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Evolving Parameters for a Noisy Biological System – The Impact of Alternative Approaches

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Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8468))

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Abstract

In this contribution we seek to evolve viable parameter values for a small-scale biological network motif concerned with bacterial nutrient uptake and metabolism. We use two different evolutionary approaches with the model: implicit and explicit. Our results reveal that significantly different characteristics of both efficiency and timescale emerge in the resulting evolved systems depending on the which particular approach is used.

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References

  1. Alon, U.: Network motifs: theory and experimental approaches. Nature Review Genetics 8(6), 450–461 (2007)

    Article  Google Scholar 

  2. Paulsson, J.: Summing up the noise in gene networks. Nature 427(6973), 415–418 (2004)

    Article  Google Scholar 

  3. Chu, D.: Replaying the tape of evolution: Evolving parameters for a simple bacterial metabolism. In: IEEE Congress on Evolutionary Computation (CEC), pp. 213–220 (2013)

    Google Scholar 

  4. Chu, D.: Evolving parameters for a noisy bio-systems. In: 2013 IEEE Symposion series on Computational Intelligence (2013)

    Google Scholar 

  5. Chu, D., Zabet, N., Mitavskiy, B.: Models of transcription factor binding: Sensitivity of activation functions to model assumptions. Journal of Theoretical Biology 257(3), 419–429 (2009)

    Article  Google Scholar 

  6. Chu, D., Zabet, N., Hone, A.: Optimal parameter settings for information processing in gene regulatory networks. BioSystems 104, 99–108 (2011)

    Article  Google Scholar 

  7. Gillespie, D.: Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  8. Barnes, D., Chu, D.: Introduction to Modelling for Biosciences. Springer, Berlin (2010)

    Book  Google Scholar 

  9. Gibson, M., Bruck, J.: Efficient exact stochastic simulation of chemical systems with many species and many channels. Journal of Physical Chemistry 104, 1876–1889 (2000)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computing, The University of Kent, Canterbury, Kent, CT2 7NF, UK

    David J. Barnes & Dominique Chu

Authors
  1. David J. Barnes
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  2. Dominique Chu
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski  & Rafał Scherer  & 

  2. Częstochowa University of Technology, 42-200, Częstochowa, Poland

    Marcin Korytkowski

  3. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2014 Springer International Publishing Switzerland

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Barnes, D.J., Chu, D. (2014). Evolving Parameters for a Noisy Biological System – The Impact of Alternative Approaches. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-07176-3_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07175-6

  • Online ISBN: 978-3-319-07176-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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