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EURASIP Journal on Bioinformatics and Systems Biology logoLink to EURASIP Journal on Bioinformatics and Systems Biology
. 2007 Apr 19;2007(1):97356. doi: 10.1155/2007/97356

Fixed Points in Discrete Models for Regulatory Genetic Networks

Dorothy Bollman 1,, Omar Colón-Reyes 1, Edusmildo Orozco 2
PMCID: PMC3171357  PMID: 18274651

Abstract

It is desirable to have efficient mathematical methods to extract information about regulatory iterations between genes from repeated measurements of gene transcript concentrations. One piece of information is of interest when the dynamics reaches a steady state. In this paper we develop tools that enable the detection of steady states that are modeled by fixed points in discrete finite dynamical systems. We discuss two algebraic models, a univariate model and a multivariate model. We show that these two models are equivalent and that one can be converted to the other by means of a discrete Fourier transform. We give a new, more general definition of a linear finite dynamical system and we give a necessary and sufficient condition for such a system to be a fixed point system, that is, all cycles are of length one. We show how this result for generalized linear systems can be used to determine when certain nonlinear systems (monomial dynamical systems over finite fields) are fixed point systems. We also show how it is possible to determine in polynomial time when an ordinary linear system (defined over a finite field) is a fixed point system. We conclude with a necessary condition for a univariate finite dynamical system to be a fixed point system.

[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]

Contributor Information

Dorothy Bollman, Email: bollman@cs.uprm.edu.

Omar Colón-Reyes, Email: ocolon@math.uprm.edu.

Edusmildo Orozco, Email: eorozco@uprrp.edu.

References

  1. Lynch JF On the threshold of chaos in random Boolean cellular automata Random Structures & Algorithms 199562-3239–260. 10.1002/rsa.324006021221752531 [DOI] [Google Scholar]
  2. Elspas B. The theory of autonomous linear sequential networks. IRE Transactions on Circuit Theory. 1959;6(1):45–60. [Google Scholar]
  3. Plantin J, Gunnarsson J, Germundsson R. Symbolic algebraic discrete systems theory—applied to a fighter aircraft. Proceedings of the 34th IEEE Conference on Decision and Control, New Orleans, La, USA, December 1995. pp. 1863–1864.
  4. Bollman D Orozco E Moreno O Laganà A, Gavrilova ML, Kumar V et al.. A parallel solution to reverse engineering genetic networks Computational Science and Its Applications—ICCSA 2004—Part 3, Lecture Notes in Computer Science, Springer, Berlin, Germany 20043045490–497.21747817 [Google Scholar]
  5. Jarrah AS, Vastani H, Duca K, Laubenbacher R. An optimal control problem for in vitro virus competition. Proceedings of the 43rd IEEE Conference on Decision and Control (CDC '), Nassau, Bahamas, December 2004. pp. 579–584.
  6. Laubenbacher R, Stigler B. A computational algebra approach to the reverse engineering of gene regulatory networks. Journal of Theoretical Biology. 2004;229(4):523–537. doi: 10.1016/j.jtbi.2004.04.037. [DOI] [PubMed] [Google Scholar]
  7. Fuller GN, Rhee CH, Hess KR. et al. Reactivation of insulin-like growth factor binding protein 2 expression in glioblastoma multiforme. Cancer Research. 1999;59(17):4228–4232. [PubMed] [Google Scholar]
  8. Shmulevich I, Dougherty ER, Zhang W. Gene perturbation and intervention in probabilistic Boolean networks. Bioinformatics. 2002;18(10):1319–1331. doi: 10.1093/bioinformatics/18.10.1319. [DOI] [PubMed] [Google Scholar]
  9. Hernández Toledo RA. Linear finite dynamical systems. Communications in Algebra. 2005;33(9):2977–2989. doi: 10.1081/AGB-200066211. [DOI] [Google Scholar]
  10. Bähler J, Svetina S. A logical circuit for the regulation of fission yeast growth modes. Journal of Theoretical Biology. 2005;237(2):210–218. doi: 10.1016/j.jtbi.2005.04.008. [DOI] [PubMed] [Google Scholar]
  11. Moreno O, Bollman D, Aviño M. Finite dynamical systems, linear automata, and finite fields. Proceedings of the WSEAS International Conference on System Science, Applied Mathematics and Computer Science, and Power Engineering Systems, Copacabana, Rio de Janeiro, Brazil, October 2002. pp. 1481–1483.
  12. Sunar B, Cyganski D. In: Proceedings of the 7th International Workshop Cryptographic Hardware and Embedded Systems (CHES '05), Lecture Notes in Computer Science, Edinburgh, UK, August-September 2005. Rao JR, Sunar B, editor. Vol. 3659. Comparison of bit and word level algorithms for evaluating unstructured functions over finite rings; pp. 237–249. [Google Scholar]
  13. Zivkovic M. A table of primitive binary polynomials. Mathematics of Computation. 1994;62(205):385–386. [Google Scholar]
  14. Blahut RE. Algebraic Methods for Signal Processing and Communications Coding. Springer, New York, NY, USA; 1991. [Google Scholar]
  15. Yildirim N, Mackey MC. Feedback regulation in the lactose operon: a mathematical modeling study and comparison with experimental data. Biophysical Journal. 2003;84(5):2841–2851. doi: 10.1016/S0006-3495(03)70013-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Laubenbacher R. Network Inference, with an application to yeast system biology. Presentation at the Center for Genomics Science, Cuernavaca, Mexico, September 2006. http://mitla.lcg.unam.mx/ http://mitla.lcg.unam.mx/
  17. Laubenbacher R, Stigler B. Mathematical Tools for Systems Biology. http://people.mbi.ohio-state.edu/bstigler/sb-workshop.pdf.
  18. Just W. The steady state system problem is NP-hard even for monotone quadratic Boolean dynamical systems. Annals of Combinatorics. in press .
  19. Macdonald BR. Finite Rings with Identity. Marcel Dekker, New York, NY, USA; 1974. [Google Scholar]
  20. Colón-Reyes O. Monomial dynamical systems, Ph.D. thesis. Virginia Polytechnic Institute and State University, Blacksburg, Va, USA; 2005. [Google Scholar]
  21. Colón-Reyes O. Monomial Dynamical Systems over Finite Fields. ProQuest, Ann Arbor, Mich, USA; 2005. [Google Scholar]
  22. Storjohann A. An Inline graphic algorithm for the Frobenius normal form. Proceedings of the 23rd International Symposium on Symbolic and Algebraic Computation (ISSAC '98), Rostock, Germany, August 1998. pp. 101–104.
  23. Kaltofen E, Shoup V. Subquadratic-time factoring of polynomials over finite fields. Mathematics of Computation. 1998;67(223):1179–1197. doi: 10.1090/S0025-5718-98-00944-2. [DOI] [Google Scholar]
  24. Colón-Reyes O, Laubenbacher R, Pareigis B. Boolean monomial dynamical systems. Annals of Combinatorics. 2004;8(4):425–439. [Google Scholar]
  25. Colón-Reyes O, Jarrah AS, Laubenbacher R, Sturmfels B. Monomial dynamical systems over finite fields. Journal of Complex Systems. 2006;16(4):333–342. [Google Scholar]
  26. Aho AV, Hopcroft JE, Ullman JD. The Design and Analysis of Computer Algorithms. Addison Wesley, Boston, Mass, USA; 1974. [Google Scholar]
  27. von zur Gathen J, Gerhard J. Modern Computer Algebra. 2. Cambridge University Press, Cambridge, UK; 2003. [Google Scholar]
  28. Ferrer E. A co-design approach to the reverse engineering problem, CISE Ph.D. thesis proposal.
  29. Savas E, Koc CK. Efficient method for composite field arithmetic. Electrical and Computer Engineering, Oregon State University, Corvallis, Ore, USA; 1999. [Google Scholar]
  30. Ferrer E, Bollman D, Moreno O. In: Proceedings of the International Euro-Par Workshops, Lecture Notes in Computer Science, Springer, Dresden, Germany, September 2006. Lehner et al., editor. Vol. 4375. Toward a solution of the reverse engineering problem usings FPGAs; pp. 301–309. [Google Scholar]
  31. Thomas R. Laws for the dynamics of regulatory networks. International Journal of Developmental Biology. 1998;42(3):479–485. [PubMed] [Google Scholar]

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