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