Skip to main content
SpringerLink
Log in

Code, Context, and Epigenetic Catalysis in Gene Expression

  • Chapter
Transactions on Computational Systems Biology XI

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 5750))

Abstract

We examine a class of probability models describing how epigenetic context affects gene expression and organismal development, using the asymptotic limit theorems of information theory in a highly formal manner. Taking classic results on spontaneous symmetry breaking abducted from statistical physics in groupoid, rather than group, circumstances, the work suggests that epigenetic information sources act as analogs to a tunable catalyst, directing development into different characteristic pathways according to the structure of external signals. The results have significant implications for epigenetic epidemiology, in particular for understanding how environmental stressors, in a large sense, can induce a broad spectrum of developmental disorders in humans.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ash, R.: Information Theory. Dover Publications, New York (1990)

    MATH  Google Scholar 

  2. Atlan, H., Cohen, I.: Immune information, self-organization, and meaning. International Immunology 10, 711–717 (1998)

    Article  Google Scholar 

  3. Atmanspacher, H.: Toward an information theoretical implementation of contextual conditions for consciousness. Acta Biotheoretica 54, 157–160 (2006)

    Article  Google Scholar 

  4. Baars, B.: A Cognitive Theory of Consciousness. Cambridge University Press, New York (1988)

    Google Scholar 

  5. Baars, B.: Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Progress in Brain Research 150, 45–53 (2005)

    Article  Google Scholar 

  6. Backdahl, L., Bushell, A., Beck, S.: Inflammatory signalling as mediator of epigenetic modulation in tissue-specific chronic inflammation. The International Journal of Biochemistry and Cell Biology (2009), doi:10.1016/j.biocel.2008.08.023

    Google Scholar 

  7. Beck, C., Schlogl, F.: Thermodynamics of Chaotic Systems. Cambridge University Press, Cambridge (1995)

    MATH  Google Scholar 

  8. Bennett, M., Hacker, P.: Philosophical Foundations of Neuroscience. Blackwell Publishing, Malden (2003)

    Google Scholar 

  9. Bennett, C.: Logical depth and physical complexity. In: Herkin, R. (ed.) The Universal Turing Machine: A Half-Century Survey, pp. 227–257. Oxford University Press, Oxford (1988)

    Google Scholar 

  10. Bos, R.: Continuous representations of groupoids. arXiv:math/0612639 (2007)

    Google Scholar 

  11. Bossdorf, O., Richards, C., Pigliucci, M.: Epigenetics for ecologists. Ecology Letters 11, 106–115 (2008)

    Google Scholar 

  12. Britten, R., Davidson, E.: Gene regulation for higher cells: a theory. Science 165, 349–357 (1969)

    Article  Google Scholar 

  13. Brown, R.: From groups to groupoids: a brief survey. Bulletin of the London Mathematical Society 19, 113–134 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  14. Buneci, M.: Representare de Groupoizi. Editura Mirton, Timisoara (2003)

    Google Scholar 

  15. Cannas Da Silva, A., Weinstein, A.: Geometric Models for Noncommutative Algebras. American Mathematical Society, RI (1999)

    MATH  Google Scholar 

  16. Ciliberti, S., Martin, O., Wagner, A.: Robustness can evolve gradually in complex regulatory networks with varying topology. PLoS Computational Biology 3(2), e15 (2007)

    Article  MathSciNet  Google Scholar 

  17. Ciliberti, S., Martin, O., Wagner, A.: Innovation and robustness in complex regulatory gene networks. Proceedings of the National Academy of Sciences 104, 13591–13596 (2007)

    Article  Google Scholar 

  18. Cohen, I.: Immune system computation and the immunological homunculus. In: Nierstrasz, O., Whittle, J., Harel, D., Reggio, G. (eds.) MoDELS 2006. LNCS, vol. 4199, pp. 499–512. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  19. Cohen, I., Harel, D.: Explaining a complex living system: dynamics, multi-scaling, and emergence. Journal of the Royal Society: Interface 4, 175–182 (2007)

    Google Scholar 

  20. Cover, T., Thomas, J.: Elements of Information Theory. John Wiley and Sons, New York (1991)

    Book  MATH  Google Scholar 

  21. Crews, D., McLachlan, J.A.: Epigenetics, evolution, endocrine disruption, health, and disease. Endocrinology 147, S4–S10 (2006)

    Article  Google Scholar 

  22. Crews, D., Gore, A., Hsu, T., Dangleben, N., Spinetta, M., Schallert, T., Anway, M., Skinner, M.: Transgenerational epigenetic imprints on mate preference. Proceedings of the National Academy of Sciences 104, 5942–5946 (2007)

    Article  Google Scholar 

  23. Dehaene, S., Naccache, L.: Towards a cognitive neuroscience of consciousness: basic evidence and a workspace framework. Cognition 79, 1–37 (2001)

    Article  Google Scholar 

  24. Dembo, A., Zeitouni, O.: Large Deviations: Techniques and Applications, 2nd edn. Springer, New York (1998)

    Book  MATH  Google Scholar 

  25. Dias, A., Stewart, I.: Symmetry groupoids and admissible vector fields for coupled cell networks. Journal of the London Mathematical Society 69, 707–736 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  26. Dretske, F.: The explanatory role of information. Philosophical Transactions of the Royal Society A 349, 59–70 (1994)

    Article  Google Scholar 

  27. Emery, M.: Stochastic Calculus on Manifolds. Springer, New York (1989)

    Book  MATH  Google Scholar 

  28. English, T.: Evaluation of evolutionary and genetic optimizers: no free lunch. In: Fogel, L., Angeline, P., Back, T. (eds.) Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming, pp. 163–169. MIT Press, Cambridge (1996)

    Google Scholar 

  29. Erdos, P., Renyi, A.: On the evolution of random graphs (1960); reprinted in The Art of Counting, pp. 574–618 (1973), and in Selected Papers of Alfred Renyi, pp. 482–525 (1976)

    Google Scholar 

  30. Ficici, S., Milnik, O., Pollak, J.: A game-theoretic and dynamical systems analysis of selection methods in coevolution. IEEE Transactions on Evolutionary Computation 9, 580–602 (2005)

    Article  Google Scholar 

  31. Feynman, R.: Lectures on Computation. Westview Press, New York (2000)

    Google Scholar 

  32. Foley, D., Craid, J., Morley, R., Olsson, C., Dwyer, T., Smith, K., Saffery, R.: Prospects for epigenetic epidemiology. American Journal of Epidemiology 169, 389–400 (2009)

    Article  Google Scholar 

  33. Gilbert, S.: Mechanisms for the environmental regulation of gene expression: ecological aspects of animal development. Journal of Bioscience 30, 65–74 (2001)

    Article  Google Scholar 

  34. Glazebrook, J.F., Wallace, R.: Small worlds and red queens in the global workspace: an information-theoretic approach. Cognitive Systems Reserch (2009), doi:10.1016/j.cogsys.2009.01.002

    Google Scholar 

  35. Golubitsky, M., Stewart, I.: Nonlinear dynamics and networks: the groupoid formalism. Bulletin of the American Mathematical Society 43, 305–364 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  36. Goubault, E., Raussen, M.: Dihomotopy as a tool in state space analysis. In: Rajsbaum, S. (ed.) LATIN 2002. LNCS, vol. 2286, pp. 16–37. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  37. Goubault, E.: Some geometric perspectives on concurrency theory. Homology, Homotopy, and Applications 5, 95–136 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  38. Gould, S.: The Structure of Evolutionary Theory. Harvard University Press, Cambridge (2002)

    Google Scholar 

  39. Guerrero-Bosagna, C., Sabat, P., Valladares, L.: Environmental signaling and evolutionary change: can exposure of pregnant mammals to environmental estrogens lead to epigenetically induced evolutionary changes in embryos? Evolution and Development 7, 341–350 (2005)

    Article  Google Scholar 

  40. Holling, C.: Cross-scale morphology, geometry and dynamicsl of ecosystems. Ecological Monographs 41, 1–50 (1992)

    Google Scholar 

  41. Jablonka, E., Lamb, M.: Epigenetic Inheritance and Evolution: The Lamarckian Dimension. Oxford University Press, Oxford (1995)

    Google Scholar 

  42. Jablonka, E., Lamb, M.J.: Epigenetic inheritance in evolution. Journal of Evolutionary Biology 11, 159–183 (1998)

    Article  Google Scholar 

  43. Jablonka, E.: Epigenetic epidemiology. International Journal of Epidemiology 33, 929–935 (2004)

    Article  Google Scholar 

  44. Jaeger, J., Surkova, S., Blagov, M., Janssens, H., Kosman, D., Kozlov, K., Manu, M., Myasnikova, E., Vanario-Alonso, C., Samsonova, M., Sharp, D., Reintiz, J.: Dynamic control of positional information in the early Drosophila embryo. Nature 430, 368–371 (2004)

    Article  Google Scholar 

  45. Jaenisch, R., Bird, A.: Epigenetic regulation of gene expression: how the genome integrates intrinsic and environmental signals. Nature Genetics Supplement 33, 245–254 (2003)

    Article  Google Scholar 

  46. Kastner, M.: Phase transitions and configuration space topology. ArXiv cond-mat/0703401 (2006)

    Google Scholar 

  47. Khinchin, A.: Mathematical Foundations of Information Theory. Dover, New York (1957)

    MATH  Google Scholar 

  48. Krebs, P.: Models of cognition: neurological possibility does not indicate neurological plausibility. In: Bara, B., Barsalou, L., Bucciarelli, M. (eds.) Proceedings of CogSci 2005, Stresa, Italy, pp. 1184–1189 (2005), http://cogprints.org/4498/

  49. Landau, L., Lifshitz, E.: Statistical Physics, Part I, 3rd edn., Part I. Elsevier, New York (2007)

    Google Scholar 

  50. Lee, J.: Introduction to topological manifolds. Springer, New York (2000)

    MATH  Google Scholar 

  51. Maas, W., Natschlager, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Computation 14, 2531–2560 (2002)

    Article  MATH  Google Scholar 

  52. Matsumoto, Y.: An Introduction to Morse Theory. American Mathematical Society, Providence (2002)

    MATH  Google Scholar 

  53. Maturana, H.R., Varela, F.J.: Autopoiesis and Cognition. Reidel Publishing Company, Dordrecht (1980)

    Book  Google Scholar 

  54. Maturana, H.R., Varela, F.J.: The Tree of Knowledge. Shambhala Publications, Boston (1992)

    Google Scholar 

  55. McCauly, J.: Chaos, Dynamics, and Fractals. Cambridge Nonlinear Science Series, Cambridge, UK (1994)

    Google Scholar 

  56. Mjolsness, E., Sharp, D., Reinitz, J.: A connectionist model of development. Journal of Theoretical Biology 152, 429–458 (1991)

    Article  Google Scholar 

  57. O’Nuallain, S.: Code and context in gene expression, cognition, and consciousness. In: Barbiere, M. (ed.) The Codes of Life: The Rules of Macroevolution, ch. 15, pp. 347–356. Springer, New York (2008)

    Chapter  Google Scholar 

  58. O’Nuallain, S., Strohman, R.: Genome and natural language: how far can the analogy be extended? In: Witzany, G. (ed.) Proceedings of Biosemiotics. Tartu University Press, Umweb (2007)

    Google Scholar 

  59. Pettini, M.: Geometry and Topology in Hamiltonian Dynamics and Statistical Mechanics. Springer, New York (2007)

    Book  MATH  Google Scholar 

  60. Pratt, V.: Modeling concurrency with geometry. In: Proceedings of the 18th ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages, pp. 311–322 (1991)

    Google Scholar 

  61. Reinitz, J., Sharp, D.: Mechanisms of even stripe formation. Mechanics of Development 49, 133–158 (1995)

    Article  Google Scholar 

  62. Scherrer, K., Jost, J.: The gene and the genon concept: a functional and information-theoretic analysis. Molecular Systems Biology 3, 87–95 (2007)

    Article  Google Scholar 

  63. Scherrer, K., Jost, J.: Gene and genon concept: coding versus regulation. Theory in Bioscience 126, 65–113 (2007)

    Article  Google Scholar 

  64. Sharp, D., Reinitz, J.: Prediction of mutant expression patterns using gene circuits. BioSystems 47, 79–90 (1998)

    Article  Google Scholar 

  65. Skierski, M., Grundland, A., Tuszynski, J.: Analysis of the three-dimensional time-dependent Landau-Ginzburg equation and its solutions. Journal of Physics A (Math. Gen.) 22, 3789–3808 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  66. Toulouse, G., Dehaene, S., Changeux, J.: Spin glass model of learning by selection. Proceedings of the National Academy of Sciences 83, 1695–1698 (1986)

    Article  MathSciNet  Google Scholar 

  67. Turner, B.: Histone acetylation and an epigeneticv code. Bioessays 22, 836–845 (2000)

    Article  Google Scholar 

  68. Wallace, R.: Consciousness: A Mathematical Treatment of the Global Neuronal Workspace Model. Springer, New York (2005)

    Google Scholar 

  69. Wallace, R.: Culture and inattentional blindness. Journal of Theoretical Biology 245, 378–390 (2007)

    Article  Google Scholar 

  70. Wallace, R.: Toward formal models of biologically inspired, highly parallel machine cognition. International Journal of Parallel, Emergent, and Distributed Systems 23, 367–408 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  71. Wallace, R.: Developmental disorders as pathological resilience domains. Ecology and Society 13(1), 29 (2008), http://www.ecologyandsociety.org/vol13/iss1/art29/

  72. Wallace, R.: Programming coevolutionary machines: the emerging conundrum. International Journal of Parallel, Emergent, and Distributed Systems (in press, 2009)

    Google Scholar 

  73. Wallace, R., Fullilove, M.: Collective Consciousness and its Discontents: Institutional Distributed Cognition, Racial Policy, and Public Health in the United States. Springer, New York (2008)

    Book  Google Scholar 

  74. Wallace, R., Wallace, D.: Punctuated equilibrium in statistical models of generalized coevolutionary resilience: how sudden ecosystem transitions can entrain both phenotype expression and Darwinian selection. In: Priami, C. (ed.) Transactions on Computational Systems Biology IX. LNCS (LNBI), vol. 5121, pp. 23–85. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  75. Wallace, R.G., Wallace, R.: Evolutionary radiation and the spectrum of consciousness. Consciousness and Cognition (2009), doi:10.1016/j.concog.2008.12.002

    Google Scholar 

  76. Waterland, R., Michels, K.: Epigenetic epidemiology of the developmental origins hypothesis. Annual Reviews of Nutrition 27, 363–388 (2007)

    Article  Google Scholar 

  77. Weaver, I.: Epigenetic effects of glucocorticoids. Seminars in Fetal and Neonatal Medicine (2009), doi:10.1016/j.siny.2008.12.002

    Google Scholar 

  78. Weinstein, A.: Groupoids: unifying internal and external symmetry. Notices of the American Mathematical Association 43, 744–752 (1996)

    MATH  Google Scholar 

  79. West-Eberhard, M.: Developmental plasticity and the origin of species differences. Proceedings of the National Academy of Sciences 102, 6543–6549 (2005)

    Article  Google Scholar 

  80. Wiegand, R.: An analysis of cooperative coevolutionary algorithms. PhD Thesis, George Mason University (2003)

    Google Scholar 

  81. Wolpert, D., Macready, W.: No free lunch theorems for search. Santa Fe Institute, SFI-TR-02-010 (1995)

    Google Scholar 

  82. Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)

    Article  Google Scholar 

  83. Wymer, C.R.: Structural nonlinear continuous-time models in econometrics. Macroeconomic Dynamics 1, 518–548 (1997)

    Article  MATH  Google Scholar 

  84. Zhu, R., Rebirio, A., Salahub, D., Kaufmann, S.: Studying genetic regulatory networks at the molecular level: delayed reaction stochastic models. Journal of Theoretical Biology 246, 725–745 (2007)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The New York State Psychiatric Institute, 549 W. 123 St., Suite 16F, New York, NY, 10027

    Rodrick Wallace

  2. Consumers Union, USA

    Deborah Wallace

Authors
  1. Rodrick Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Deborah Wallace
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centre for Computational and Systems Biology, The Microsoft Research - University of Trento, Piazza Manci, 17, 38050, Povo, (TN), Italy

    Corrado Priami

  2. Department of Information Technologies, Ã…bo Akademi University, FI-20520, Turku, Finland

    Ralph-Johan Back

  3. Computational Biomodelling Laboratory, Turku Center for Computer Science, FIN-20520, Turku, Finland

    Ion Petre

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Wallace, R., Wallace, D. (2009). Code, Context, and Epigenetic Catalysis in Gene Expression. In: Priami, C., Back, RJ., Petre, I. (eds) Transactions on Computational Systems Biology XI. Lecture Notes in Computer Science(), vol 5750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04186-0_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04186-0_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04185-3

  • Online ISBN: 978-3-642-04186-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation