Skip to main content
SpringerLink
Log in

Algorithms for Inference, Analysis and Control of Boolean Networks

  • Conference paper
Algebraic Biology (AB 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5147))

Included in the following conference series:

Abstract

Boolean networks (BNs) are known as a mathematical model of genetic networks. In this paper, we overview algorithmic aspects of inference, analysis and control of BNs while focusing on the authors’ works. For inference of BN, we review results on the sample complexity required to uniquely identify a BN. For analysis of BN, we review efficient algorithms for identifying singleton attractors. For control of BN, we review NP-hardness results and dynamic programming algorithms for general and special cases.

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. Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Proc. Pacific Symposium on Biocomputing 1999, pp. 17–28 (1999)

    Google Scholar 

  2. Akutsu, T., Miyano, S., Kuhara, S.: Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. Journal of Computational Biology 7, 331–343 (2000)

    Article  Google Scholar 

  3. Akutsu, T., Miyano, S., Kuhara, S.: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16, 727–734 (2000)

    Article  Google Scholar 

  4. Akutsu, T., Kuhara, S., Maruyama, O., Miyano, S.: Identification of genetic networks by strategic gene disruptions and gene overexpressions under a boolean model. Theoretical Computer Science 298, 235–251 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  5. Akutsu, T., Hayashida, M., Ching, W.-K., Ng, M.K.: Control of Boolean networks: Hardness results and algorithms for tree-structured networks. Journal of Theoretical Biology 244, 670–679 (2007)

    Article  MathSciNet  Google Scholar 

  6. Arpe, J., Reischuk, R.: When does greedy learning of relevant attributes succeed? In: Lin, G. (ed.) COCOON. LNCS, vol. 4598, pp. 296–306. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  7. Bilke, S., Sjunnesson, F.: Number of attractors in random Boolean networks. Physical Review E 72, 016110 (2005)

    Article  Google Scholar 

  8. Datta, A., Choudhary, A., Bittner, M.L., Dougherty, E.R.: External control in Markovian genetic regulatory networks. Machine Learning 52, 169–191 (2003)

    Article  MATH  Google Scholar 

  9. Devloo, V., Hansen, P., Labbé, M.: Identification of all steady states in large networks by logical analysis. Bulletin of Mathematical Biology 65, 1025–1051 (2003)

    Article  Google Scholar 

  10. Drossel, B., Mihaljev, T., Greil, F.: Number and length of attractors in a critical Kauffman model with connectivity one. Physical Review Letters 94, 088701 (2005)

    Article  Google Scholar 

  11. Faryabi, B., Datta, A., Dougherty, E.R.: On approximate stochastic control in genetic regulatory networks. IET Systems Biology 1, 361–368 (2007)

    Article  Google Scholar 

  12. Fukagawa, D., Akutsu, T.: Performance analysis of a greedy algorithm for inferring Boolean functions. Information Processing Letters 93, 7–12 (2005)

    Article  MathSciNet  Google Scholar 

  13. Garg, A., Xenarios, I., Mendoza, L., DeMicheli, G.: An efficient method for dynamic analysis of gene regulatory networks and in silico gene perturbation experiments. In: Speed, T., Huang, H. (eds.) RECOMB 2007. LNCS (LNBI), vol. 4453, pp. 62–76. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  14. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Co., New York (1979)

    MATH  Google Scholar 

  15. Irons, D.J.: Improving the efficiency of attractor cycle identification in Boolean networks. Physica D 217, 7–21 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  16. Kauffman, S.A.: The Origins of Order: Self-organization and Selection in Evolution. Oxford Univ. Press, New York (1993)

    Google Scholar 

  17. Kitano, H.: Computational systems biology. Nature 420, 206–210 (2002)

    Article  Google Scholar 

  18. Kitano, H.: Cancer as a robust system: implications for anticancer therapy. Nature Reviews Cancer 4, 227–235 (2004)

    Article  Google Scholar 

  19. Langmead, C.J., Jha, S.K.: Symbolic approaches for finding control strategies in Boolean networks. In: Proc. 6th Asia-Pacific Bioinformatics Conference, pp. 307–319. Imperial College Press, London (2008)

    Google Scholar 

  20. Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: Proc. Pacific Symposium on Biocomputing 1998, pp. 18–29 (1998)

    Google Scholar 

  21. Milano, M., Roli, A.: Solving the safistiability problem through Boolean networks. In: Lamma, E., Mello, P. (eds.) AI*IA 1999. LNCS (LNAI), vol. 1792, pp. 72–93. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  22. Mochizuki, A.: An analytical study of the number of steady states in gene regulatory networks. Journal of Theoretical Biology 236, 291–310 (2005)

    Article  MathSciNet  Google Scholar 

  23. Mossel, E., O’Donnell, R., Servedio, R.A.: Learning functions of k relevant variables. Journal of Computer and System Sciences 69, 421–434 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  24. Ng, M.K., Zhang, S.-Q., Ching, W.-K., Akutsu, T.: A control model for Markovian genetic regulatory network. Transactions on Computational Systems Biology V, 36–48 (2006)

    Google Scholar 

  25. Pal, R., Datta, A., Bittner, M.L., Dougherty, E.R.: Intervention in context-sensitive probabilistic Boolean networks. Bioinformatics 21, 1211–1218 (2005)

    Article  Google Scholar 

  26. Pal, R., Datta, A., Bittner, M.L., Dougherty, E.R.: Optimal infinite-horizon control for probabilistic Boolean networks. IEEE Transactions on Signal Processing 54, 2375–2387 (2006)

    Article  Google Scholar 

  27. Samuelsson, B., Troein, C.: Superpolynomial growth in the number of attractors in kauffman networks. Physical Review Letters 90, 098701(2003)

    Article  MathSciNet  Google Scholar 

  28. Shmulevich, I., Dougherty, E.R., Kim, S., Zhang, W.: Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 261–274 (2002)

    Article  Google Scholar 

  29. Tamura, T., Akutsu, T.: An improved algorithm for detecting a singleton attractor in a Boolean network consisting of AND/OR nodes. In: Proceedings of the 3rd International Conference on Algebraic Biology (to appear)

    Google Scholar 

  30. Zhang, S.-Q., Hayashida, M., Akutsu, T., Ching, W.-K., Ng, M.K.: Algorithms for finding small attractors in Boolean networks. EURASIP Journal on Bioinformatics and Systems Biology 2007, 20180 (2007)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan

    Tatsuya Akutsu, Morihiro Hayashida & Takeyuki Tamura

Authors
  1. Tatsuya Akutsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Morihiro Hayashida
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Takeyuki Tamura
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Katsuhisa Horimoto Georg Regensburger Markus Rosenkranz Hiroshi Yoshida

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Akutsu, T., Hayashida, M., Tamura, T. (2008). Algorithms for Inference, Analysis and Control of Boolean Networks. In: Horimoto, K., Regensburger, G., Rosenkranz, M., Yoshida, H. (eds) Algebraic Biology. AB 2008. Lecture Notes in Computer Science, vol 5147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85101-1_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-85101-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85100-4

  • Online ISBN: 978-3-540-85101-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation