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International Conference on Computational Science and Its Applications

ICCSA 2016: Computational Science and Its Applications -- ICCSA 2016 pp 322–331Cite as

Bacteria Foraging Optimization for Drug Design

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9788))

Abstract

The bacterial foraging optimization (BFO) method has been successfully applied in a number of optimization problems, especially alongside Particle Swarm Optimization as hybrid combinations. This relatively recent method is based on the locomotion and behavior of bacteria E.coli, with modifications made over the years to increase search time, space and reduce convergence time. Regardless of changes, BFO algorithms are still based on 4 main features which are Chemotaxis, reproduction, swarming, elimination and dispersal behaviours of E.coli. A nature based algorithm, BFO has been utilized in several optimization problems such as the power loss reduction problem and in the area of PID applications. Ligand docking is another optimization problem that can potentially benefit from BFO application and this paper will focus on the methodology of BFO application and its results. We are of the opinion that the incorporation of BFO in the ligand docking problem is effective and efficient.

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References

  1. Blundell, T.L.: Structure-based drug design. Nature 384, 23–26 (1996). doi:10.1038/384023a0

    Article  Google Scholar 

  2. Jones, G., Willette, P., Glen, R.C., Leach, A.R., Taylor, R.: J. Mol. Biol. 207, 727 (1997)

    Article  Google Scholar 

  3. Huang, S.Y., Zou, X.: Advances and challenges in protein-ligand docking. Int. J. Mol. Sci. 11, 3016–3034 (2010)

    Article  Google Scholar 

  4. Song, C.M., Lim, S.J., Tong, J.C.: Recent advances in computer-aided drug design. Brief. Bioinform. 10, 579–591 (2009)

    Article  Google Scholar 

  5. Schmitt, S., Kuhn, D., Klebe, G.: A new method to detect related function among proteins independent of sequence and fold homology. J. Mol. Biol. 323, 387–406 (2002)

    Article  Google Scholar 

  6. An, J., Totrov, M., Abagyan, R.: Comprehensive identification of “Druggable” protein ligand binding sites. Genome Inf. 15, 31–41 (2004)

    Google Scholar 

  7. Cecchini, M., Kolb, P., Majeux, N., et al.: Automated docking of highly flexible ligands by genetic algorithms: a critical assessment. J. Comput. Chem. 25(3), 412–422 (2003)

    Article  Google Scholar 

  8. Gohlke, H., Klebe, G.: Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew. Chem. Int. Ed. 41, 2644–2676 (2002)

    Article  Google Scholar 

  9. Tame, J.R.H.: Scoring functions–the first 100 years. J. Comput. Aided Mol. Des. 19, 445–451 (2005). doi:10.1007/s10822-005-8483-7

    Article  Google Scholar 

  10. Willett, P.: Genetic algorithms in molecular recognition and design. Trends Biotechnol. 13, 516–521 (1995)

    Article  Google Scholar 

  11. Jones, G., Willett, P., Glen, R.C., et al.: Development and validation of a genetic algorithm for flexible docking. J. Mol. Biol. 267(3), 727–748 (1997)

    Article  Google Scholar 

  12. Ross, B.J.: A Lamarckian evolution strategy for genetic algorithms. In: The Practical Handbook of Genetic Algorithms, p. 16. CRC Press, Boca Raton (1999)

    Google Scholar 

  13. López-Camacho, E., García Godoy, M.J., García-Nieto, J., et al.: Solving molecular flexible docking problems with metaheuristics: a comparative study. Appl. Soft Comput. 28, 379–393 (2015). doi:10.1016/j.asoc.2014.10.049

    Article  Google Scholar 

  14. Bertsimas, D., Tsitsiklis, J.: Simulated annealing. Stat. Sci. 8, 10–15 (1993)

    Article  MATH  Google Scholar 

  15. Rutenbar, R.A.: Simulated annealing algorithms: an overview. IEEE Circuits Devices Mag. 5, 19–26 (1989). doi:10.1109/101.17235

    Article  Google Scholar 

  16. Suman, B., Kumar, P.: A survey of simulated annealing as a tool for single and multiobjective optimization. J. Oper. Res. Soc. 10, 1143 (2006)

    Article  MATH  Google Scholar 

  17. Liu, M., Wang, S.: MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. J. Comput. Aided Mol. Des. 13(5), 435–451 (1999)

    Article  Google Scholar 

  18. Friesner, R.A., Banks, J.L., Murphy, R.B., et al.: Glide: a new approach for rapid, accurate docking and scoring. 1. method and assessment of docking accuracy. J. Med. Chem. 47, 1739–1749 (2004). doi:10.1021/jm0306430

    Article  Google Scholar 

  19. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26, 29–41 (1996). doi:10.1109/3477.484436

    Article  Google Scholar 

  20. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. (ICNN) 4, 1942–1948 (1995)

    Google Scholar 

  21. Kennedy, J.: Small worlds and megaminds: effects of neighborhood topology on particle swarm performance. In: Conference on Evolutionary Computation, pp. 1931–1938 (1999)

    Google Scholar 

  22. Ghosh, A., Ghosh, A., Chowdhury, A., Hazra, J.: An evolutionary approach to drug-design using quantam binary particle swarm optimization algorithm. In: 2012 IEEE Students’ Conference on Electrical, Electronics and Computer Science, pp. 1–4 (2012). doi:10.1109/SCEECS.2012.6184776

  23. Liu, Y., Zhao, L., Li, W., Yang, Y.L.: FIPSDock: a new molecular docking technique driven by fully informed swarm optimization algorithm. J. Comput. Chem. 34, 67–75 (2013). doi:10.1002/jcc.23108

    Article  Google Scholar 

  24. Berg, H.C., Berry, R.M.: E.coli in motion. Phys. Today 58, 64–65 (2005). doi:10.1063/1.1897527

    Google Scholar 

  25. Stocker, R.: Reverse and flick: hybrid locomotion in bacteria. PNAS 108, 2635–2636 (2009). doi:10.1073/pnas.1019199108

    Article  Google Scholar 

  26. Polin, M., Tuval, I., Drescher, K., Gollub, J.P., Goldstein, R.E.: Chlamydomonas swims with two “Gears” in a eukaryotic version of run-and-tumble locomotion. Science 325, 487–4890 (2009). doi:10.1126/science.1172667

    Article  Google Scholar 

  27. Egbert, M.D., Barandiaran, X.E., di Paolo, E.A.: A minimal model of metabolism-based chemotaxis. PLoS Comput. Biol. (2010). doi:10.1371/journal.pcbi.1001004

    MathSciNet  Google Scholar 

  28. Passino, K.M.: Bacterial foraging optimization. Int. J. Swarm Intell. Res. 1, 1–16 (2010). doi:10.4018/jsir.2010010101

    Article  Google Scholar 

  29. Segall, J.E., Block, S.M., Berg, H.C.: Temporal comparisons in bacterial chemotaxis. Proc. Natl. Acad. Sci. USA 83, 8987–8991 (1986). doi:10.1073/pnas.83.23.8987

    Article  Google Scholar 

  30. Madigan, M.T., Martinko, J.M., Stahl, D.A., Clark, D.P.: Brock Microbiology, 13th edn. Pearson, Glenview (2012)

    Google Scholar 

  31. Turner, L., Zhang, R., Darnton, N.C., Berg, H.C.: Visualization of flagella during bacterial swarming. J. Bacteriol. 192, 3259–3267 (2010). doi:10.1128/JB.00083-10

    Article  Google Scholar 

  32. Damton, N.C., Turner, L., Rojevsky, S., Berg, H.C.: Dynamics of bacterial swarming. Biophys. J. 98, 2082–2090 (2010). doi:10.1016/j.bpj.2010.01.053

    Article  Google Scholar 

  33. Das, S., Biswas, A., Dasgupta, S., Abraham, A.: Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Found. Comput. Intell. 3(3), 23–55 (2009). doi:10.1007/978-3-642-01085-9_2

    Google Scholar 

  34. Stewart, E.J., Madden, R., Paul, G., Taddei, F.: Aging and death in an organism that reproduces by morphologically symmetric division. PLoS Biol. 3, 0295–0300 (2005). doi:10.1371/journal.pbio.0030045

    Article  Google Scholar 

  35. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. Mag. 22, 52–67 (2010). doi:10.1109/MCS.2002.1004010

    Article  Google Scholar 

  36. Passino, K.M.: Biomimicry for Optimization, Control and Automation. Springer-Verlag, London (2004)

    MATH  Google Scholar 

  37. Biswas, A., Dasgupta, S., Das, S., Abraham, A.: Synergy of PSO and bacterial foraging optimization-a comparative study on numerical benchmarks. In: Corchado, E., Corchado, J.M., Abraham, A. (eds.) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol. 44, pp. 255–263. Springer, Heidelberg (2008). doi:10.1007/978-3-540-74972-1_34

    Chapter  Google Scholar 

  38. Shen, H., Zhu, Y., Zhou, X., Guo, H., Chang, C.: Bacterial foraging optimization algorithm with particle swarm optimization strategy for global numerical optimization, pp. 497–504. ACM (2009). doi:10.1145/1543834.1543901

  39. Sharma, V., Pattnaik, S.S., Garg, T.: A review of bacterial foraging optimization and its applications. In: IJCA, pp. 9–12 (2012)

    Google Scholar 

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Acknowledgement

This work is carried out within the framework of a research grant funded by Ministry of Higher Education (MOHE) Fundamental Research Grant Scheme (Project Code: FRGS/1/2014/ICT1/TAYLOR/03/1).

Author information

Authors and Affiliations

  1. School of Biosciences, Taylor’s University, Subang Jaya, Malaysia

    Sally Chen Woon Peh & Jer Lang Hong

  2. School of Computing and IT, Taylor’s University, Subang Jaya, Malaysia

    Sally Chen Woon Peh & Jer Lang Hong

Authors
  1. Sally Chen Woon Peh
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  2. Jer Lang Hong
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Corresponding author

Correspondence to Sally Chen Woon Peh .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University, Bari, Italy

    Carmelo M. Torre

  6. Monash University, Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

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© 2016 Springer International Publishing Switzerland

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Peh, S.C.W., Hong, J.L. (2016). Bacteria Foraging Optimization for Drug Design. In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9788. Springer, Cham. https://doi.org/10.1007/978-3-319-42111-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-42111-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42110-0

  • Online ISBN: 978-3-319-42111-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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