Skip to main content
SpringerLink
Log in

The Role of Computation in Complex Regulatory Networks

  • Chapter
Power Laws, Scale-Free Networks and Genome Biology

Part of the book series: Molecular Biology Intelligence Unit ((MBIU))

Abstract

Biological phenomena differ significantly from physical phenomena. At the heart of this distinction is the fact that biological entities have computational abilities and thus they are inherently difficult to predict. This is the reason why simplified models that provide the minimal requirements for computation turn out to be very useful to study networks of many components. In this chapter, we briefly review the dynamical aspects of models of regulatory networks, discussing their most salient features, and we also show how these models can give clues about the way in which networks may organize their capacity to evolve, by providing simple examples of the implementation of robustness and modularity.

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

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Hopfield JJ. Physics, computation, and why biology looks so different. J Theor Biol 1994; 171:53–60.

    Article  Google Scholar 

  2. Hartwell LH, Hopfield JJ, Leibler S et al. From molecular to modular cell biology. Nature 1999; 402:C47–C52.

    Article  Google Scholar 

  3. Gould SJ. The Stucture of Evolutionary Theory. Belknap: Harvard Press, 2003.

    Google Scholar 

  4. Wolfram S. Undecidability and intractability in theoretical physics. Phys Rev Lett 1985; 54:735–738.

    Article  ADS  MathSciNet  Google Scholar 

  5. Davidson EH et al. A genomic regulatory network for development. Science 2002; 295:1669–1678.

    Article  ADS  Google Scholar 

  6. Lee TI et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002; 298(5594):799–804.

    Article  ADS  Google Scholar 

  7. Bell AC, West AG, Felsenfeld G. Insulators and boundaries: Versatile regulatory elements in the eukaryotic genome. Science 2001; 291:447–450.

    Article  ADS  Google Scholar 

  8. Albert B, Johnson A, Lewis J et al. Molecular Biology of the Cell. 4th ed. New York: Garland Science, 2002.

    Google Scholar 

  9. Albert R, Othmer HG. The topology of the regulatory interactions predicts the expression pattern of the drosophila segment polarity genes. J Theor Biol 2003; 223:1–18.

    Article  MathSciNet  Google Scholar 

  10. von Dassow G, Meir E, Munro E. The segment polarity network is a robust developmental module. Nature 2000; 406:188–192.

    Article  ADS  Google Scholar 

  11. Barkai N, Leibler S. Robustness in simple biochemical networks. Nature 1997; 387:913–917.

    Article  ADS  Google Scholar 

  12. Solé RV, Ferándndez P, Kauffman SA. Adaptive walks in a gene network model of morphogenesis: Insights into the Cambrian explosion. IJDB 2003.

    Google Scholar 

  13. Hasty J, McMillen D, Isaacs F et al. Computational studies on gene regulatory networks: In numero molecular biology. Nature Rev Gen 2001; 2:268–279.

    Article  Google Scholar 

  14. Butler D. Computing 2010: From black holes to biology. Nature 1999; 402:C67–C70.

    Article  ADS  Google Scholar 

  15. Sipser M. Introduction to the Theory of Computation. PWS Publishing Company, 1997.

    Google Scholar 

  16. McAdams HH, Shapiro L. Circuit simulation of genetic networks. Science 1995; 269:650–656.

    Article  ADS  Google Scholar 

  17. Bray D. Protein molecules as computational elements in living cells. Nature 1995; 376:307–312.

    Article  ADS  Google Scholar 

  18. Sveiczer A, Csikasz-Nagy A, Gyorffy B et al. Modeling the fission yeast cell cycle: Quantized cycle times in weel-cdc25delta mutant cells. PNAS 2000; 97:7865–7870.

    Article  ADS  Google Scholar 

  19. Tyson JJ, Chen K, Novak B. Network dynamics and cell physiology. Nat Rev Mol Cell Biol 2001; 2:908–916.

    Article  Google Scholar 

  20. Anderson PW. More is different. Science 1972; 177:393–396.

    Article  ADS  Google Scholar 

  21. Hayes JP. Principles of Digital Logic Design. Reading: Addison-Wesley, 1993.

    Google Scholar 

  22. Aldana-González M, Coppersmith S, Kadanoff LP. Boolean dynamics with random couplings. In: Kaplan E, Marsden JE, Sreenivasan KR, eds. Perspectives and Problems in Nonlinear Science. A Celebratory Volume in Honor of Lawrence Sirovich. Springer: Applied Mathematical Sciences, 2003.

    Google Scholar 

  23. Kauffman SA. Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 1969; 22:437–467.

    Article  MathSciNet  Google Scholar 

  24. Kauffman SA. The Origins of Order. New York: Oxford University Press, 1993.

    Google Scholar 

  25. Wuensche A. Genomic regulation modeled as a network with basins of attraction. In: Altman RB, Dunker AK, Hunter L, Klien TE, eds. Pacific Symposium on Biocomputi’ 98. Singapore: World Scientific, 1998.

    Google Scholar 

  26. Stanley HE. Introduction to Phase Transitions and Critical Phenomena. New York: Oxford Uni-versity Press, 1971.

    Google Scholar 

  27. Luque B, Solé RV. Phase transitions in random networks: Simple analytic determination of critical points. Phys Rev E 1997; 55:257–260.

    Article  ADS  Google Scholar 

  28. Dorogovtsev SN, Mendes JFF. Evolution of Networks. New York: Oxford University Press, 2003.

    Book  MATH  Google Scholar 

  29. Langton C. Computation at the edge of chaos: Phase transitions and emergent computation. Physica D 1990; 42:12–37.

    Article  ADS  MathSciNet  Google Scholar 

  30. McAdams HH, Arkin A. It’s a noisy business! genetic regulation at the nanomolar scale. Trends Genet 1999; 15(2):65–69.

    Article  Google Scholar 

  31. Solé RV, Delgado J. Universal computation in fluid neural networks. Complexity 1996; 2(2):49–56.

    Article  Google Scholar 

  32. Wolfram S. Universality and complexity in cellular automata. Physica D 1984; 10:1–35.

    Article  ADS  MathSciNet  Google Scholar 

  33. Dhar A, Lakdawala P, Mandal G et al. Role of initial conditions in the classification of the rule space of cellular automata dynamics. Phys Rev E 1995; 51:3032–3037.

    Article  ADS  Google Scholar 

  34. Aldana M, Cluzel P. A natural class of robust networks. PNAS 2003; 100:8710–8714.

    Article  ADS  Google Scholar 

  35. Mitchell M, Hraber PT, Crutchfield JP. Revisiting the edge of chaos: Evolving cellular automata to perform computations. Complex Systems 1993; 7:89–130.

    MATH  Google Scholar 

  36. von Neumann J. Probabilistic logics and the synthesis of reliable organisms from unreliable components. Shannon C, McCarthy J, eds. Automata Studies. Princeton: Princeton University Press, 1956.

    Google Scholar 

  37. Elowitz MB, Levine AJ, Siggia ED et al. Stochastic gene expression in a single cell. Science 2002; 297:1183–1186.

    Article  ADS  Google Scholar 

  38. Blake WJ, Kaern M, Cantor CR et al. Noise in eukaryotic gene expression. Nature 2003; 422:633–637.

    Article  ADS  Google Scholar 

  39. Smith V, Chou KN, Lashkari D et al. Functional analysis of the genes of yeast chromosome V by genetic footprinting. Science 1996; 274:2069–2074.

    Article  ADS  Google Scholar 

  40. Nowak MA, Boerlijst MC, Cooke J et al. Evolution of genetic redundancy. Nature 1997; 388:167–171.

    Article  ADS  Google Scholar 

  41. Gerald M, Edelman GM, Gaily JA. Degeneracy and complexity in biological systems. PNAS 2001; 98:13763–13768.

    Article  ADS  Google Scholar 

  42. Tononi G, Sporns O, Edelman GM. Measures of degeneracy and redundancy in biological net-works. PNAS 1999; 96:3257–3262.

    Article  ADS  Google Scholar 

  43. Wagner A. Robustness against mutations in genetic networks of yeast. Nat Genet 2000; 24:355–361.

    Article  Google Scholar 

  44. Kirschner M, Gerhart J. Evolvability. PNAS 1998; 95:8420–8427.

    Article  ADS  Google Scholar 

  45. Wagner GP, Altenberg L. Complex adaptations and the evolution of evolvability. Evolution 1996; 50:967–976.

    Article  Google Scholar 

  46. Schuster P. How does complexity arise in evolution. Complexity 1996; 2:22–30.

    Article  Google Scholar 

  47. Schuster P, Fontana W, Stadler P et al. From sequences to shapes and back: A case study in RNA secondary structures. Proc Roy Soc London B 1994; 255:279–284.

    Article  ADS  Google Scholar 

  48. von Dassow G, Munro E. Modularity in animal development and evolution: Elements of a conceptual framework for evodevo. J Exp Zool 1999; 406(6792):188–192.

    Google Scholar 

  49. Solé RV, Salazar I, Garcia-Fernández J. Common pattern formation, modularity and phase transitions in a gene network model of morphogenesis. Physica A 2002; 305:640–647.

    Article  MATH  ADS  Google Scholar 

  50. Salazar-Ciudad I, Newman SA, Solé RV. Phenotypic and dynamical transitions in model genetic networks i: Emergence of patterns and genotype-phenotype relationships. Evol Dev 2001; 3(2):84–94.

    Article  Google Scholar 

  51. Wagner GP. Homologues, natural kinds, and the evolution of modularity. Am Zool 1996; 36:36–43.

    Google Scholar 

  52. Pawson T, Nash P. Assembly of cell regulatory systems through protein interaction domains. Science 2003; 300:445–452.

    Article  Google Scholar 

  53. Buchler NE, Gerland U, Hwa T. On schemes of combinatorial transcription logic. Proc Natl Acad Sci USA 2003; 100:5136–5141.

    Article  ADS  Google Scholar 

  54. Girvan M, Newman MEJ. Community structure in social and biological networks. PNAS 2002; 99:8271–8276.

    Article  MathSciNet  Google Scholar 

  55. Ihmels J, Friedlander G, Bergmann S et al. Revealing modular organization in the yeast transcriptional network. Nat Genet 2002; 31:370–377.

    Google Scholar 

  56. Zhou H. Distance, dissimilarity index, and network community structure. Phys Rev E 2003; 67:061901.

    Article  ADS  Google Scholar 

  57. Ravasz E, Somera AL, Mongru DA et al. Hierarchical organization of modularity in metabolic networks. Science 2002; 297:1551–1555.

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICREA-Complex Systems Lab, Universitat Pompeu Fabra (GRIB), Dr Aiguader 80, 08003, Barcelona, Spain

    Pau Fernández & Ricard V. Solé

  2. Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico, 87501, USA

    Ricard V. Solé

Authors
  1. Pau Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricard V. Solé
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ricard V. Solé .

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Eurekah.com and Springer Science+Business Media

About this chapter

Cite this chapter

Fernández, P., Solé, R.V. (2006). The Role of Computation in Complex Regulatory Networks. In: Power Laws, Scale-Free Networks and Genome Biology. Molecular Biology Intelligence Unit. Springer, Boston, MA. https://doi.org/10.1007/0-387-33916-7_12

Download citation

Publish with us

Policies and ethics

Search

Navigation