Rules of engagement : competitive coevolutionary dynamics in computational systems

Given that evolutionary biologists have considered coevolutionary interactions since the dawn of Darwinism, it is perhaps surprising that coevolution was largely overlooked during the formative years of evolutionary computing. It was not until the early 1990s that Hillis' seminal work thrust coevolution into the spotlight. Upon attempting to evolve fixed-length sorting networks, a problem with a long and competitive history, Hillis found that his standard evolutionary algorithm was producing sub-standard networks. In response, he decided to reciprocally evolve a population of testlists against the sorting network population; thus producing a coevolutionary system. The result was impressive; coevolution not only outperformed evolution, but the best network it discovered was only one comparison longer than the best-known solution. For the first time, a coevolutionary algorithm had been successfully applied to problem-solving. Pre-Hillis, the shortcomings of standard evolutionary algorithms had been understood for some time: whilst defining an adequate fitness function can be as challenging as the problem one is hoping to solve, once achieved, the accumulation of fitness-improving mutations can push a population towards local optima that are difficult to escape. Coevolution offers a solution. By allowing the fitness of each evolving individual to vary (through competition) with other reciprocally evolving individuals, coevolution removes the requirement of a fitness yardstick. In conjunction, the reciprocal adaptations of each individual begin to erode local optima as soon as they appear. However, coevolution is no panacea. As a problem-solving tool, coevolutionary algorithms suffer from some debilitating dynamics, each a result of the relative fitness assessment of individuals. In a single-, or multi-, population competitive system, coevolution may stabilize at a suboptimal equilibrium, or mediocre stable state; analogous to the traditional problem of local optima. Populations may become highly specialized in an unanticipated (and undesirable) manner; potentially resulting in brittle solutions that are fragile to perturbation. The system may cycle; producing dynamics similar to the children's game rock-paper-scissors. Disengagement may occur, whereby one population out-performs another to the extent that individuals cannot be discriminated on the basis of fitness alone; thus removing selection pressure and allowing populations to drift. Finally, coevolution's relative fitness assessment renders traditional visualization techniques (such as the graph of fitness over time) obsolete; thus exacerbating each of the above problems. This thesis attempts to better understand and address the problems of coevolution through the design and analysis of simple coevolutionary models. 'Reduced virulence' - a novel technique specifically designed to tackle disengagement - is developed. Empirical results demonstrate the ability of reduced virulence to combat disengagement both in simple and complex domains, whilst outperforming the only known competitors. Combining reduced virulence with diversity maintenance techniques is also shown to counteract mediocre stability and over-specialization. A critique of the CIAO plot - a visualization technique developed to detect coevolutionary cycling - highlights previously undocumented ambiguities; experimental evidence demonstrates the need for complementary visualizations. Extending the scope of visualization, a first exploration into coevolutionary steering is performed; a technique allowing the user to interact with a coevolutionary system during run-time. Using a simple model incorporating reduced virulence, the coevolutionary steering demonstration highlights the future potential of such tools for both research and education. The role of neutrality in coevolution is discussed in detail. Whilst much emphasis is placed upon neutral networks in the evolutionary computation literature, the nature of coevolutionary neutrality is generally overlooked. Preliminary ideas for modelling coevolutionary neutrality are presented. Finally, whilst this thesis is primarily aimed at a computing audience, strong reference to evolutionary biology is made throughout. Exemplifying potential crossover, the CIAO plot, a tool previously unused in biology, is applied to a simulation of E. Coli, with results con rming empirical observations of real bacteria.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Protein multi-scale organization through graph partitioning and robustness analysis: Application to the myosin-myosin light chain interaction

Despite the recognized importance of the multi-scale spatio-temporal organization of proteins, most computational tools can only access a limited spectrum of time and spatial scales, thereby ignoring the effects on protein behavior of the intricate coupling between the different scales. Starting from a physico-chemical atomistic network of interactions that encodes the structure of the protein, we introduce a methodology based on multi-scale graph partitioning that can uncover partitions and levels of organization of proteins that span the whole range of scales, revealing biological features occurring at different levels of organization and tracking their effect across scales. Additionally, we introduce a measure of robustness to quantify the relevance of the partitions through the generation of biochemically-motivated surrogate random graph models. We apply the method to four distinct conformations of myosin tail interacting protein, a protein from the molecular motor of the malaria parasite, and study properties that have been experimentally addressed such as the closing mechanism, the presence of conserved clusters, and the identification through computational mutational analysis of key residues for binding.Comment: 13 pages, 7 Postscript figure

Searching for improvement

Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents

Linking spatial distribution of Rhipicephalus appendiculatus to climatic variables important for the successful biocontrol by Metarhizium anisopliae in Eastern Africa

Cattle production is constantly threatened by diseases like East Coast fever, also known as theileriosis, caused by the protozoan parasite Theileria parva which is transmitted by ticks such as the brown ear tick, Rhipicephalus appendiculatus. To reduce the extensive use of chemical acaricides, fungal-based microbial control agents such as Metarhizium anisopliae have been tested and show promising results against R. appendiculatus both in field and in semi-field experiments in Africa. However, no known endeavors to link the spatial distribution of R. appendiculatus to climatic variables important for the successful application of M. anisopliae in selected East African countries exists. This work therefore aims to improve the successful application of M. anisopliae against R. appendiculatus by designing a temperature-dependent model for the efficacy of M. anisopliae against three developmental stages (larvae, nymphs, adults) of R. appendiculatus. Afterward a spatial prediction of potential areas where this entomopathogenic fungus might cause a significant epizootic in R. appendiculatus population in three selected countries (Kenya, Tanzania, Uganda) in Eastern Africa were generated. This can help to determine whether the temperature and rainfall at a local or regional scale might give good conditions for application of M. anisopliae and successful microbial control of R. appendiculatus.publishedVersio

Dermanyssus gallinae: the long journey of the poultry red mite to become a vector

: The possibility that Dermanyssus gallinae, the poultry red mite, could act as a vector of infectious disease-causing pathogens has always intrigued researchers and worried commercial chicken farmers, as has its ubiquitous distribution. For decades, studies have been carried out which suggest that there is an association between a wide range of pathogens and D. gallinae, with the transmission of some of these pathogens mediated by D. gallinae as vector. The latter include the avian pathogenic Escherichia coli (APEC), Salmonella enterica serovars Enteritidis and Gallinarum and influenza virus. Several approaches have been adopted to investigate the relationship between D. gallinae and pathogens. In this comprehensive review, we critically describe available strategies and methods currently available for conducting trials, as well as outcomes, analyzing their possible strengths and weaknesses, with the aim to provide researchers with useful tools for correctly approach the study of the vectorial role of D. gallinae

Investigating The Dynamics of Hepatic Inflammation Through Simulation

Inflammation is a fundamental mechanism for the body to induce repair and healing in tissues, and exacerbated inflammatory responses are associated with a wide variety of diseases and disorders. Categorising the various cells, proteins, and precise mechanisms involved in initiating and driving inflammation poses significant challenges, due to the complex interplay that occurs between them. In this thesis, I will introduce a deadly parasitic disease called Visceral Leishmaniasis (VL) as a case study in using computational modelling techniques to elucidate the mechanisms underpinning inflammation. During VL infection, inflammatory aggregations of immune system cells form, these are called granulomas. Granulomas function to contain and subsequently remove infection. Whilst immunological studies have provided insights into the structure and function of granulomas, there remains a breadth of questions which laboratory techniques are currently incapable of answering. As such, the challenges facing biologists from a scientific perspective will be addressed, I will then argue after a thorough review of the relevant literature, that agent-based computational modelling is a logical choice for research into granuloma formation, and that such models can help answer some outstanding questions in the field. The thesis presents the process of designing and developing the first spatially resolved model of liver localised granuloma formation during VL. The development and use of modelling and simulation to study granulomas has involved close collaboration with immunologists at all stages through conceptualisation, modelling, implementation, and also results interpretation. I describe the use of established statistical techniques to instill confidence in both the model, and the results it can produce through simulation. Through iterative hypothesis generation and testing, the research undertaken has allowed for several predictions to be made, some of which have biological significance and which were later validated experimentally. Specifically, transcriptomic data analysis revealed that both infected and uninfected Kupffer cells are equally capable of responding to infection in a similar manner, something which wasn't previously evident in the literature. Using this transcriptomic data, I investigated through simulation, several experimental scenarios and elucidated a novel mechanism of immune system regulation in the liver microenvironment. Using an experimental model of Leishmania donovani infection, I demonstrated that such an immune regulatory mechanism can be overcome with the expansion of early promoter cells called Natural Killer T cells

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

Forest Pathology and Plant Health

Every year, a number of new forest pathosystems are discovered as the result of introduction of alien pathogens, host shifts and jumps, hybridization and recombination among pathogens, etc. Disease outbreaks may also be favored by climate change and forest management. The mechanisms driving the resurgence of native pathogens and the invasion of alien ones need to be better understood in order to draft sustainable control strategies. For this Special Issue, we welcome population biology studies providing insights on the epidemiology and invasiveness of emergent forest pathogens possibly by contrasting different scenarios varying in pathogen and host populations size, genetics, phenotype and phenology, landscape fragmentation, occurrence of disturbances, management practices, etc. Both experimental and monitoring approaches are welcome. In summary, this special issue focuses on how variability in hosts, pathogens, or ecology may affect the emergence of new threats to plant species