PhD thesis,

Simulation of the Evolution of Single Celled Organisms with Genome, Metabolism and Time-Varying Phenotype

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University of Technology, Sydney, Australia, (July 1999)

Abstract

A novel model of a biological cell is presented. Primary features in the cell are a genome and metabolism. Pairs of genome and metabolism coevolve with a genetic algorithm (GA) to produce cells that can survive in simple environments. Evolution of the genome is Darwinian, whereas evolution of the metabolism has Lamarckian features through acquired chemical concentrations being inherited. Fitness is more closely correlated with the mother cell than with the father. A biologically inspired double-strand genome model is presented. Double-stranded genomes admit a large increase in the number of schemata represented by each genome compared to single-strand encodings. This gives GAs more information to use and allows faster search. Simple implementation of a biologically inspired algorithm for inversion also becomes possible, as well as a compression of data on the genome. Increased rates of inversion showed an increase in population convergence. Double-stranded genomes impose constraints between strands that decrease the overall rate of population convergence. Four-bit bases from a parallel genomic language are encoded on the genome. The parallel genomic language, following the operon model of Jacob and Monod, allows genes to be placed on the genome at any loci and allows easy implementation of an inversion operator. The genome and chemical metabolism of a cell in our model have a close relationship. Genomes specify allowable families of enzyme-catalysed chemical reactions and families of chemicals that may diffuse through the cell membrane at increased rate. Chemicals produced from metabolic processes regulate genes and allow expression of proteins from the genome. We introduce the "bootstrapping" problem: evolution of cells stable in simple environments from random genomes and initial simple metabolic conditions. Experiments show that solution of the "bootstrapping" problem is much easier with coevolution than when the initial metabolic conditions remain fixed. A gallery of cellular survival strategies is given. Genes in the population are diverse because there is a variety of equally valid solutions to the problem posed by the environment. Solution to the "bootstrapping" problem is hindered because fitness functions cannot differentiate between cells using myopic solutions rather than long-term strategies. Cells with myopic strategies attain high fitness but produce offspring with high probability of cell death (ie, when the myopic solution begins to fail). A novel solution, where fitness of parents is retroactively modified when the fitness of offspring becomes known, reduces the number of cells exhibiting myopic strategies.

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