Advertisement

Swarm Intelligence Algorithms in Bioinformatics

Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 94)

Summary

Research in bioinformatics necessitates the use of advanced computing tools for processing huge amounts of ambiguous and uncertain biological data. Swarm Intelligence (SI) has recently emerged as a family of nature inspired algorithms, especially known for their ability to produce low cost, fast and reasonably accurate solutions to complex search problems. In this chapter, we explore the role of SI algorithms in certain bioinformatics tasks like microarray data clustering, multiple sequence alignment, protein structure prediction and molecular docking. The chapter begins with an overview of the basic concepts of bioinformatics along with their biological basis. It also gives an introduction to swarm intelligence with special emphasis on two specific SI algorithms well-known as Particle Swarm Optimization (PSO) and Ant Colony Systems (ACS). It then provides a detailed survey of the state of the art research centered around the applications of SI algorithms in bioinformatics. The chapter concludes with a discussion on how SI algorithms can be used for solving a few open ended problems in bioinformatics.

Keywords

Genetic Algorithm Particle Swarm Optimization Particle Swarm Optimization Algorithm Travel Salesman Problem Swarm Intelligence 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Baldi P and Brunak S (1998) Bioinformatics: The Machine Learning Approach, MIT Press, Cambridge, MA.Google Scholar
  2. 2.
    Altman RB, Valencia A, Miyano S and Ranganathan, S (2001) Challenges for intelligent systems in biology, IEEE Intelligent Systems, vol. 16, no. 6, pp. 14–20.CrossRefGoogle Scholar
  3. 3.
    Haykin S. (1999) Neural Networks: A Comprehensive Foundation, Prentice Hall.Google Scholar
  4. 4.
    Holland JH (1975) Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor.Google Scholar
  5. 5.
    Goldberg DE (1975) Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.Google Scholar
  6. 6.
    Mitra S and Hayashi Y (2006) Bioinformatics with soft computing, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Vol. 36, pp. 616–635.CrossRefGoogle Scholar
  7. 7.
    Bonabeau E, Dorigo M, Theraulaz G (2001) Swarm intelligence: From natural to artificial systems. Journal of Artificial Societies and Social Simulation, 4(1).Google Scholar
  8. 8.
    Engelbrecht AP (2005) Fundamentals of Computational Swarm Intelligence. Wiley.Google Scholar
  9. 9.
    Beni G and Wang U (1989) Swarm intelligence in cellular robotic systems. In NATO Advanced Workshop on Robots and Biological Systems, Il Ciocco, Tuscany, Italy.Google Scholar
  10. 10.
    Kennedy J, Eberhart R (1995) Particle swarm optimization, In Proceedings of IEEE International conference on Neural Networks. 1942–1948.Google Scholar
  11. 11.
    Dorigo M (1992) Optimization, learning, and natural algorithms, Ph.D. dissertation (in Italian), Dipartimento di Elettronica, Politecnico di Milano, Milano, Italy.Google Scholar
  12. 12.
    Dorigo M, Di Caro G and Gambardella L (1999) Ant colony optimization: A new metaheuristic. In PJ Angeline, Z Michalewicz, M Schoenauer, X Yao, and A Zalzala, (eds), Proceedings of the Congress on Evolutionary Computation, IEEE Press, Vol. 2, pp. 1470–1477.Google Scholar
  13. 13.
    Lewin B (1995) Genes VII. Oxford University Press, New York, NY.Google Scholar
  14. 14.
    Wu AS and Lindsay RK (1996) A Survey of Intron Research in Genetics, In Proc. 4th Conf. of on Parallel Problem Solving from Nature, pp. 101–110.Google Scholar
  15. 15.
    Setubal J and Meidanis J (1999) Introduction to Computational Molecular Biology, International Thomson Publishing, 20 park plaza, Boston, MA 02116.Google Scholar
  16. 16.
  17. 17.
    Couzin ID, Krause J, James R, Ruxton GD, Franks NR (2002) Collective Memory and Spatial Sorting in Animal Groups, Journal of Theoretical Biology, 218, pp. 1–11.CrossRefMathSciNetGoogle Scholar
  18. 18.
    Krause J and Ruxton GD (2002) Living in Groups. Oxford: Oxford University Press.Google Scholar
  19. 19.
    Partridge BL, Pitcher TJ (1980) The sensory basis of fish schools: relative role of lateral line and vision. Journal of Comparative Physiology, 135, pp. 315–325.CrossRefGoogle Scholar
  20. 20.
    Partridge BL (1982) The structure and function of fish schools. Science American, 245, pp. 90–99.Google Scholar
  21. 21.
    Major PF, Dill LM (1978) The three-dimensional structure of airborne bird flocks. Behavioral Ecology and Sociobiology, 4, pp. 111–122.CrossRefGoogle Scholar
  22. 22.
    Branden CI and Tooze J (1999) Introduction to Protein Structure: 2nd edition. Garland Publishing, New York, 2nd edition.Google Scholar
  23. 23.
    Grosan C, Abraham A and Monica C (2006) Swarm Intelligence in Data Mining, in Swarm Intelligence in Data Mining, Abraham A, Grosan C and Ramos V (Eds), Springer, pp. 1–16.Google Scholar
  24. 24.
    Milonas MM (1994) Swarms, phase transitions, and collective intelligence, In Langton CG Ed., Artificial Life III, Addison Wesley, Reading, MA.Google Scholar
  25. 25.
    Serra R and Zanarini G (1990) Complex Systems and Cognitive Processes. New York, NY: Springer-Verlag.Google Scholar
  26. 26.
    Flake G (1999) The Computational Beauty of Nature. Cambridge, MA: MIT Press.Google Scholar
  27. 27.
    Kennedy J, Eberhart R and Shi Y (2001) Swarm Intelligence, Morgan Kaufmann Academic Press.Google Scholar
  28. 28.
    Dorigo M and Gambardella LM (1996) A Study of Some Properties of Ant Q, In Proc. PPSN IV - 4th Int. Conf. Parallel Problem Solving From Nature, Berlin, Germany: Springer-Verlag, pp. 656–665.CrossRefGoogle Scholar
  29. 29.
    Dorigo M and Gambardella LM (1997) Ant colony system: A cooperative learning approach to the traveling salesman problem, IEEE Trans. Evol. Comput., vol. 1, pp. 53–66.CrossRefGoogle Scholar
  30. 30.
    Deneubourg JL (1997) Application de I’ordre par fluctuations? la descriptio de certaines? tapes de la construction dun id chez les termites, Insect Sociaux, vol. 24, pp. 117–130.CrossRefGoogle Scholar
  31. 31.
    Dorigo M, Maniezzo V and Colorni A (1996) The ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybern. B, vol. 26.Google Scholar
  32. 32.
    Kennedy J (1999) Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance, Proceedings of the 1999 Congress of Evolutionary Computation, vol. 3, IEEE Press, pp. 1931–1938.Google Scholar
  33. 33.
    Kennedy J and Mendes R (2002) Population structure and particle swarm performance. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC), IEEE Press, pp. 1671–1676.Google Scholar
  34. 34.
    Watts DJ and Strogatz SH (1998) Collective dynamics of small-world networks. Nature, 393, 440–442.CrossRefGoogle Scholar
  35. 35.
    Dall’Asta L, Baronchelli A, Barrat A and Loreto V (2006) Agreement dynamics on small-world networks. Europhysics Letters.Google Scholar
  36. 36.
    Barrat A and Weight M (2000) On the properties of small-world network models. The European Physical Journal, 13, pp. 547–560.Google Scholar
  37. 37.
    Moore C and Newman MEJ (2000) Epidemics and percolation in small-world networks. Physics. Review. E 61, 5678–5682.CrossRefGoogle Scholar
  38. 38.
    Jasch F and Blumen A (2001) Trapping of random walks on small-world networks. Physical Review E 64, 066104.CrossRefGoogle Scholar
  39. 39.
    Chen J, Antipov E, Lemieux B, Cedeno W, and Wood DH (1999) DNA computing implementing genetic algorithms, Evolution as Computation, Springer Verlag, New York, pp. 39–49.Google Scholar
  40. 40.
    Vesterstrom J and Thomsen R (2004) A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 04), IEEE Press, pp. 1980–1987.Google Scholar
  41. 41.
    Das S, Konar A, Chakraborti UK (2005) A New Evolutionary Algorithm Applied to the Design of Two-dimensional IIR Filters in ACM-SIGEVO Proceedings of Genetic and Evolutionary Computation Conference (GECCO-2005), Washington DC.Google Scholar
  42. 42.
    Hassan R, Cohanim B and de Weck O (2005) Comparison of Particle Swarm Optimization and the Genetic Algorithm, AIAA-2005-1897, 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference.Google Scholar
  43. 43.
    Luscombe NM, Greenbaum D and Gerstein M (2001) What is Bioinformatics? A Proposed Definition and Overview of the Field, Yearbook of Medical Informatics, pp. 83–100.Google Scholar
  44. 44.
    Quackenbush J (2001) Computational analysis of microarray data, National Review of Genetics, vol. 2, pp. 418–427.CrossRefGoogle Scholar
  45. 45.
    Special Issue on Bioinformatics, IEEE Computer, vol. 35, July 2002.Google Scholar
  46. 46.
    Jain AK, Murty MN and Flynn, PJ (1999) Data clustering: a review, ACM Computing Surveys, vol. 31, no. 3, pp. 264–323.CrossRefGoogle Scholar
  47. 47.
    Baker TK, Carfagna MA, Gao H, Dow ER, Li O, Searfoss GH, and Ryan TP (2001) Temporal Gene Expression Analysis of Monolayer Cultured Rat Hepatocytes, Chem. Res. Toxicol., Vol. 14, No. 9.Google Scholar
  48. 48.
    Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D and Levine AJ (1999) Broad Patterns of Gene Expression Revealed by Clustering Analysis of Tumor and Normal Colon Tissues Probed by Oligonucleotide Arrays, Proc. Natl. Acad. Sci. USA, Cell Biology, Vol. 96, pp. 6745–6750.CrossRefGoogle Scholar
  49. 49.
    Maurice G and Kendall M (1961) The Advanced Theory of Statistics, Vol. 2, Charles Griffin and Company Limited.Google Scholar
  50. 50.
    Wen X, Fuhrman S, Michaels GS, Carr DB, Smith S, Barker JL, and Somogyi R (1998) Large-scale temporal gene expression mapping of central nervous system development, Proc. Natl. Acad. Sci. USA, Neurobiology, Vol. 95, pp. 334–339.CrossRefGoogle Scholar
  51. 51.
    Spellman EM, Brown PL, Brown D (1998) Cluster Analysis and Display of Genome-wide expression patterns, Proc. Natl. Acad. Sci. USA 95: 14863–14868.CrossRefGoogle Scholar
  52. 52.
    Yeung KY, Ruzzo WL (2001) Principal Component Analysis for Clustering Gene Expression Data, Bioinformatics, 17, pp. 763–774.CrossRefGoogle Scholar
  53. 53.
    Raychaudhuri S, Stuart JM and Altman RB (2000) Principal Components Analysis to Summarize Microarray Experiments: Application to Sporulation Time Series, Pacific Symposium on Biocomputing 2000, Honolulu, Hawaii, pp. 452–463.Google Scholar
  54. 54.
    Li L, Weinberg CR, Darden TA and Pedersen LG (2001) Gene Selection for Sample Classification Based on Gene Expression Data: Study of Sensitivity to Choice of Parameters of the GA/KNN Method, Bioinformatics, 17, pp. 1131–1142.CrossRefGoogle Scholar
  55. 55.
    Herrero J, Valencia A and Dopazo J (2001) A hierarchical unsupervised growing neural network for clustering gene expression patterns, Bioinformatics, 17, pp. 126–136.CrossRefGoogle Scholar
  56. 56.
    Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES and Golub TR (1999) Interpreting patterns of gene expression with self organizing maps: Methods and applications to hematopoietic differentiation. PNAS, 96, pp. 2907–2912.CrossRefGoogle Scholar
  57. 57.
    Toronen P, Kolehmainen M, Wong G, Castren E (1999) Analysis of Gene Expression Data Using Self-organizing Maps, FEBS letters 451, pp. 142–146.CrossRefGoogle Scholar
  58. 58.
    Xiao X, Dow ER, Eberhart RC, Miled ZB and Oppelt RJ (2003) Gene Clustering Using Self-Organizing Maps and Particle Swarm Optimization, Proc of the 17th International Symposium on Parallel and Distributed Processing (PDPS ’03), IEEE Computer Society, Washington DC.Google Scholar
  59. 59.
    Kohonen T (1995) Self-organizing Maps, 2nd ed., Springer-Verlag, Berlin.Google Scholar
  60. 60.
  61. 61.
    Liu BF, Chen HM, Huang HL, Hwang SF and Ho SY (2005) Flexible protein-ligand docking using particle swarm optimization, in Proc. of Congress on Evolutionary Computation (CEC 2005), IEEE Press, Washinton DC.Google Scholar
  62. 62.
    Jones G, Willett P, Glen RC, Leach AR and Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267(3): pp. 727–748.CrossRefGoogle Scholar
  63. 63.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK and Olson AJ (1998) Automated docking using a lamarckian genetic algorithm and an empirical binding free energy function. Journal of Computational Chemistry, 19(14): pp. 1639–1662.CrossRefGoogle Scholar
  64. 64.
    Ewing TJA, Makino S, Skillman AG and Kuntz ID (2001) Dock 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design, 15(5): pp. 411–428.CrossRefGoogle Scholar
  65. 65.
    Lipman DJ, Altschul SF and Kececioglu JD (1989). A tool for multiple sequence alignment. Proc. Natl. Acad. Sci. USA, 86: pp. 4412–4415.CrossRefGoogle Scholar
  66. 66.
    Feng DF, Doolittle RF (1987) Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J. Mol. Evol. 25, pp. 351–360.CrossRefGoogle Scholar
  67. 67.
    Thompson JD, Higgins DG and Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Research, vol. 22, No. 22, pp. 4673–4680.CrossRefGoogle Scholar
  68. 68.
    Chen Y, Pan Y, Chen L, Chen J (2006) Partitioned optimization algorithms for multiple sequence alignment, Proc. of the 20th International Conference on Advanced Information Networking and Applications - (AINA’06), IEEE Computer Society Press, Washington DC., Volume 02, pp. 618–622.Google Scholar
  69. 69.
    Notredame C and Higgins DG, SAGA: sequence alignment by genetic algorithm, Nucleic Acids Research, vol. 24, no. 8, pp. 1515–1524.Google Scholar
  70. 70.
    Rasmussen TK and Krink T (2003) Improved hidden Markov model training for multiple sequence alignment by a particle swarm optimization-evolutionary algorithm hybrid, BioSystems 72 (2003).Google Scholar
  71. 71.
    Stolcke A and Omohundro S (1993) Hidden Markov Model induction by Bayesian model merging. In NIPS 5, pp. 11–18.Google Scholar
  72. 72.
    Hamam Y and Al-Ani T (1996). Simulated annealing approach for Hidden Markov Models. 4th WG-7.6 Working Conference on Optimization-Based Computer-Aided Modeling and Design, ESIEE, France.Google Scholar
  73. 73.
    Felsenstein J (1973). Maximum likelihood estimation of evolutionary trees from continuous characters.Am. J. Hum. Gen. 25: 471–492.Google Scholar
  74. 74.
    Lewis PO (1998), A genetic algorithm for maximum likelihood phylogeny inference using nucleotide sequence data, Molecular Biology and Evolution, vol. 15, no. 3, pp. 277–283.Google Scholar
  75. 75.
    Lemmon AR and Milinkovitch MC (2002) The metapopulation genetic algorithm: An efficient solution for the problem of large phylogeny estimation, Proc. Natl Acad Sci U S A., vol. 99, no. 16, pp. 10516–10521.CrossRefGoogle Scholar
  76. 76.
    Perretto M and Lopes HS (2005) Reconstruction of phylogenetic trees using the ant colony optimization paradigm, Genetic and Molecular Research 4 (3), pp. 581–589.Google Scholar
  77. 77.
    Ando S and Iba H (2002) Ant algorithm for construction of evolutionary tree, in Proc. of Congress on Evolutionary Computation (CEC 2002), IEEE Press, USA.Google Scholar
  78. 78.
    Neethling M and Engelbrecht AP (2006) Determining RNA Secondary Structure using Set-based Particle Swarm Optimization, in Proc. of Congress on Evolutionary Computation (CEC 2006), IEEE Press, USA.Google Scholar
  79. 79.
    Hofacker IL (2003) Vienna rna secondary structure server, Nucleic Acids Research, vol. 31:13, pp. 3429–3431.CrossRefGoogle Scholar
  80. 80.
    Lau KF and Dill KA (1989) A lattice statistical mechanics model of the conformation and sequence space of proteins. Macromolecules 22, pp. 3986–3997.CrossRefGoogle Scholar
  81. 81.
    Richards FM (1977) Areas, volumes, packing, and protein structures. Annu. Rev. Biophys. Bioeng. 6, pp. 151–176.CrossRefGoogle Scholar
  82. 82.
    Krasnogor N, Hart WE, Smith J and Pelta DA (1999) Protein structure prediction with evolutionary algorithms. Proceedings of the Genetic & Evolutionary Computing Conf (GECCO 1999).Google Scholar
  83. 83.
    Shmygelska A, Hoos HH (2003) An Improved Ant Colony Optimization Algorithm for the 2D HP Protein Folding Problem. Canadian Conference on AI 2003: 400–417.MathSciNetGoogle Scholar
  84. 84.
    Shmygelska A, Hoos HH (2005) An ant colony optimization algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics 6:30.CrossRefGoogle Scholar
  85. 85.
    Chu D, Till M and Zomaya A (2005) Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model, Proc. of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS’05), IEEE Computer Society Press.Google Scholar
  86. 86.
    Meksangsouy P and Chaiyaratana N (2003) DNA fragment assembly using an ant colony system algorithm, in Proc. of Congress on Evolutionary Computation (CEC 2006), IEEE Press, USA.Google Scholar
  87. 87.
    Ando S and Iba H (2001) Inference of gene regulatory model by genetic algorithms, Proc. Congress on Evolutionary Computation (CEC 2001), vol. 1, pp. 712–719.CrossRefGoogle Scholar
  88. 88.
    Behera N and Nanjundiah V (1997) Trans-gene regulation in adaptive evolution: a genetic algor-ithm model, Journal of Theoretical Biology, vol. 188, pp. 153–162.CrossRefGoogle Scholar
  89. 89.
    Ando S and Iba H (2000) Quantitative Modeling of Gene Regulatory Network - Identifying the Network by Means of Genetic Algorithms, The Eleventh Genome Informatics Workshop, 2000.Google Scholar
  90. 90.
    Ando S and Iba H (2001) The Matrix Modeling of Gene Regulatory Networks - Reverse Engineering by Genetic Algorithms, Proc. Atlantic Symposium on Computational Biology and Genome Information Systems and Technology.Google Scholar
  91. 91.
    Tominaga D, Okamoto M, Maki Y, Watanabe S and Eguchi Y (1999) Nonlinear Numerical optimization technique based on a genetic algorithm for inverse Problems: Towards the inference of genetic networks, Computer Science and Biology (Proc. German Conf. on Bioinformatics), pp. 127–140.Google Scholar
  92. 92.
    Branden CI and Tooze J (1999) Introduction to Protein Structure: 2nd edition. Garland Publishing, New York, 2nd edition.Google Scholar
  93. 93.
    Liu Y and Beveridge DL (2002) Exploratory studies of ab initio protein structure prediction: multiple copy simulated annealing, amber energy functions, and a generalized born/solvent accessibility solvation model. Proteins, 46.Google Scholar
  94. 94.
    Unger R and Moult J (1993) A genetic algorithm for 3d protein folding simulations. In 5th Proc. Intl. Conf. on Genetic Algorithms, pp. 581–588.Google Scholar
  95. 95.
    Pokarowski P, Kolinski A and Skolnick J (2003) A minimal physically realistic protein-like lattice model: Designing an energy landscape that ensures all-or-none folding to a unique native state. Biophysics Journal, 84: pp. 1518–26.CrossRefGoogle Scholar
  96. 96.
    Kitagawa U and Iba H (2002) Identifying Metabolic Pathways and Gene Regulation Networks with Evolutionary Algorithms, in Evolutionary Computation in Bioinformatics, Fogel GB and Corne DW (Eds.) Morgan Kaufmann.Google Scholar
  97. 97.
    Shayne CG (2005), Drug Discovery Handbook, Wiley-Interscience.Google Scholar
  98. 98.
    Madsen U. (2002), Textbook of Drug Design and Discovery, CRC Press, USA.Google Scholar
  99. 99.
    Venkatasubramanian V, Chan K and Caruthers JM (1995). Evolutionary Design of Molecules with Desired Properties Using the Genetic Algorithm, J. Chem. Inf. Comp. Sci., 35, pp. 188–195.Google Scholar
  100. 100.
    Glen RC and Payne AWR (1995) A Genetic Algorithm for the Automated Generation of Molecule within Constraints. J. Computer-Aided Molecular Design, 9, pp. 181–202.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Center of Excellence for Quantifiable Quality of ServiceNorwegian University of Science and TechnologyTrondheimNorway

Personalised recommendations