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- W1510238714 abstract "Computational modeling provides the means to integrate and test biological theories which span different temporal and spatial scales. This thesis computationally explores the evolutionary relationship between inherited genetic information, organism behaviour, and environment. Recent advances in molecular biology have led to an explosion of data and theoretical revision. The sequencing of genomes has led to increased appreciation of the complexity of the genetic system. The notion of genes assigned to phenotypic traits is being replaced with the understanding that complex networks of gene regulation are the driving force of ontogeny, and that the large-scale dynamics of these networks may confer properties such as robustness to the organism. The genetic whole has become greater than the sum of its parts. The mapping from genotypes to phenotypes is only a part of the evolutionary story. While it is generally accepted that multicellular phenotypes do not directly modify the inherited information they pass to their offspring, there are processes such as the Baldwin effect that act over evolutionary time, and through which individual behaviours can alter the genetic composition of populations. Such relationships between environment and populations, and genotype and phenotype, must be accounted for in evolutionary models. This thesis seeks to address the growing need for models which encompass levels from the genotype to the population from two approaches: 1. Investigating the interactions and information flow from the genotype to the phenotype through a computational model of gene extraction and regulation, and 2. Investigating the impact of phenotype and population behaviours on the genotype through a computational model of genetic assimilation. In the first set of studies, the impact of sequence-level modifications were analyzed at the network level. These studies determined that the types of evolutionary operators commonly used in network-level models of gene regulation can be expanded, and their relative frequencies justified. In addition, the structure of the regulatory networks after cumulative sequence duplications were found to be distinct from networks with cumulative random node and link changes. A model linking this genetic system with a developmental plant phenotype was implemented to further explore ways of appropriately modeling and linking genotype and phenotype. Phenotypic development was based on the structure of the gene regulatory network. This model was revised to study the effects of competing selective pressures, with the development of the artificial phenotype controlled by the dynamics of the regulatory network. Evolutionary simulations of this sequence – network - plant model successfully resulted in phenotypes adapted to the competing pressures of light interception, reproductive success and surface area. The second set of studies investigated the conditions under which learnt behaviour could be assimilated into inherited information. Extending previous studies of the Baldwin effect, the conditions necessary for this assimilation to occur were analysed with neural network phenotypes operating in both simple and tunably complex environments. It was found that genetic assimilation will occur when (i) conditions are poised such that a small fraction of the population is able to acquire a task through learning, (ii) the task confers sufficient selective benefits, and (iii) the act of learning incurs a cost. By computationally exploring the two key aspects of the genotype / phenotype mapping – how genotypes control phenotypes and how phenotypes influence genotypes – this thesis provides a methodological scaffold upon which increasingly complex models at each of the levels of genotype, phenotype and environment can be built." @default.
- W1510238714 created "2016-06-24" @default.
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- W1510238714 date "2006-01-01" @default.
- W1510238714 modified "2023-09-27" @default.
- W1510238714 title "From genotypes to phenotypes and back again: modeling the interaction between individual behaviour and evolution" @default.
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