The European Physical Journal B

, Volume 77, Issue 2, pp 233–237 | Cite as

Regulatory networks and connected components of the neutral space

A look at functional islands
  • G. BoldhausEmail author
  • K. KlemmEmail author
Interdisciplinary Physics


The functioning of a living cell is largely determined by the structure of its regulatory network, comprising non-linear interactions between regulatory genes. An important factor for the stability and evolvability of such regulatory systems is neutrality – typically a large number of alternative network structures give rise to the necessary dynamics. Here we study the discretized regulatory dynamics of the yeast cell cycle [Li et al., PNAS, 2004] and the set of networks capable of reproducing it, which we call functional. Among these, the empirical yeast wildtype network is close to optimal with respect to sparse wiring. Under point mutations, which establish or delete single interactions, the neutral space of functional networks is fragmented into 4.7 × 108 components. One of the smaller ones contains the wildtype network. On average, functional networks reachable from the wildtype by mutations are sparser, have higher noise resilience and fewer fixed point attractors as compared with networks outside of this wildtype component.


Regulatory Network Functional Network Neutral Network Tional Network Yeast Cell Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    M. Kimura, The Neutral Theory of Molecular Evolution (Cambridge University Press, Cambridge, UK, 1983) Google Scholar
  2. 2.
    A. Wagner, Robustness and Evolvability in Living Systems (Princeton University Press, 2005) Google Scholar
  3. 3.
    P. Schuster, W. Fontana, P.F. Stadler, I.L. Hofacker, Proc. Roy. Soc. Lond. B 255, 279 (1994) CrossRefADSGoogle Scholar
  4. 4.
    A. Babajide, I.L. Hofacker, M.J. Sippl, P.F. Stadler, Fold Des. 2, 261 (1997) CrossRefGoogle Scholar
  5. 5.
    E. Davidson, M. Levin, Proceedings of the National Academy of Sciences of the United States of America 102, 4935 (2005) CrossRefADSGoogle Scholar
  6. 6.
    S. Bornholdt, K. Sneppen, Phys. Rev. Lett. 81, 236 (1998) CrossRefADSGoogle Scholar
  7. 7.
    S. Ciliberti, O.C. Martin, A. Wagner, Proceedings of the National Academy of Sciences of the United States of America 104, 13591 (2007) CrossRefADSGoogle Scholar
  8. 8.
    S. Ciliberti, O.C. Martin, A. Wagner, PLoS Computational Biology 3, e15 (2007) Google Scholar
  9. 9.
    F. Stauffer, J. Berg, EPL 88, 48004 (2009) CrossRefADSGoogle Scholar
  10. 10.
    F. Li, T. Long, Y. Lu, Q. Ouyang, C. Tang, Proceedings of the National Academy of Sciences of the United States of America 101, 4781 (2004) CrossRefADSGoogle Scholar
  11. 11.
    P.T. Spellman, G. Sherlock, M.Q. Zhang, V.R. Iyer, K. Anders, M.B. Eisen, P.O. Brown, D. Botstein, B. Futcher, Mol. Biol. Cell. 9, 3273 (1998) Google Scholar
  12. 12.
    S. Kauffman, J. Theor. Biol. 22, 437 (1969) CrossRefMathSciNetGoogle Scholar
  13. 13.
    B. Drossel, Reviews of Nonlinear Dynamics and Complexity (Wiley-VCH, 2008), Chap. Random Boolean Networks, Vol. 1, pp. 69–99 Google Scholar
  14. 14.
    K.Y. Lau, S. Ganguli, C. Tang, Phys. Rev. E 75, 051907 (2007) CrossRefADSGoogle Scholar
  15. 15.
    W. Imrich, Product Graphs: Structure And Recognition (Wiley Interscience Series in Discrete Mathematics, 2000) Google Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Bioinformatics Group, Dept. of Computer ScienceUniversity of LeipzigLeipzigGermany

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