to lysogenize Escherichia coli. Virology 3:42–61
     [Google Scholar]
  41. 41. 
    Karlebach G, Shamir R. 2008. Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9:770–80
     [Google Scholar]
  42. 42. 
    Kobiler O, Rokney A, Friedman N, Court DL, Stavans J, Oppenheim AB 2005. Quantitative kinetic analysis of the bacteriophage λ genetic network. PNAS 102:4470–75
     [Google Scholar]
  43. 43. 
    Kobiler O, Rokney A, Oppenheim AB. 2007. Phage lambda CIII: a protease inhibitor regulating the lysis-lysogeny decision. PLOS ONE 2:e363
     [Google Scholar]
  44. 44. 
    Kourilsky P. 1973. Lysogenization by bacteriophage lambda. Mol. Gen. Genet. 122:183–95
     [Google Scholar]
  45. 45. 
    Kuchina A, Espinar L, Çağatay T, Balbin AO, Zhang F et al. 2011. Temporal competition between differentiation programs determines cell fate choice. Mol. Syst. Biol. 7:557
     [Google Scholar]
  46. 46. 
    Kuzdzal-Fick JJ, Chen L, Balázsi G. 2019. Disadvantages and benefits of evolved unicellularity versus multicellularity in budding yeast. Ecol. Evol. 9:8509–23
     [Google Scholar]
  47. 47. 
    Lederberg EM. 1950. Lysogenicity in Escherichia coli strain K-12. Microbial Genet. Bull. 1:5–8
     [Google Scholar]
  48. 48. 
    Lederberg EM, Lederberg J. 1953. Genetic studies of lysogenicity in Escherichia coli. Genetics 38:51–64
     [Google Scholar]
  49. 49. 
    Li C, Wang J. 2013. Quantifying cell fate decisions for differentiation and reprogramming of a human stem cell network: landscape and biological paths. PLOS Comput. Biol. 9:e1003165
     [Google Scholar]
  50. 50. 
    Lieb M. 1953. Studies on lysogenization in Escherichia coli. Cold Spring Harb. Symp. Quant. Biol. 18:71–73
     [Google Scholar]
  51. 51. 
    Little JW, Shepley DP, Wert DW. 1999. Robustness of a gene regulatory circuit. EMBO J 18:4299–307
     [Google Scholar]
  52. 52. 
    Lu TK, Collins JJ 2009. Engineered bacteriophage targeting gene networks as adjuvants for antibiotic therapy. PNAS 106:124629–34
     [Google Scholar]
  53. 53. 
    Lyon P. 2015. The cognitive cell: bacterial behavior reconsidered. Front. Microbiol. 6:264
     [Google Scholar]
  54. 54. 
    Maslov S, Sneppen K. 2015. Well-temperate phage: optimal bet-hedging against local environmental collapses. Sci. Rep. 5:10523
     [Google Scholar]
  55. 55. 
    Maurice CF. 2019. Considering the other half of the gut microbiome: bacteriophages. mSystems 4:
     [Google Scholar]
  56. 56. 
    McAdams HH, Arkin A 1997. Stochastic mechanisms in gene expression. PNAS 94:814–19
     [Google Scholar]
  57. 57. 
    McAdams HH, Shapiro L. 1995. Circuit simulation of genetic networks. Science 269:650–56
     [Google Scholar]
  58. 58. 
    Michod RE, Roze D. 2001. Cooperation and conflict in the evolution of multicellularity. Heredity 86:1–7
     [Google Scholar]
  59. 59. 
    Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. 2002. Network motifs: simple building blocks of complex networks. Science 298:824–27
     [Google Scholar]
  60. 60. 
    Möbius W, Laan L. 2015. Physical and mathematical modeling in experimental papers. Cell 163:1577–83
     [Google Scholar]
  61. 61. 
    Mojtahedi M, Skupin A, Zhou J, Castano IG, Leong-Quong RY et al. 2016. Cell fate decision as high-dimensional critical state transition. PLOS Biol 14:e2000640
     [Google Scholar]
  62. 62. 
    Moris N, Pina C, Arias AM. 2016. Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet. 17:693–703
     [Google Scholar]
  63. 63. 
    Nanda AM, Thormann K, Frunzke J. 2015. Impact of spontaneous prophage induction on the fitness bacterial populations and host-microbe interactions. J. Bacteriol. 197:410–19
     [Google Scholar]
  64. 64. 
    Nevozhay D, Adams RM, Murphy KF, Josić K, Balázsi G 2009. Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression. PNAS 106:5123–28
     [Google Scholar]
  65. 65. 
    Oppenheim AB, Kobiler O, Stavans J, Court DL, Adhya S. 2005. Switches in bacteriophage lambda development. Annu. Rev. Genet. 39:409–29
     [Google Scholar]
  66. 66. 
    Parker LE. 2012. Decision making as optimization in multi-robot teams. Distrib. Comput. Internet Technol. 7154:35–49
     [Google Scholar]
  67. 67. 
    Porwollik S, Wong RM-Y, McClelland M 2002. Evolutionary genomics of Salmonella: gene acquisitions revealed by microarray analysis. PNAS 99:8956–61
     [Google Scholar]
  68. 68. 
    Ptashne M. 1967. Specific binding of the λ phage repressor to λ DNA. Nature 214:232–34
     [Google Scholar]
  69. 69. 
    Ptashne M. 2004. A Genetic Switch: Phage Lambda Revisited Cold Spring Harbor, NY: Cold Spring Harb. Lab. Press
  70. 70. 
    Ray JCJ, Tabor JJ, Igoshin OA. 2011. Non-transcriptional regulatory processes shape transcriptional network dynamics. Nat. Rev. Microbiol. 9:817–28
     [Google Scholar]
  71. 71. 
    Reinitz J, Vaisnys JR. 1990. Theoretical and experimental analysis of the phage lambda genetic switch implies missing levels of cooperativity. J. Theor. Biol. 145:295–318
     [Google Scholar]
  72. 72. 
    Santillán M, Mackey MC. 2004. Why the lysogenic state of phage lambda is so stable: a mathematical modeling approach. Biophys. J. 86:75–84
     [Google Scholar]
  73. 73. 
    Shao Q, Cortes MG, Trinh JT, Guan J, Balázsi G, Zeng L 2018. Coupling of DNA replication and negative feedback controls gene expression for cell-fate decisions. iScience 6:1–12
     [Google Scholar]
  74. 74. 
    Shao Q, Trinh JT, McIntosh CS, Christenson B, Balázsi G, Zeng L 2017. Lysis-lysogeny coexistence: prophage integration during lytic development. MicrobiologyOpen 6:e00395
     [Google Scholar]
  75. 75. 
    Shao Q, Trinh JT, Zeng L. 2019. High-resolution studies of lysis–lysogeny decision-making in bacteriophage lambda. J. Biol. Chem. 294:3343–49
     [Google Scholar]
  76. 76. 
    Shea MA, Ackers GK. 1985. The OR control system of bacteriophage lambda. A physical-chemical model for gene regulation. J. Mol. Biol. 181:211–30
     [Google Scholar]
  77. 77. 
    Shklarsh A, Ariel G, Schneidman E, Ben-Jacob E. 2011. Smart swarms of bacteria-inspired agents with performance adaptable interactions. PLOS Comput. Biol. 7:e1002177
     [Google Scholar]
  78. 78. 
    Singh A, Dennehy JJ. 2014. Stochastic holin expression can account for lysis time variation in the bacteriophage λ. J. R. Soc. Interface 11:20140140
     [Google Scholar]
  79. 79. 
    Sinha V, Goyal A, Svenningsen SL, Semsey S, Krishna S. 2017. In silico evolution of lysis-lysogeny strategies reproduces observed lysogeny propensities in temperate bacteriophages. Front. Microbiol. 8:1386
     [Google Scholar]
  80. 80. 
    Stewart FM, Levin BR. 1984. The population biology of bacterial viruses: why be temperate.. Theor. Popul. Biol. 26:93–117
     [Google Scholar]
  81. 81. 
    St-Pierre F, Endy D 2008. Determination of cell fate selection during phage lambda infection. PNAS 105:20705–10
     [Google Scholar]
  82. 82. 
    Svenningsen SL, Semsey S. 2014. Commitment to lysogeny is preceded by a prolonged period of sensitivity to the late lytic regulator Q in bacteriophage λ. J. Bacteriol. 196:3582–88
     [Google Scholar]
  83. 83. 
    Symmons O, Raj A 2016. What's luck got to do with it: single cells, multiple fates, and biological nondeterminism. Mol. Cell 62:788–802
     [Google Scholar]
  84. 84. 
    Thomas R, Gathoye AM, Lambert L. 1976. A complex control circuit: regulation of immunity in temperate bacteriophages. Eur. J. Biochem. 71:211–27
     [Google Scholar]
  85. 85. 
    Tian T, Burrage K. 2004. Bistability and switching in the lysis/lysogeny genetic regulatory network of bacteriophage λ. J. Theor. Biol. 227:229–37
     [Google Scholar]
  86. 86. 
    Trimmer PC, Houston AI, Marshall JAR, Bogacz R, Paul ES et al. 2008. Mammalian choices: combining fast-but-inaccurate and slow-but-accurate decision-making systems. Proc. Biol. Sci. 275:2353–61
     [Google Scholar]
  87. 87. 
    Trinh JT, Shao Q, Guan J, Zeng L. 2020. Emerging heterogeneous compartments by viruses in single bacterial cells. Nat. Commun. 11:3813
     [Google Scholar]
  88. 88. 
    Trinh JT, Székely T, Shao Q, Balázsi G, Zeng L. 2017. Cell fate decisions emerge as phages cooperate or compete inside their host. Nat. Commun. 8:14341
     [Google Scholar]
  89. 89. 
    Wahl LM, Betti MI, Dick DW, Pattenden T, Puccini AJ. 2019. Evolutionary stability of the lysis-lsyogeny decision: why be virulent?. Evolution 73:92–98
     [Google Scholar]
  90. 90. 
    Weigle JJ, Delbruck M. 1951. Mutual exclusion between an infecting phage and a carried phage. J. Bacteriol. 62:301–18
     [Google Scholar]
  91. 91. 
    Weitz JS, Mileyko Y, Joh RI, Voit EO. 2008. Collective decision making in bacterial viruses. Biophys. J. 95:2673–80
     [Google Scholar]
  92. 92. 
    Westerhoff HV, Brooks AN, Simeonidis E, García-Contreras R, He F et al. 2014. Macromolecular networks and intelligence in microorganisms. Front. Microbiol. 5:379
     [Google Scholar]
  93. 93. 
    Wollman EL. 1953. Genetic determinism of lysogeny. Ann. Inst. Pasteur 84:281–93
     [Google Scholar]
  94. 94. 
    Wu M, Su R-Q, Li X, Ellis T, Lai Y-C, Wang X 2013. Engineering of regulated stochastic cell fate determination. PNAS 110:10610–15
     [Google Scholar]
  95. 95. 
    Yarchuk O, Jacques N, Guillerez J, Dreyfus M. 1992. Interdependence of translation, transcription and mRNA degradation in the lacZ gene. J. Mol. Biol. 226:581–96
     [Google Scholar]
  96. 96. 
    Zeng L, Skinner SO, Zong C, Sippy J, Feiss M, Golding I. 2010. Decision making at a subcellular level determines the outcome of bacteriophage infection. Cell 141:682–91
     [Google Scholar]
  97. 97. 
    Zong C, So L, Sepúlveda LA, Skinner SO, Golding I. 2010. Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene. Mol. Syst. Biol. 6:440
     [Google Scholar]
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From Bench to Keyboard and Back Again: A Brief History of Lambda Phage Modeling
Annual Review of Biophysics 50, 117 (2021); https://doi.org/10.1146/annurev-biophys-082020-063558
/content/journals/10.1146/annurev-biophys-082020-063558
/content/journals/10.1146/annurev-biophys-082020-063558
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