<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>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.</p>
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Comp Funct Genomics. 2004 Feb; 5(1):100-104