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International Conference on Evolutionary Multi-Criterion Optimization

EMO 2013: Evolutionary Multi-Criterion Optimization pp 696–709Cite as

Single and Multi-objective in Silico Evolution of Tunable Genetic Oscillators

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7811))

Abstract

We compare the ability of single and multi-objective evolutionary algorithms to evolve tunable self-sustained genetic oscillators. Our research is focused on the influence of objective setup on the success rate of evolving self-sustained oscillations and the tunability of the evolved oscillators. We compare temporal and frequency domain fitness functions for single and multi-objective evolution of the parameters in a three-gene genetic regulatory network. We observe that multiobjectivization can hinder convergence when decomposing a period specific based single objective setup in to a multi-objective setup that includes a frequency specific objective. We also find that the objective decomposition from a frequency specified single objective setup to a multi-objective setup, which also specifies period, enable the synthesis of oscillatory dynamics. However this does not help to enhance tunability. We reveal that the use of a helper function in the frequency domain improves the tunability of the oscillators, compared to a time domain based single objective, even if no desired frequency is specified.

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References

  1. Alon, U.: Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8, 450–461 (2007)

    Article  Google Scholar 

  2. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: Simple building blocks of complex networks. Science 298(5594), 824–827 (2002), http://www.sciencemag.org/content/298/5594/824.abstract

    Article  Google Scholar 

  3. Jin, Y., Sendhoff, B.: Evolving in silico bistable and oscillatory dynamics for gene regulatory network motifs. In: IEEE World Congress on Computational Intelligence Evolutionary Computation, CEC 2008, pp. 386–391 (June 2008)

    Google Scholar 

  4. Gonze, D.: Coupling oscillations and switches in genetic networks. Biosystems 99(1), 60–69 (2010), http://www.sciencedirect.com/science/article/pii/S030326470900149X

    Article  Google Scholar 

  5. Goldbeter, A.: Biochemical Oscillations and Cellular Rhythms, vol. 1. Cambridge University Press (April 1997), http://dx.doi.org/10.1017/CB09780511608193

  6. Chay, T.R.: A model for biological oscillations. Proceedings of the National Academy of Sciences of the United States of America 78, 2204–2207 (1981), http://ukpmc.ac.uk/abstract/MED/6264468

  7. Francis, M.R., Fertig, E.J.: Quantifying the dynamics of coupled networks of switches and oscillators. PLoS ONE 7(1), e29497 (2012), http://dx.doi.org/10.1371%2Fjournal.pone.0029497

  8. Rempe, M., Best, J., Terman, D.: English A mathematical model of the sleep/wake cycle. Journal of Mathematical Biology 60, 615–644 (2010), http://dx.doi.org/10.1007/s00285-009-0276-5

    Article  MathSciNet  MATH  Google Scholar 

  9. Ennes, R.H., McGuire, G.C.: Nonlinear Physics with Mathematica for Scientists and Engineers. Birkhäuser, Boston (2001)

    Book  Google Scholar 

  10. Fall, C.P., Marland, E.S., Wagner, J.M., Tyson, J.J.: Compuatational Cell Biology. In: Antman, S.S., Marsden, J.E., Sirovich, L., Wiggins, S. (eds.), vol. 20. Springer (July 2002)

    Google Scholar 

  11. Berridge, M., Rapp, P.: A comparative survey of the function, mechanism and control of cellular oscillators. The Journal of Experimental Biology 81, 217–279 (1979), http://europepmc.org/abstract/MED/390080

    Google Scholar 

  12. Jin, Y., Meng, Y., Sendhoff, B.: Influence of regulation logic on the easiness of evolving sustained oscillation for gene regulatory networks. In: IEEE Symposium on Artificial Life, ALife 2009, pp. 61–68 (April 2009)

    Google Scholar 

  13. Tsai, T.Y.-C., Choi, Y.S., Ma, W., Pomerening, J.R., Tang, C., Ferrell, J.E.: Robust, tunable biological oscillations from interlinked positive and negative feedback loops. Science 321(5885), 126–129 (2008), http://www.sciencemag.org/content/321/5885/126.abstract

    Article  Google Scholar 

  14. Alon, U.: An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC (2006)

    Google Scholar 

  15. Ito, S., Izumi, N., Hagihara, S., Yonezaki, N.: Qualitative analysis of gene regulatory networks by satisfiability checking of linear temporal logic. In: Proceedings of the 2010 IEEE International Conference on Bioinformatics and Bioengineering, ser. BIBE 2010, pp. 232–237. IEEE Computer Society, Washington, DC (2010), http://dx.doi.org/10.1109/BIBE.2010.45

    Chapter  Google Scholar 

  16. Chu, D.: Evolving genetic regulatory networks for systems biology. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 875–882 (September 2007)

    Google Scholar 

  17. Jin, Y., Meng, Y.: Emergence of robust regulatory motifs from in silico evolution of sustained oscillation. Biosystems 103(1), 38–44 (2011), http://www.sciencedirect.com/science/article/pii/S0303264710001693

    Article  MathSciNet  Google Scholar 

  18. Sirbu, A., Ruskin, H.J., Crane, M.: Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics 11, 59 (2010)

    Article  Google Scholar 

  19. Thomas, S.A., Jin, Y.: Combining genetic oscillators and switches using evolutionary algorithms. In: 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 28–34 (May 2012)

    Google Scholar 

  20. Handl, J., Kell, D.B., Knowles, J.: Multiobjective optimization in bioinformatics and computational biology. IEEE/ACM Trans. Comput. Biol. Bioinformatics 4(2), 279–292 (2007), http://dx.doi.org/10.1109/TCBB.2007.070203

    Article  Google Scholar 

  21. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)

    Article  Google Scholar 

  22. Handl, J., Lovell, S.C., Knowles, J.D.: Investigations into the Effect of Multiobjectivization in Protein Structure Prediction. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN X. LNCS, vol. 5199, pp. 702–711. Springer, Heidelberg (2008), http://dx.doi.org/10.1007/978-3-540-87700-4_70

    Chapter  Google Scholar 

  23. Silva-Rocha, R., de Lorenzo, V.: Noise and robustness in prokaryotic regulatory networks. Annual Review of Microbiology 64(1), 257–275 (2010), http://www.annualreviews.org/doi/abs/10.1146/annurev.micro.091208.073229

    Article  Google Scholar 

  24. Frigo, M., Johnson, S.G.: The design and implementation of FFTW3. Proceedings of the IEEE 93(2), 216–231 (2005); Special issue on “Program Generation, Optimization, and Platform Adaptation

    Article  Google Scholar 

  25. Brockhoff, D., Friedrich, T., Hebbinghaus, N., Klein, C., Neumann, F., Zitzler, E.: Do additional objectives make a problem harder?”. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, ser. GECCO 2007, pp. 765–772. ACM, New York (2007), http://doi.acm.org/10.1145/1276958.1277114

    Chapter  Google Scholar 

  26. Knowles, J.D., Watson, R.A., Corne, D.W.: Reducing Local Optima in Single-Objective Problems by Multi-objectivization. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 269–283. Springer, Heidelberg (2001), http://dl.acm.org/citation.cfm?id=647889.736521

    Chapter  Google Scholar 

  27. Jensen, M.T.: Helper-objectives: Using multi-objective evolutionary algorithms for single-objective optimisation. Journal of Mathematical Modelling and Algorithms 3, 323–347 (2004), http://dx.doi.org/10.1023/B:JMMA.0000049378.57591.c6 , doi:10.1023/B:JMMA.0000049378.57591.c6

    Article  MathSciNet  MATH  Google Scholar 

  28. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)

    MATH  Google Scholar 

  29. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Systems 9, 115–148 (1995)

    MathSciNet  MATH  Google Scholar 

  30. Mendoza, M.R., Bazzan, A.L.C.: Evolving random boolean networks with genetic algorithms for regulatory networks reconstruction. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, ser. GECCO 2011, pp. 291–298. ACM, New York (2011), http://doi.acm.org/10.1145/2001576.2001617

    Google Scholar 

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Authors and Affiliations

  1. Department of Computing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Spencer Angus Thomas & Yaochu Jin

Authors
  1. Spencer Angus Thomas
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  2. Yaochu Jin
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Editor information

Editors and Affiliations

  1. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Robin C. Purshouse

  2. Department of Automatic Control Systems and Engineering, University of Sheffield, Mappin Street, S1 3JD, Sheffield, UK

    Peter J. Fleming

  3. Department of Informatics Engineering, University of Coimbra, Pólo II, Pinhal de Marrocos, 3030-290, Coimbra, Portugal

    Carlos M. Fonseca

  4. Department of Economics and Business, University of Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Greco

  5. Unilever Research & Development Port Sunlight, Quarry Road East, CH63 3JW, Merseyside, UK

    Jane Shaw

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© 2013 Springer-Verlag Berlin Heidelberg

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Thomas, S.A., Jin, Y. (2013). Single and Multi-objective in Silico Evolution of Tunable Genetic Oscillators. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_52

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  • DOI: https://doi.org/10.1007/978-3-642-37140-0_52

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37139-4

  • Online ISBN: 978-3-642-37140-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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