A model based ant colony design for the protein engineering problem

  • Authors:

  • Matteo Borrotti;Davide De Lucrezia;Giovanni Minervini;Irene Poli

  • Affiliations:

  • Department of Statistics, University of Bologna, Bologna, Italy, European Centre for Living Technology, University of Venice, Venice, Italy;European Centre for Living Technology, University of Venice, Venice, Italy;European Centre for Living Technology, University of Venice, Venice, Italy;European Centre for Living Technology, University of Venice, Venice, Italy, Department of Statistics, University of Venice, Venice, Italy

  • Venue:
  • ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
  • Year:
  • 2010

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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.