Individual-based modelling of bacterial ecologies and evolution: Conference Reviews

  • Authors:

  • C. Vlachos;R. Gregory;R. C. Paton;J. R. Saunders;Q. H. Wu

  • Affiliations:

  • BioComputing and Computational Biology Research Group, Dept of Computer Science, Univ of Liverpool, Chadwick Building, Peach Street, Liverpool, UK;BioComputing and Computational Biology Research Group, Dept of Computer Science, Univ of Liverpool and Dept. of Elec. Eng. and Elec., Faculty of Eng., Univ. of Liverpool, Brownlow Hill, Liverpool, ...;BioComputing and Computational Biology Research Grp., Dept. of Comp. Sci., Univ. of Liverpool, Chadwick Building, Peach Street, Liverpool L69 7ZF, UK;Microbiology and Genomics Division, School of Biological Sciences, University of Liverpool, Biosciences Building, Liverpool L69 7ZB, UK;Department of Electrical Engineering and Electronics, Faculty of Engineering, University of Liverpool, Brownlow Hill, Liverpool L69 3GJ, UK

  • Venue:
  • Comparative and Functional Genomics
  • Year:
  • 2004

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Abstract

This paper presents two approaches to the individual-based modelling of bacterial ecologies and evolution using computational tools. The first approach is a fine-grained model that is based on networks of interactivity between computational objects representing genes and proteins. The second approach is a coarser-grained, agent-based model, which is designed to explore the evolvability of adaptive behavioural strategies in artificial bacteria represented by learning classifier systems. The structure and implementation of these computational models is discussed, and some results from simulation experiments are presented. Finally, the potential applications of the proposed models to the solution of real-world computational problems, and their use in improving our understanding of the mechanisms of evolution, are briefly outlined. Copyright © 2004 John Wiley & Sons, Ltd.