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Stochasticity in gene expression: from theories to phenotypes

Key Points

  • Stochasticity in gene expression is manifested as fluctuations in the abundance of expressed molecules at the single-cell level, and variability and heterogeneity within populations of genetically identical cells.

  • Analyses of simple models indicate that stochasticity in gene expression is dominated by translational bursting, arising from a low number of expressed mRNAs, and transcriptional bursting, arising from slow transitions between promoter states.

  • Transcriptional bursting, which arises from random transitions between chromatin states, might cause stochastic all-or-nothing responses in eukaryotic cells and lead to the emergence of populations that contain a mixture of expressing and non-expressing cells.

  • Experimental evidence indicates that translational bursting is a dominant source of stochasticity in prokaryote gene expression, and that both translational and transcriptional bursting contribute to stochasticity in eukaryote gene expression.

  • Evidence also indicates that translational bursting in eukaryotes is an evolvable trait that is subject to natural selection. Transcriptional bursting has been implicated in syndromes that are associated with haploinsufficiency.

  • Sources that are extrinsic to the process of gene expression, such as fluctuations in regulatory signals, also contribute significantly to stochasticity in gene expression. Gene-intrinsic and gene-extrinsic noise can be distinguished experimentally using a two-reporter assay.

  • Fluctuations in regulatory signals are important for the function of transcriptional regulatory networks. In genetic cascades, such fluctuations lead to increased population variability at intermediate expression levels and an initial population asynchrony that increases with cascade length. Increased variability in a regulatory signal might also cause the emergence of mixed populations, containing cells that show either high or low expression levels of the target gene.

  • Negative and positive feedback typically leads to reduction and amplification, respectively, of fluctuations and population heterogeneity. Positive feedback can yield unique or multiple cellular-expression states, depending on the strength of the feedback.

  • Stochasticity in gene expression might provide microorganisms with the flexibility required to respond and adapt to environmental changes and stresses, and can prevent cells from being trapped in suboptimal epigenetic states and phenotypes.

  • Stochastic mechanisms have also been implicated in cellular differentiation and development. They provide a means of generating the initial population heterogeneity on which regulatory mechanisms can function to establish and propagate the expression of cell-type-specific genes.

Abstract

Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.

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Figure 1: A model of the expression of a single gene.
Figure 2: Finite-number effects and translational bursting.
Figure 3: Slow promoter transitions and transcriptional bursting.
Figure 4: Measuring gene-intrinsic noise.
Figure 5: Noise in gene networks.

References

  1. Novick, A. & Weiner, M. Enzyme induction as an all-or-none phenomenon. Proc. Natl Acad. Sci. USA 43, 553–566 (1957).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Powell, E. O. An outline of the pattern of bacterial generation times. J. Gen. Microbiol. 18, 382–417 (1958).

    Article  CAS  PubMed  Google Scholar 

  3. Singh, U. N. Polyribosomes and unstable messenger RNA: a stochastic model of protein synthesis. J. Theor. Biol. 25, 444–460 (1969).

    Article  CAS  PubMed  Google Scholar 

  4. Maloney, P. C. & Rotman, B. Distribution of suboptimally induces-D-galactosidase in Escherichia coli. The enzyme content of individual cells. J. Mol. Biol. 73, 77–91 (1973).

    Article  CAS  PubMed  Google Scholar 

  5. Spudich, J. L. & Koshland, D. E. Jr. Non-genetic individuality: chance in the single cell. Nature 262, 467–471 (1976).

    Article  CAS  PubMed  Google Scholar 

  6. Rigney, D. R. & Schieve, W. C. Stochastic model of linear, continuous protein synthesis in bacterial populations. J. Theor. Biol. 69, 761–766 (1977).

    Article  CAS  PubMed  Google Scholar 

  7. Berg, O. G. A model for the statistical fluctuations of protein numbers in a microbial population. J. Theor. Biol. 71, 587–603 (1978).

    Article  CAS  PubMed  Google Scholar 

  8. Orphanides, G. & Reinberg, D. A unified theory of gene expression. Cell 108, 439–451 (2002).

    Article  CAS  PubMed  Google Scholar 

  9. Ko, M. S. A stochastic model for gene induction. J. Theor. Biol. 153, 181–194 (1991).

    Article  CAS  PubMed  Google Scholar 

  10. Ko, M. S. Induction mechanism of a single gene molecule: stochastic or deterministic? Bioessays 14, 341–346 (1992).

    Article  CAS  PubMed  Google Scholar 

  11. Peccoud, J. & Ycart, B. Markovian modeling of gene-product synthesis. Theor. Popul. Biol. 48, 222–234 (1995).

    Article  Google Scholar 

  12. McAdams, H. H. & Arkin, A. Stochastic mechanisms in gene expression. Proc. Natl Acad. Sci. USA 94, 814–819 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Carrier, T. A. & Keasling, J. D. Mechanistic modeling of prokaryotic mRNA decay. J. Theor. Biol. 189, 195–209 (1997).

    Article  CAS  PubMed  Google Scholar 

  14. Arkin, A., Ross, J. & McAdams, H. H. Stochastic kinetic analysis of developmental pathway bifurcation in phage-λ-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Carrier, T. A. & Keasling, J. D. Investigating autocatalytic gene expression systems through mechanistic modeling. J. Theor. Biol. 201, 25–36 (1999).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Paulsson, J., Berg, O. G. & Ehrenberg, M. Stochastic focusing: fluctuation-enhanced sensitivity of intracellular regulation. Proc. Natl Acad. Sci. USA 97, 7148–7153 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Barkai, N. & Leibler, S. Circadian clocks limited by noise. Nature 403, 267–268 (2000).

    Article  CAS  PubMed  Google Scholar 

  19. Kepler, T. B. & Elston, T. C. Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations. Biophys. J. 81, 3116–3136 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Kierzek, A. M., Zaim, J. & Zielenkiewicz, P. The effect of transcription and translation initiation frequencies on the stochastic fluctuations in prokaryotic gene expression. J. Biol. Chem. 276, 8165–8172 (2001).

    Article  CAS  PubMed  Google Scholar 

  21. Thattai, M. & van Oudenaarden, A. Intrinsic noise in gene regulatory networks. Proc. Natl Acad. Sci. USA 98, 8614–8619 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Paulsson, J. & Ehrenberg, M. Noise in a minimal regulatory network: plasmid copy number control. Q. Rev. Biophys. 34, 1–59 (2001).

    Article  CAS  PubMed  Google Scholar 

  23. Ozbudak, E. M., Thattai, M., Kurtser, I., Grossman, A. D. & van Oudenaarden, A. Regulation of noise in the expression of a single gene. Nature Genet. 31, 69–73 (2002). An experimental computational study of noise in prokaryotic gene expression, focusing on contributions from the processes of transcription and translation.

    Article  CAS  PubMed  Google Scholar 

  24. Gonze, D., Halloy, J. & Goldbeter, A. Robustness of circadian rhythms with respect to molecular noise. Proc. Natl Acad. Sci. USA 99, 673–678 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wolf, D. M. & Arkin, A. P. Fifteen minutes of fim: control of type 1 pili expression in E. coli. OMICS 6, 91–114 (2002).

    Article  CAS  PubMed  Google Scholar 

  26. Thattai, M. & van Oudenaarden, A. Attenuation of noise in ultrasensitive signaling cascades. Biophys. J. 82, 2943–2950 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Vilar, J. M., Kueh, H. Y., Barkai, N. & Leibler, S. Mechanisms of noise-resistance in genetic oscillators. Proc. Natl Acad. Sci. USA 99, 5988–5992 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Swain, P. S., Elowitz, M. B. & Siggia, E. D. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc. Natl Acad. Sci. USA 99, 12795–12800 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Blake, W. J., Kærn, M., Cantor, C. R. & Collins, J. J. Noise in eukaryotic gene expression. Nature 422, 633–637 (2003). A study of noise in eukaryotic gene expression, highlighting how the processes of transcription and translation contribute to this.

    Article  CAS  PubMed  Google Scholar 

  30. Sato, K., Ito, Y., Yomo, T. & Kaneko, K. On the relation between fluctuation and response in biological systems. Proc. Natl Acad. Sci. USA 100, 14086–14090 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sasai, M. & Wolynes, P. G. Stochastic gene expression as a many-body problem. Proc. Natl Acad. Sci. USA 100, 2374–2379 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Simpson, M. L., Cox, C. D. & Sayler, G. S. Frequency domain analysis of noise in autoregulated gene circuits. Proc. Natl Acad. Sci. USA 100, 4551–4556 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Shibata, T. Fluctuating reaction rates and their application to problems of gene expression. Phys. Rev. E 67, 061906 (2003).

    Article  CAS  Google Scholar 

  34. Paulsson, J. Summing up the noise in gene networks. Nature 427, 415–418 (2004).

    Article  CAS  PubMed  Google Scholar 

  35. Pirone, J. R. & Elston, T. C. Fluctuations in transcription factor binding can explain the graded and binary responses observed in inducible gene expression. J. Theor. Biol. 226, 111–121 (2004).

    Article  CAS  PubMed  Google Scholar 

  36. Raser, J. M. & O'Shea, E. K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004). A study of the contributions of intrinsic and extrinsic noise in eukaryotic gene expression.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Simpson, M. L., Cox, C. D. & Sayler, G. S. Frequency domain chemical Langevin analysis of stochasticity in gene transcriptional regulation. J. Theor. Biol. 229, 383–394 (2004).

    Article  CAS  PubMed  Google Scholar 

  38. Tao, Y. Intrinsic and external noise in an auto-regulatory genetic network. J. Theor. Biol. 229, 147–156 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Tomioka, R., Kimura, H., T, J. K. & Aihara, K. Multivariate analysis of noise in genetic regulatory networks. J. Theor. Biol. 229, 501–521 (2004).

    Article  CAS  PubMed  Google Scholar 

  40. Orrell, D. & Bolouri, H. Control of internal and external noise in genetic regulatory networks. J. Theor. Biol. 230, 301–312 (2004).

    Article  CAS  PubMed  Google Scholar 

  41. Karmakar, R. & I., B. Graded and binary responses in stochastic gene expression. Phys. Biol. 1, 197–204 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Morishita, Y. & Aihara, K. Noise-reduction through interaction in gene expression and biochemical reaction processes. J. Theor. Biol. 228, 315–325 (2004).

    Article  CAS  PubMed  Google Scholar 

  43. Swain, P. S. Efficient attenuation of stochasticity in gene expression through post-transcriptional control. J. Mol. Biol. 344, 965–976 (2004).

    Article  CAS  PubMed  Google Scholar 

  44. Shibata, T. & Fujimoto, K. Noisy signal amplification in ultrasensitive signal transduction. Proc. Natl Acad. Sci. USA 102, 331–336 (2005).

    Article  CAS  PubMed  Google Scholar 

  45. Allen, R. J., Warren, P. B. & Ten Wolde, P. R. Sampling rare switching events in biochemical networks. Phys. Rev. Lett. 94, 018104 (2005).

    Article  CAS  PubMed  Google Scholar 

  46. Wang, Z. W., Hou, Z. H. & Xin, H. W. Internal noise stochastic resonance of synthetic gene network. Chem. Phys. Lett. 401, 307–311 (2005).

    Article  CAS  Google Scholar 

  47. Roma, D. M., O'Flanagan, R. A., Ruckenstein, A. E., Sengupta, A. M. & Mukhopadhyay, R. Optimal path to epigenetic switching. Phys. Rev. E 71, 011902 (2005).

    Article  CAS  Google Scholar 

  48. Forger, D. B. & Peskin, C. S. Stochastic simulation of the mammalian circadian clock. Proc. Natl Acad. Sci. USA 102, 321–324 (2005).

    Article  CAS  PubMed  Google Scholar 

  49. England, J. L. & Cardy, J. Morphogen gradient from a noisy source. Phys. Rev. Lett. 94, 078101 (2005).

    Article  CAS  PubMed  Google Scholar 

  50. van Kampen, N. G. Stochastic Processes in Physics and Chemistry (North-Holland Personal Library, Amsterdam, 1992).

    Google Scholar 

  51. Paldi, A. Stochastic gene expression during cell differentiation: order from disorder? Cell. Mol. Life. Sci. 60, 1775–1778 (2003).

    Article  CAS  PubMed  Google Scholar 

  52. Ko, M. S., Nakauchi, H. & Takahashi, N. The dose dependence of glucocorticoid-inducible gene expression results from changes in the number of transcriptionally active templates. EMBO J. 9, 2835–2842 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ross, I. L., Browne, C. M. & Hume, D. A. Transcription of individual genes in eukaryotic cells occurs randomly and infrequently. Immunol. Cell Biol. 72, 177–185 (1994).

    Article  CAS  PubMed  Google Scholar 

  54. Simpson, P. Notch signalling in development: on equivalence groups and asymmetric developmental potential. Curr. Opin. Genet. Dev. 7, 537–542 (1997).

    Article  CAS  PubMed  Google Scholar 

  55. Graubert, T. A. et al. Stochastic, stage-specific mechanisms account for the variegation of a human globin transgene. Nucleic Acids Res. 26, 2849–2858 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Nutt, S. L. et al. Independent regulation of the two Pax5 alleles during B-cell development. Nature Genet. 21, 390–395 (1999).

    Article  CAS  PubMed  Google Scholar 

  57. Hume, D. A. Probability in transcriptional regulation and its implications for leukocyte differentiation and inducible gene expression. Blood 96, 2323–2328 (2000).

    CAS  PubMed  Google Scholar 

  58. Biggar, S. R. & Crabtree, G. R. Cell signaling can direct either binary or graded transcriptional responses. EMBO J. 20, 3167–3176 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Joers, A., Jaks, V., Kase, J. & Maimets, T. p53-dependent transcription can exhibit both on/off and graded response after genotoxic stress. Oncogene 23, 6175–6185 (2004).

    Article  CAS  PubMed  Google Scholar 

  60. Fiering, S., Whitelaw, E. & Martin, D. I. To be or not to be active: the stochastic nature of enhancer action. Bioessays 22, 381–387 (2000).

    Article  CAS  PubMed  Google Scholar 

  61. Rosenfeld, N. Y., Young, J. W., Alon, U., Swain, P. S. & Elowitz, M. B. Gene regulation at the single cell level. Science, 307, 1962–1965 (2005).

    Article  CAS  PubMed  Google Scholar 

  62. Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002). A study of the impact of intrinsic and extrinsic noise on prokaryotic gene expression.

    Article  CAS  PubMed  Google Scholar 

  63. Hoosangi, S., Thiberge, S. & Weiss, R. Ultrasensitivity and noise propagation in a synthetic transcriptional cascade. Proc. Natl Acad. Sci. USA 102, 3581–3586 (2005). This study investigates how the length of transcriptional regulatory cascades affects the propagation of noise in gene expression.

    Article  CAS  Google Scholar 

  64. Pedraza, J. M. & van Oudenaarden, A. Noise propagation in genetic networks. Science 307, 1965–1969 (2005). Another important study of the propagation of gene-expression noise in a transcriptional regulatory cascade.

    Article  CAS  PubMed  Google Scholar 

  65. Becskei, A. & Serrano, L. Engineering stability in gene networks by autoregulation. Nature 405, 590–593 (2000). This paper demonstrates that negative feedback reduces population heterogeneity.

    Article  CAS  PubMed  Google Scholar 

  66. Becskei, A., Seraphin, B. & Serrano, L. Positive feedback in eukaryotic gene networks: cell differentiation by graded to binary response conversion. EMBO J. 20, 2528–2535 (2001). A study of the effect of positive feedback on population variability.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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). This study investigates the effects of varying feedback strength in a positive-feedback gene network.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Louis, M. & Becskei, A. Binary and graded responses in gene networks. Sci. STKE 2002, PE33 (2002).

    PubMed  Google Scholar 

  69. Savageau, M. A. Comparison of classical and autogenous systems of regulation in inducible operons. Nature 252, 546–549 (1974).

    Article  CAS  PubMed  Google Scholar 

  70. Rao, C. V., Wolf, D. M. & Arkin, A. P. Control, exploitation and tolerance of intracellular noise. Nature 420, 231–237 (2002). An excellent review of stochastic simulation methods, noise-control mechanisms and experimental results.

    Article  CAS  PubMed  Google Scholar 

  71. Monk, N. A. Oscillatory expression of Hes1, p53, and NF-κB driven by transcriptional time delays. Curr. Biol. 13, 1409–1413 (2003).

    Article  CAS  PubMed  Google Scholar 

  72. Lev Bar-Or, R. et al. Generation of oscillations by the p53–Mdm2 feedback loop: a theoretical and experimental study. Proc. Natl Acad. Sci. USA 97, 11250–11255 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Lahav, G. et al. Dynamics of the p53–Mdm2 feedback loop in individual cells. Nature Genet. 36, 147–150 (2004).

    Article  CAS  PubMed  Google Scholar 

  74. Dale, J. K. et al. Periodic Notch inhibition by Lunatic Fringe underlies the chick segmentation clock. Nature 421, 275–278 (2003).

    Article  CAS  PubMed  Google Scholar 

  75. Pourquie, O. The segmentation clock: converting embryonic time into spatial pattern. Science 301, 328–330 (2003).

    Article  CAS  PubMed  Google Scholar 

  76. Lewis, J. Autoinhibition with transcriptional delay: a simple mechanism for the zebrafish somitogenesis oscillator. Curr. Biol. 13, 1398–1408 (2003).

    Article  CAS  PubMed  Google Scholar 

  77. Rida, P. C., Le Minh, N. & Jiang, Y. J. A Notch feeling of somite segmentation and beyond. Dev. Biol. 265, 2–22 (2004).

    Article  CAS  PubMed  Google Scholar 

  78. Delbruck, M. Statistical fluctuations in autocatalytic reactions. J. Chem. Phys. 8, 120–124 (1945).

    Article  Google Scholar 

  79. Epstein, I. R. The consequences of imperfect mixing in autocatalytic chemical and biological systems. Nature 374, 321–327 (1995).

    Article  CAS  PubMed  Google Scholar 

  80. Ozbudak, E. M., Thattai, M., Lim, H. N., Shraiman, B. I. & Van Oudenaarden, A. Multistability in the lactose utilization network of Escherichia coli. Nature 427, 737–740 (2004).

    Article  CAS  PubMed  Google Scholar 

  81. Acar, M. B., & van Oudenaarden, A. Enhancement of cellular memory by reducing stochastic transitions. Nature (in the press). A study of stochastic effects in an endogenous genetic network.

  82. Fraser, H. B., Hirsh, A. E., Giaever, G., Kumm, J. & Eisen, M. B. Noise minimization in eukaryotic gene expression. PLoS Biol. 2, e137 (2004). A bioinformatics study that provides support for the hypothesis that gene-expression noise is subject to natural selection.

    Article  PubMed  PubMed Central  Google Scholar 

  83. McAdams, H. H. & Arkin, A. It's a noisy business! Genetic regulation at the nanomolar scale. Trends Genet. 15, 65–69 (1999).

    Article  CAS  PubMed  Google Scholar 

  84. Lewis, K. Programmed death in bacteria. Microbiol. Mol. Biol. Rev. 64, 503–514 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Booth, I. R. Stress and the single cell: intrapopulation diversity is a mechanism to ensure survival upon exposure to stress. Int. J. Food Microbiol. 78, 19–30 (2002).

    Article  PubMed  Google Scholar 

  86. Thattai, M. & van Oudenaarden, A. Stochastic gene expression in fluctuating environments. Genetics 167, 523–530 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Aertsen, A. & Michiels, C. W. Stress and how bacteria cope with death and survival. Crit. Rev. Microbiol. 30, 263–273 (2004).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  89. Maughan, H. & Nicholson, W. L. Stochastic processes influence stationary-phase decisions in Bacillus subtilis. J. Bacteriol. 186, 2212–2214 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Levin, B. R. Microbiology. Noninherited resistance to antibiotics. Science 305, 1578–1579 (2004).

    Article  CAS  PubMed  Google Scholar 

  91. van Roon, M. A., Aten, J. A., van Oven, C. H., Charles, R. & Lamers, W. H. The initiation of hepatocyte-specific gene expression within embryonic hepatocytes is a stochastic event. Dev. Biol. 136, 508–516 (1989).

    Article  CAS  PubMed  Google Scholar 

  92. Sternberg, P. W. & Felix, M. A. Evolution of cell lineage. Curr. Opin. Genet. Dev. 7, 543–550 (1997).

    Article  CAS  PubMed  Google Scholar 

  93. Enver, T., Heyworth, C. M. & Dexter, T. M. Do stem cells play dice? Blood 92, 348–351; discussion 352 (1998).

    CAS  PubMed  Google Scholar 

  94. Abkowitz, J. L., Catlin, S. N. & Guttorp, P. Evidence that hematopoiesis may be a stochastic process in vivo. Nature Med. 2, 190–197 (1996).

    Article  CAS  PubMed  Google Scholar 

  95. Kupiec, J. J. A Darwinian theory for the origin of cellular differentiation. Mol. Gen. Genet. 255, 201–208 (1997).

    Article  CAS  PubMed  Google Scholar 

  96. Deenick, E. K., Hasbold, J. & Hodgkin, P. D. Switching to IgG3, IgG2b, and IgA is division linked and independent, revealing a stochastic framework for describing differentiation. J. Immunol. 163, 4707–4714 (1999).

    CAS  PubMed  Google Scholar 

  97. Blewitt, M. E., Chong, S. & Whitelaw, E. How the mouse got its spots. Trends Genet. 20, 550–554 (2004).

    Article  CAS  PubMed  Google Scholar 

  98. Hoang, T. The origin of hematopoietic cell type diversity. Oncogene 23, 7188–7198 (2004).

    Article  CAS  PubMed  Google Scholar 

  99. Wardle, F. C. & Smith, J. C. Refinement of gene expression patterns in the early Xenopus embryo. Development 131, 4687–4696 (2004).

    Article  CAS  PubMed  Google Scholar 

  100. Kurakin, A. Self-organization vs Watchmaker: stochastic gene expression and cell differentiation. Dev. Genes Evol. 215, 46–52 (2005).

    Article  PubMed  Google Scholar 

  101. LaForge, B., Guez, D., Martinez, M. & Kupiec, J. J. Modeling embryogenesis and cancer: an approach based on an equilibrium between autostabilization of stochastic gene expression and the interdependence of cells for proliferation. Prog. Biophys. Mol. Biol. 89, 93–120 (2005).

    Article  CAS  PubMed  Google Scholar 

  102. Russo, E., Martienssen, R. & Riggs, A. D. Epigenetic Mechanisms of Gene Regulation (Cold Spring Harbor Lab. Press, Plainview, New York, 1996).

    Google Scholar 

  103. Rakyan, V. K., Preis, J., Morgan, H. D. & Whitelaw, E. The marks, mechanisms and memory of epigenetic states in mammals. Biochem. J. 356, 1–10 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Pfeifer, G. P., Steigerwald, S. D., Hansen, R. S., Gartler, S. M. & Riggs, A. D. Polymerase chain reaction-aided genomic sequencing of an X chromosome-linked CpG island: methylation patterns suggest clonal inheritance, CpG site autonomy, and an explanation of activity state stability. Proc. Natl Acad. Sci. USA 87, 8252–8256 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Lorincz, M. C., Schubeler, D., Hutchinson, S. R., Dickerson, D. R. & Groudine, M. DNA methylation density influences the stability of an epigenetic imprint and Dnmt3a/b-independent de novo methylation. Mol. Cell. Biol. 22, 7572–7580 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Sato, N., Nakayama, M. & Arai, K. Fluctuation of chromatin unfolding associated with variation in the level of gene expression. Genes Cells 9, 619–630 (2004).

    Article  CAS  PubMed  Google Scholar 

  107. Fourel, G., Magdinier, F. & Gilson, E. Insulator dynamics and the setting of chromatin domains. Bioessays 26, 523–532 (2004).

    Article  CAS  PubMed  Google Scholar 

  108. Chelly, J., Concordet, J. P., Kaplan, J. C. & Kahn, A. Illegitimate transcription: transcription of any gene in any cell type. Proc. Natl Acad. Sci. USA 86, 2617–2621 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Bird, A. P. Gene number, noise reduction and biological complexity. Trends Genet. 11, 94–100 (1995).

    Article  CAS  PubMed  Google Scholar 

  110. Jablanka, E. & Regev, A. Gene number, methylation and biological complexity. Trends Genet. 11, 383–384 (1995).

    Article  CAS  PubMed  Google Scholar 

  111. Ahmad, K. & Henikoff, S. Modulation of a transcription factor counteracts heterochromatic gene silencing in Drosophila. Cell 104, 839–847 (2001).

    Article  CAS  PubMed  Google Scholar 

  112. Cheutin, T. et al. Maintenance of S heterochromatin domains by dynamic HP1 binding. Science 299, 721–725 (2003).

    Article  CAS  PubMed  Google Scholar 

  113. Festenstein, R. et al. Modulation of heterochromatin protein 1 dynamics in primary mammalian cells. Science 299, 719–721 (2003).

    Article  CAS  PubMed  Google Scholar 

  114. Gottschling, D. E., Aparicio, O. M., Billington, B. L. & Zakian, V. A. Position effect at S. cerevisiae telomeres: reversible repression of Pol II transcription. Cell 63, 751–762 (1990).

    Article  CAS  PubMed  Google Scholar 

  115. Lundgren, M. et al. Transcription factor dosage affects changes in higher order chromatin structure associated with activation of a heterochromatic gene. Cell 103, 733–743 (2000).

    Article  CAS  PubMed  Google Scholar 

  116. Seidman, J. G. & Seidman, C. Transcription factor haploinsufficiency: when half a loaf is not enough. J. Clin. Invest. 109, 451–455 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Cook, D. L., Gerber, A. N. & Tapscott, S. J. Modeling stochastic gene expression: implications for haploinsufficiency. Proc. Natl Acad. Sci. USA 95, 15641–15646 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Ferrer, J. A genetic switch in pancreatic β-cells: implications for differentiation and haploinsufficiency. Diabetes 51, 2355–2362 (2002).

    Article  CAS  PubMed  Google Scholar 

  119. Kemkemer, R., Schrank, S., Vogel, W., Gruler, H. & Kaufmann, D. Increased noise as an effect of haploinsufficiency of the tumor-suppressor gene neurofibromatosis type 1 in vitro. Proc. Natl Acad. Sci. USA 99, 13783–13788 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Magee, J. A., Abdulkadir, S. A. & Milbrandt, J. Haploinsufficiency at the Nkx3.1 locus. A paradigm for stochastic, dosage-sensitive gene regulation during tumor initiation. Cancer Cell 3, 273–283 (2003).

    Article  CAS  PubMed  Google Scholar 

  121. Kuang, Y., Biran, I. & Walt, D. R. Simultaneously monitoring gene expression kinetics and genetic noise in single cells by optical well arrays. Anal. Chem. 76, 6282–6286 (2004).

    Article  CAS  PubMed  Google Scholar 

  122. Metzler, R. The future is noisy: the role of spatial fluctuations in genetic switching. Phys. Rev. Lett. 8706, 068103 (2001).

    Article  CAS  Google Scholar 

  123. Droge, P. & Muller-Hill, B. High local protein concentrations at promoters: strategies in prokaryotic and eukaryotic cells. Bioessays 23, 179–183 (2001).

    Article  CAS  PubMed  Google Scholar 

  124. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    Article  CAS  PubMed  Google Scholar 

  125. Tchuraev, R. N., Stupak, I. V., Tropynina, T. S. & Stupak, E. E. Epigenes: design and construction of new hereditary units. FEBS Lett. 486, 200–202 (2000).

    Article  CAS  PubMed  Google Scholar 

  126. Kramer, B. P. et al. An engineered epigenetic transgene switch in mammalian cells. Nature Biotechnol. 22, 867–870 (2004).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Elowitz, M. B. & Leibler, S. A synthetic oscillatory network of transcriptional regulators. Nature 403, 335–338 (2000).

    Article  CAS  PubMed  Google Scholar 

  129. Atkinson, M. R., Savageau, M. A., Myers, J. T. & Ninfa, A. J. Development of genetic circuitry exhibiting toggle switch or oscillatory behavior in Escherichia coli. Cell 113, 597–607 (2003).

    Article  CAS  PubMed  Google Scholar 

  130. Steuer, R., Zhou, C. & Kurths, J. Constructive effects of fluctuations in genetic and biochemical regulatory systems. Biosystems 72, 241–251 (2003).

    Article  CAS  PubMed  Google Scholar 

  131. You, L., Cox, R. S. 3rd, Weiss, R. & Arnold, F. H. Programmed population control by cell–cell communication and regulated killing. Nature 428, 868–871 (2004).

    Article  CAS  PubMed  Google Scholar 

  132. Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387, 913–917 (1997).

    Article  CAS  PubMed  Google Scholar 

  133. Alon, U., Surette, M. G., Barkai, N. & Leibler, S. Robustness in bacterial chemotaxis. Nature 397, 168–171 (1999).

    Article  CAS  PubMed  Google Scholar 

  134. Levin, M. D. Noise in gene expression as the source of non-genetic individuality in the chemotactic response of Escherichia coli. FEBS Lett. 550, 135–138 (2003).

    Article  CAS  PubMed  Google Scholar 

  135. Korobkova, E., Emonet, T., Vilar, J. M., Shimizu, T. S. & Cluzel, P. From molecular noise to behavioural variability in a single bacterium. Nature 428, 574–578 (2004).

    Article  CAS  PubMed  Google Scholar 

  136. Stelling, J., Sauer, U., Szallasi, Z., Doyle, F. J. 3rd & Doyle, J. Robustness of cellular functions. Cell 118, 675–685 (2004).

    Article  CAS  PubMed  Google Scholar 

  137. Kerszberg, M. Noise, delays, robustness, canalization and all that. Curr. Opin. Genet. Dev. 14, 440–445 (2004).

    Article  CAS  PubMed  Google Scholar 

  138. Goulian, M. Robust control in bacterial regulatory circuits. Curr. Opin. Microbiol. 7, 198–202 (2004).

    Article  CAS  PubMed  Google Scholar 

  139. Barabasi, A. L. & Oltvai, Z. N. Network biology: understanding the cell's functional organization. Nature Rev. Genet. 5, 101–113 (2004).

    Article  CAS  PubMed  Google Scholar 

  140. Kitano, H. Biological robustness. Nature Rev. Genet. 5, 826–837 (2004).

    Article  CAS  PubMed  Google Scholar 

  141. Bar-Yam, Y. & Epstein, I. R. Response of complex networks to stimuli. Proc. Natl Acad. Sci. USA 101, 4341–4345 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to thank M. Elowitz, J. Hasty, F. Isaacs, E. O'Shea, A. van Oudenaarden, J. Paulsson, J. Raser, M. Simpson, P. Swain and R. Weiss for useful discussions and insights. We apologize to the authors whose contributions could not be discussed owing to space limitations. This work was supported by the Canada Research Chair programme (M.K.), Defence Advanced Research Projects Agency (T.C.E.) and the National Institutes of Health (W.J.B. and J.J.C.).

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DATABASES

Entrez Gene

GAL1

NF1

PHO5

FURTHER INFORMATION

Jim Collins' Applied Biodynamics Laboratory

Mads Kærn's Dynamical Systems Biology Laboratory

Tim Elston's homepage

Glossary

ISOGENIC

Genetically identical. Individual cells within an isogenic population are typically the progeny of a single ancestor.

NUCLEOSOME

The fundamental unit into which DNA and histones are packaged in eukaryotic cells. It is the basic structural subunit of chromatin and consists of 200 bp of DNA and an octamer of histone proteins.

SYNONYMOUS CODONS

Codons that have different nucleotide triplets, but which encode the signal for incorporation of the same amino-acid residue during translation. Differences in synonymous codon usage can result in differences in translation rates because codon-specific tRNAs have different abundances.

TATA BOX

A consensus sequence within promoters that is enriched in thymine and adenine residues, and is generally important for the recruitment of the transcriptional machinery.

UPSTREAM ACTIVATING SEQUENCE

A sequence that is located upstream of a promoter at which transcriptional activators bind and subsequently facilitate the expression of downstream genes.

P53–MDM2 FEEDBACK LOOP

One of the best-studied negative-feedback regulatory networks in human cells. The tumour-suppressor p53 activates the synthesis of Mdm2, which in turn targets p53 for degradation.

SEGMENTAL CLOCK

The gene-regulatory network that allows the periodic and population-synchronous expression of genes in the primitive streak and posterior presomitic mesoderm of developing vertebrates. This allows the formation of a periodic pattern of gene expression in the anterior presomitic mesoderm.

LYSOGENIC/LYTIC DECISION PATHWAY

The gene-regulatory network that allows bacteriophage-λ to switch between a dormant (lysogenic) state, in which phage DNA is integrated into the chromosome of the host cell, and an active (lytic) state, in which the cellular machinery of the host is used to rapidly produce phage progeny.

EPIGENETIC MEMORY

The ability to transfer information through successive generations without modification of the DNA sequence. Common mechanisms of epigenetic inheritance are covalent modifications of DNA and altered chromatin structure that affects gene expression.

HAPLOINSUFFICIENCY

The inactivation of one of two alleles in diploid cells to produce a heterozygote that is insufficient to assure normal function.

DENDRITES

Short, tree-like extensions that are features of many neurons and allow the transmittance of nerve impulses between cells.

OPTICAL WELL ARRAY

An emerging technology for temporal single-cell fluorescence measurements. Each cell is contained within a well etched into the tip of a single optical fibre, with thousands of such fibres arranged in an array. This allows simultaneous measurements across large cell populations.

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Kærn, M., Elston, T., Blake, W. et al. Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6, 451–464 (2005). https://doi.org/10.1038/nrg1615

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