Advertisement

Motif Finding Using Ant Colony Optimization

  • Salim Bouamama
  • Abdellah Boukerram
  • Amer F. Al-Badarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6234)

Abstract

A challenging problem in molecular biology is the identification of the specific binding sites of transcription factors in the promoter regions of genes referred to as motifs. This paper presents an Ant Colony Optimization approach that can be used to provide the motif finding problem with promising solutions. The proposed approach incorporates a modified form of the Gibbs sampling technique as a local heuristic optimization search step. Further, it searches both in the space of starting positions as well as in the space of motif patterns so that it has more chances to discover potential motifs. The approach has been implemented and tested on some datasets including the Escherichia coli CRP protein dataset. Its performance was compared with other recent proposed algorithms for finding motifs such as MEME, MotifSampler, BioProspector, and in particular Genetic Algorithms. Experimental results show that our approach could achieve comparable or better performance in terms of motif accuracy within a reasonable computational time.

Keywords

Bioinformatics Ant Colony Optimization Motif Finding Metaheuristics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bailey, T.L., Elkan, C.: Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In: Proc. of the 2nd Int. Conf. on Intelligent Systems for Molecular Biology, pp. 28–36. AAAI Press, Menlo Park (1994)Google Scholar
  2. 2.
    Che, D., Song, Y., Rasheed, K.: MDGA: Motif discovery using a genetic algorithm. In: Proc. of the 2005 Conf. on Genetic and Evolutionary Computation (GECCO 2005), pp. 447–452. ACM Press, Washington (2005)CrossRefGoogle Scholar
  3. 3.
    Das, M., Dai, H.: A survey of the DNA motif finding algorithms. BMC Bioinformatics 8(suppl.7), S21 (2007)Google Scholar
  4. 4.
    Dorigo, M., Maniezzo, V., Colorni, A.: The Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics - Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  5. 5.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  6. 6.
    Jones, N.C., Pevzner, P.A.: An Introduction to Bioinformatics Algorithms. MIT Press, Cambridge (2004)Google Scholar
  7. 7.
    Karpenko, O., Shi, J., Dai, Y.: Prediction of MHC class II binders using the ant colony search strategy. Artificial Intelligence in Medicine 35(1), 147–156 (2005)CrossRefGoogle Scholar
  8. 8.
    Kaya, M.: MOGAMOD: Multi-objective genetic algorithm for motif discovery. Expert Systems with Applications 36, 1039–1047 (2009)CrossRefGoogle Scholar
  9. 9.
    Keith, J.M., Adams, P., Bryant, D., Kroese, D.P., Mitchelson, K.R., Cochran, D., Lala, G.H.: A simulated annealing algorithm for finding consensus sequences. Bioinformatics 18(11), 1494–1499 (2002)CrossRefGoogle Scholar
  10. 10.
    Lawrence, C., Altschul, S., Boguski, M., Liu, J., Neuwald, A., Wootton, J.: A Gibbs sampling strategy for multiple alignments. Science 262(5131), 208–214 (1993)CrossRefGoogle Scholar
  11. 11.
    Liao, Y.J., Yang, C.B., Shiau, S.H.: Motif finding in biological sequences. In: Proc. of 2003 Symposium on Digital Life and Internet Technologies, Tainan, Taiwan, pp. 89–98 (2003)Google Scholar
  12. 12.
    Liu, F.F., Tsai, J.J., Chen, R., Chen, S., Shih, S.: FMGA: Finding motifs by genetic algorithm. In: IEEE 4th Symposium on Bioinformatics and Bioengineering (BIBE 2004), pp. 459–466. IEEE Press, Los Alamitos (2004)CrossRefGoogle Scholar
  13. 13.
    Liu, X., Brutlag, D.L., Liu, J.: BioProspector: Discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. In: Pac. Symp. Biocomput., pp. 127–138 (2001)Google Scholar
  14. 14.
    Pevzner, P., Sze, S.: Combinatorial approaches to finding subtle signals in DNA sequences. In: Proc. of the 8th Int. Conf. on Intelligent Systems for Molecular Biology (ISMB 2000), pp. 269–278. AAAI Press, San Diego (2000)Google Scholar
  15. 15.
    Seehuus, R., Tveit, A., Edsberg, O.: Discovering biological motifs with genetic programming. In: Proc. of the 2005 Conf. on Genetic and Evolutionary Computation (GECCO 2005), pp. 401–408. ACM Press, Washington (2005)CrossRefGoogle Scholar
  16. 16.
    Stormo, G.D., Hartzell, G.W.: Identifying protein-binding sites from unaligned DNA fragments. Proc. Natl. Acad. Sci. 86(4), 1183–1187 (1989)CrossRefGoogle Scholar
  17. 17.
    Stützle, T., Hoos, H.: \(\mathcal{MAX}\)-\(\mathcal{MIN}\) ant system. Future Generation Computer Systems 16, 889–914 (2000)CrossRefGoogle Scholar
  18. 18.
    Thijs, G., Lescot, M., Marchal, K., Rombauts, S., Moor, B.D., Rouzé, P., Moreau, Y.: A higher order background model improves the detection of regulatory elements by Gibbs Sampling. Bioinformatics 17(12), 1113–1122 (2001)CrossRefGoogle Scholar
  19. 19.
    Tompa, M., et al.: Assessing computational tools for the discovery of transcription factor binding sites. Nature Biotechnology 23(1), 137–144 (2005)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Salim Bouamama
    • 1
  • Abdellah Boukerram
    • 2
  • Amer F. Al-Badarneh
    • 3
  1. 1.Department of Computer ScienceUniversity of M’silaAlgeria
  2. 2.Department of Computer ScienceUniversity of SétifAlgeria
  3. 3.Jordan University of Science and TechnologyIrbidJordan

Personalised recommendations