Skip to main content
SpringerLink
Log in

A Model Based Ant Colony Design for the Protein Engineering Problem

  • Conference paper
Book cover Swarm Intelligence (ANTS 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6234))

Included in the following conference series:

Abstract

In many experimental setting, we are concerned with finding the optimal experimental design, i.e. the configuration of predictive variables corresponding to an optimal value of the response. However, the high dimensionality of the search space, the vast number of variables and the economical constrains limit the ability of classical techniques to reach the optimum of a function. In this paper, we investigate the combination of statistical modeling and optimization algorithms to better explore the combinatorial search space and increase the performance of classical approaches. To this end, we propose a Model based Ant Colony Design (MACD) based on statistical modelling and Ant Colony Optimization. We apply the novel technique to a simulative case study related to Synthetic Biology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aita, T., Iwakura, M., Husimi, Y.: A cross-section of the fitness landscape of dihydrofolate reductase. Protein Eng. 14(9), 633–638 (2001)

    Article  Google Scholar 

  2. Bershtein, S., Segal, M., Bekerman, R., Tokuriki, N., Tawfik, D.S.: Robustness-epistasis link shapes the fitness landscape of a randomly drifting protein. Nature 444(7121), 929–932 (2006)

    Article  Google Scholar 

  3. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)

    Article  Google Scholar 

  4. Dorigo, M., Stützle, T.: Ant Colony Optimization. Bradford Book (2004)

    Google Scholar 

  5. Montgomery, D.C.: Design and Analysis of Experiments. John Wiley & Sons, Chichester (2006)

    Google Scholar 

  6. Stützle, T., Hoos, H.H.: \(\mathcal{MAX-MIN}\) ant system. Future Gener. Comput. Syst. 16(9), 889–914 (2000)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Statistics, University of Bologna, Bologna, Italy

    Matteo Borrotti

  2. European Centre for Living Technology, University of Venice, Venice, Italy

    Matteo Borrotti, Davide De Lucrezia, Giovanni Minervini & Irene Poli

  3. Department of Statistics, University of Venice, Venice, Italy

    Irene Poli

Authors
  1. Matteo Borrotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davide De Lucrezia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Minervini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Irene Poli
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. IRIDIA, CoDE, Université Libre de Bruxelles, Av. F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Mauro Birattari

  3. ’Dalla Molle’ Institute for Artificial Intelligence (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Complex Systems Institute, Paris-Ile-de-France, CREA, Ecole Polytechnique & CNRS, 57-59, rue Lhomond, 75005, Paris, France

    René Doursat

  5. Department of Computer Science, University of Pretoria, 0002, P.O. Box, Pretoria, South Africa

    Andries P. Engelbrecht

  6. EPFL, Laboratory of Intelligent Systems, Lausanne, Switzerland

    Dario Floreano

  7. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland

    Luca Maria Gambardella

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

    Roderich Groß

  9. KOVAN Research Lab., Department of Computer Engineering, Middle East Technical University, Inonu Bulvari, 06531, Ankara, Turkey

    Erol Şahin

  10. Department of Bioengineering, Binghamton University, State University of New York, 13902-6000, Binghamton NY, USA

    Hiroki Sayama

  11. IRIDIA, CoDE, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Thomas Stützle

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Borrotti, M., De Lucrezia, D., Minervini, G., Poli, I. (2010). A Model Based Ant Colony Design for the Protein Engineering Problem. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2010. Lecture Notes in Computer Science, vol 6234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15461-4_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-15461-4_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15460-7

  • Online ISBN: 978-3-642-15461-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation