1,721,056 research outputs found
Directed evolution of an artificial cell lineage
No full textBiological development is a complex process that mediates between genotypes, to which mutations occur, and phenotypes, on which selection acts. Properties of development can therefore have considerable impact on evolution. However, in many existing simulation models of development, the developmental process itself is difficult to recover and/or analyse. We have previously introduced a model of development in which the developmental process is represented as a cell lineage. Here we use this model to further explore the control of development, and the influence that development has on shaping an adaptive landscape
An Exploration of NK Landscapes with Neutrality
No full textRecent research into the evolution of RNA molecules has raised awareness of the neutral theory of evolution. Several fitness landscape models based upon the NK model have been proposed to investigate the behaviour of populations evolving on neutral landscapes. In this study, Kauffman's results pertaining to the NK fitness landscape model are replicated and a new visualisation technique is developed to illustrate the presence of ruggedness and neutrality across a landscape. These results are extended to the neutral NKp and NKq variants of the NK model. It is shown that these two models of neutrality result in landscapes with significant structural differences. An important goal in evolutionary computation is a greater understanding of the relationship between landscape structure and the optimal choice of evolutionary algorithms. A series of simulations is run comparing the performance of a variety of individual and population-based algorithms on a range of neutral and non-neutral landscapes. It is found that hill-climbing algorithms are generally capable of finding better individual solutions, but population-based algorithms tend to find higher average solutions. Finally, it is shown that crossover allows a population on a neutral landscape to maintain a much higher level of genetic diversity on a neutral layer than a population using mutation alone. Furthermore, populations using crossover suffer a less dramatic loss of diversity when a new fitness level is discovered, and recover diversity more rapidly
Two design patterns for visualising the parameter space of complex systems
No full textA key feature of complex systems is that their behaviour can vary significantly depending on their location in parameter space. A major challenge for researchers is to understand how combinations of system parameters influence behaviour; that is, to understand the shape of parameter space. Tools for visualising the structure and dynamics of complex systems and the shape of their parameter spaces play an important role in addressing this challenge. Many of these tools are developed to address problems in specific domains. If complex systems share certain general properties that transcend their specific domain, it should be possible to share tools for understanding these systems between domains. One technique that has been proposed for achieving this is the use of design patterns
LinMap: visualising complexity gradients in evolutionary landscapes
No full textThis paper describes an interactive visualisation tool, LinMap, for exploring the structure of complexity gradients in evolutionary landscapes. LinMap is a computationally efficient and intuitive tool for visualising and exploring multidimensional parameter spaces. An artificial cell lineage model is presented that allows complexity to be quantified according to several different developmental and phenotypic metrics. LinMap is applied to the evolutionary landscapes generated by this model to demonstrate that different definitions of complexity produce different gradients across the same landscape; that landscapes are characterised by a phase transition between proliferating and quiescent cell lineages where both complexity and diversity are maximised; and that landscapes defined by adaptive fitness and complexity can display different topographical features
Social movement recruitment and networks: a computational model (Abstract)
No full textSocial movements are groups of people who come together to act collectively in support or opposition of some political or social issue. It is widely accepted that social ties between individuals are a key avenue of recruitment for social movements. Properties of the social network, such as the number and strength of ties, and the presence of well connected individuals, are important determinants of how effectively a social movement can recruit new members, and hence its probability of success. At the same time, an individual's participation in a social movement is likely to strongly influence the set of people they come in contact with, and hence on the set of individuals with whom they may form new social ties. Thus, there is a bidirectional relationship between the short term dynamic of group formation occurring on a social network, and the longer term topological evolution of that social network. Explicitly considering the relationship between group formation and social evolution raises two interesting questions: how does social network structure influence the effectiveness of group formation, and how does group formation influence the evolution of the social network? Here, we propose a simple model of group formation and social network evolution and investigate the extent to which the recruitment process of a social movement can bring about (or hamper) the emergence of structural conditions contributing to its success
Artificial Ontogenies: A Computational Model of the Control and Evolution of Development
No full textUnderstanding 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
A comparison of neutral landscapes - NK, NKp and NKq
No full textRecent research in molecular evolution has raised awareness of the importance of selective neutrality. Several different models of neutrality have been proposed based on Kauffman’s well-known NK landscape model. Two of these models, NKp and NKq, are investigated and found to display significantly different structural properties. The fitness distributions of these neutral landscapes reveal that their levels of correlation with non-neutral landscapes are significantly different, as are the distributions of neutral mutations. In this paper we describe a series of simulations of a hill climbing search algorithm on NK, NKp and NKq landscapes with varying levels of epistatic interaction. These simulations demonstrate differences in the way that epistatic interaction affects the 'searchability' of neutral landscapes. We conclude that the method used to implement neutrality has an impact on both the structure of the resulting landscapes and on the performance of evolutionary search algorithms on these landscapes. These model-dependent effects must be taken into consideration when modelling biological phenomena
A gene regulatory network for cell differentiation in Caenorhabditis elegans
No full textBiological development is a remarkably complex process. A single cell, in an appropriate environment, contains enough information to produce a wide variety of specialised cell types, whose spatial and temporal dynamics interact to form intricately detailed patterns and behaviour. Much of the complexity of a developing system lies in the dynamics of gene regulation that occur within each cell. We used a simple recurrent network to model the process of gene regulation and evolved systems that were able to generate the first four cell divisions of the C. elegans cell lineage tree with a high degree of accuracy
Investigating ontogenetic space with developmental cell lineages
No full textDevelopment plays a significant role in biological evolution, and is likely to prove an effective route to overcoming the limitations of direct genotype-phenotype mappings in artificial evolution. Nonetheless, the relationship between development and evolution is complex and still poorly understood. One question of current interest concerns the possible role that developmental processes may play in orienting evolution. A first step towards exploring this issue from a theoretical perspective is understanding the structure of ontogenetic space: the space of possible genotype-phenotype mappings. Using a quantitative model of development that enables ontogenetic space to be characterised in terms of complexity, we show that ontogenetic landscapes have a characteristic structure that varies with genotypic properties
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