Artificial Ontogenies: A Computational Model of the Control and Evolution of Development

Geard, Nicholas Lewis (2006). Artificial Ontogenies: A Computational Model of the Control and Evolution of Development PhD Thesis, School of Information Technology and Electrical Engineering , University of Queensland.

       
Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
n01front_Geard.pdf n01front_Geard.pdf application/pdf 118.43KB 3
n02content_Geard.pdf n02content_Geard.pdf application/pdf 6.52MB 3
Author Geard, Nicholas Lewis
Thesis Title Artificial Ontogenies: A Computational Model of the Control and Evolution of Development
School, Centre or Institute School of Information Technology and Electrical Engineering
Institution University of Queensland
Publication date 2006
Thesis type PhD Thesis
Supervisor Associate Professor Janet Wiles
Abstract/Summary Understanding the behaviour of biological systems is a challenging task. Gene regulation, development and evolution are each a product of nonlinear interactions between many individual agents: genes, cells or organisms. Moreover, these three processes are not isolated, but interact with one another in an important fashion. The development of an organism involves complex patterns of dynamic behaviour at the genetic level. The gene networks that produce this behaviour are subject to mutations that can alter the course of development, resulting in the production of novel morphologies. Evolution occurs when these novel morphologies are favoured by natural selection and survive to pass on their genes to future generations. Computational models can assist us to understand biological systems by providing a framework within which their behaviour can be explored. Many natural processes, including gene regulation and development, have a computational element to their control. Constructing formal models of these systems enables their behaviour to be simulated, observed and quantified on a scale not otherwise feasible. This thesis uses a computational simulation methodology to explore the relationship between development and evolution. An important question in evolutionary biology is how to explain the direction of evolution. Conventional explanations of evolutionary history have focused on the role of natural selection in orienting evolution. More recently, it has been argued that the nature of development, and the way it changes in response to mutation, may also be a significant factor. A network-lineage model of artificial ontogenies is described that incorporates a developmental mapping between the dynamics of a gene network and a cell lineage representation of a phenotype. Three series of simulation studies are reported, exploring: (a) the relationship between the structure of a gene network and its dynamic behaviour; (b) the characteristic distributions of ontogenies and phenotypes generated by the dynamics of gene networks; (c) the effect of these characteristic distributions on the evolution of ontogeny. The results of these studies indicate that the model networks are capable of generating a diverse range of stable behaviours, and possess a small yet significant sensitivity to perturbation. In the context of developmental control, the intrinsic dynamics of the model networks predispose the production of ontogenies with a modular, quasi-systematic structure. This predisposition is reflected in the structure of variation available for selection in an adaptive search process, resulting in the evolution of ontogenies biased towards simplicity. These results suggest a possible explanation for the levels of ontogenetic complexity observed in biological organisms: that they may be a product of the network architecture of developmental control. By quantifying complexity, variation and bias, the network-lineage model described in this thesis provides a computational method for investigating the effects of development on the direction of evolution. In doing so, it establishes a viable framework for simulating computational aspects of complex biological systems.

 
Citation counts: Google Scholar Search Google Scholar
Access Statistics: 126 Abstract Views, 6 File Downloads  -  Detailed Statistics
Created: Fri, 21 Nov 2008, 14:52:36 EST