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An O(1.787n)-Time Algorithm for Detecting a Singleton Attractor in a Boolean Network Consisting of AND/OR Nodes

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Abstract

The Boolean network (BN) is a mathematical model of genetic networks. It is known that detecting a singleton attractor, which is also called a fixed point, is NP-hard even for AND/OR BNs (i.e., BNs consisting of AND/OR nodes), where singleton attractors correspond to steady states. Though a naive algorithm can detect a singleton attractor for an AND/OR BN in O(n 2n) time, no O((2 − ε)n) (ε> 0) time algorithm was known even for an AND/OR BN with non-restricted indegree, where n is the number of nodes in a BN. In this paper, we present an O(1.787n) time algorithm for detecting a singleton attractor of a given AND/OR BN, along with related results.

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References

  1. Akutsu, T.: On finding attractors in Boolean Networks using SAT algorithms (manuscript)

    Google Scholar 

  2. Akutsu, T., Kuhara, S., Maruyama, O., Miyano, S.: A system for identifying genetic networks from gene expression patterns produced by gene disruptions and overexpressions. Genome Informatics 9, 151–160 (1998)

    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. Albert, R., Barabasi, A-L.: Dynamics of complex systems: Scaling laws for the period of Boolean networks. Physical Review Letters 84, 5660–5663 (2000)

    Article  Google Scholar 

  5. Drossel, B., Mihaljev, T., Greil, F.: Number and length of attractors in a critical Kauffman model with connectivity one. Physical Review Letters 94, 88701 (2005)

    Article  Google Scholar 

  6. Glass, L., Kauffman, S.A.: The logical analysis of continuous, nonlinear biochemical control networks. Journal of Theoretical Biology 39, 103–129 (1973)

    Article  Google Scholar 

  7. Hirsch, E.A.: New worst-case upper bounds for SAT. Journal of Automated Reasoning 24, 397–420 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  8. Huang, S.: Gene expression profiling, genetic networks, and cellular states: an integrating concept for tumorigenesis and drug discovery. Journal of Molecular Medicine 77(6), 469–480 (1999)

    Article  Google Scholar 

  9. Iwama, K., Tamaki, S.: Improved upper bounds for 3-SAT. In: Proc. 15th ACM-SIAM Symposium on Discrete Algorithms, p. 328 (2004)

    Google Scholar 

  10. Kauffman, S.: Metabolic stability and epigenesis in randomly connected genetic nets. Journal of Theoretical Biology 22, 437–467 (1968)

    Article  Google Scholar 

  11. Kauffman, S.: The Origin of Order: Self-organization and selection in evolution. Oxford Univ. Press, New York (1993)

    Google Scholar 

  12. Kauffman, S., Peterson, C., Samuelsson, B., Troein, C.: Random Boolean network models and the yeast transcriptional network. Proceedings of the National Academy of Sciences 100(25), 14796–14799 (2003)

    Article  Google Scholar 

  13. Leone, M., Pagnani, A., Parisi, G., Zagordi, O.: Finite size corrections to random Boolean networks, cond-mat/0611088 (2006)

    Google Scholar 

  14. Milano, M., Roli, A.: Solving the satisfiability problem through Boolean networks. In: Lamma, E., Mello, P. (eds.) AI*IA 99:Advances in Artificial Intelligence. LNCS (LNAI), vol. 1792, pp. 72–93. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  15. Mochizuki, A.: An analytical study of the number of steady states in gene regulatory networks. J. Theoret. Biol. 236, 291–310 (2005)

    Article  MathSciNet  Google Scholar 

  16. Samuelsson, B., Troein, C.: Superpolynomial growth in the number of attractors in Kauffman networks. Physical Review Letters 90, 98701 (2003)

    Google Scholar 

  17. Shmulevich, I., Kauffman, S.: Activities and sensitivities in Boolean network models. Physical Review Letters 93(4), 48701 (2004)

    Article  Google Scholar 

  18. Somogyi, R., Sniegoski, C.A.: Modeling the complexity of genetic networks: Understanding multigenic and pleitropic regulation. Complexity 1(6), 45–63 (1996)

    MathSciNet  Google Scholar 

  19. Yamamoto, M.: An improved Õ (1.234m)-time deterministic algorithm for SAT. In: Proc. International Symposium on Algorithms and Computation, pp. 644–653 (2005)

    Google Scholar 

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

    Google Scholar 

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Erzsébet Csuhaj-Varjú Zoltán Ésik

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Tamura, T., Akutsu, T. (2007). An O(1.787n)-Time Algorithm for Detecting a Singleton Attractor in a Boolean Network Consisting of AND/OR Nodes. In: Csuhaj-Varjú, E., Ésik, Z. (eds) Fundamentals of Computation Theory. FCT 2007. Lecture Notes in Computer Science, vol 4639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74240-1_43

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  • DOI: https://doi.org/10.1007/978-3-540-74240-1_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74239-5

  • Online ISBN: 978-3-540-74240-1

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