| dc.description.abstract | Technology, nowadays, has given us huge computational potential, but
computer sciences have major problems tapping into this pool of resources.
One of the main issues is how to program and design distributed systems.
Biology has solved this issue about half a billion years ago, during the
Cambrian explosion: the evolution of multicellularity. The evolution of multicellularity
allowed cells to differentiate and so divide different tasks to different
groups of cells; this combined with evolution gives us a very good example of
how massively parallel distributed computational system can function and be
“programmed”.
However, the evolution of multicellularity is not very well understood,
and most traditional methodologies used in evolutionary theory are not apt to
address and model the whole transition to multicellularity.
In this thesis I develop and argue for new computational artificial life
methodologies for the study of the evolution of multicellularity that are able
to address the whole transition, give new insights, and complement existing
methods. I argue that these methodologies should have three main characteristics:
accessible across scientific disciplines, have potentiality for complex
behaviour, and be easy to analyse.
To design models, which possess those characteristics, I developed a
model of genetic regulatory networks (GRNs) that control artificial cells, which
I have used in multiple evolutionary experiments. The first experiment was
designed to present some of the engineering problems of evolving multicelled
systems (applied to graph-colouring), and to perfect my artificial cell model.
The two subsequent experiments demonstrate the characteristics listed above:
one model based on a genetic algorithm with an explicit two-level fitness function
to evolve multicelled cooperative patterning, and one with freely evolving artificial cells that have evolved some multicelled cooperation as evidenced by
novel measures, and has the potential to evolve multicellularity. These experiments
show how artificial life models of evolution can discover and investigate
new hypotheses and behaviours that traditional methods cannot. | en_US |
