Computing Bacterial Evolvability Using Individual-Based Models
Richard Gergory (University of Liverpool, UK), Richard Vlachos (University of Liverpool, UK), Ray C. Paton (University of Liverpool, UK), John W. Palmer (University of Liverpool, UK), Q. H. Wu (University of Liverpool, UK) and Jon R. Saunders (University of Liverpool, UK)
Copyright: © 2005
This chapter describes two approaches to individual-based modelling that are based on bacterial evolution and bacterial ecologies. Some history of the individual-based modelling approach is presented and contrasted to traditional methods. Two related models of bacterial evolution are then discussed in some detail. The first model consists of populations of bacterial cells, each bacterial cell containing a genome and many gene products derived from the genome. The genomes themselves are slowly mutated over time. As a result, this model contains multiple time scales and is very fine-grained. The second model employs a coarser-grained, agent-based architecture designed to explore the evolvability of adaptive behavioural strategies in artificial bacterial ecologies. The organisms in this approach are represented by mutating learning classifier systems. Finally, the subject of computability on parallel machines and clusters is applied to these models, with the aim of making them efficiently scalable to the point of being biologically realistic by containing sufficient numbers of complex individuals.