A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.

Genetic variation and evolutionary divergence within and among populations, species, and genera of the Cambarinae

Seven crayfish species from three genera of the subfamily Cambarinae were electrophoretically examined for genetic variation at a total of twenty-six loci. Polymorphism was detected primarily at three loci: Ao-2, Lap, and Pgi. The average heterozygosities over-all loci for each species were found to be very low when compared to most other invertebrate species that have been examined electrophoretically. With the exception of Cambarus bartoni, the interpopulation genetic identities are high within any given species. The average interspecific identities are somewhat lower and the average intergeneric identities are lower still. Populations, species and genera conform to the expected taxonomic progression. The two samples of ~ bartoni show high genetic similarity at only 50 percent of the loci compared. Locus by locus identity comparisons among species yield U-shaped distributions of genetic identities. Construction of a phylogenetic dendrogram using species mean genetic distances values shows that species grouping is in agreement with morphological taxonomy with the exception of the high similarity between Orconectespropinquus and Procambarus pictus. This high similarity suggests the possibility of a regulatory change between the two species. It appears that the low heterozygosities, high interpopulation genetic identities, and taxonomic mispositioning can all be explained on the basis of low mutation rates.

Evolutionary synthesis of stochastic gene network models using feature-based search spaces

A feature-based fitness function is applied in a genetic programming system to synthesize stochastic gene regulatory network models whose behaviour is defined by a time course of protein expression levels. Typically, when targeting time series data, the fitness function is based on a sum-of-errors involving the values of the fluctuating signal. While this approach is successful in many instances, its performance can deteriorate in the presence of noise. This thesis explores a fitness measure determined from a set of statistical features characterizing the time series' sequence of values, rather than the actual values themselves. Through a series of experiments involving symbolic regression with added noise and gene regulatory network models based on the stochastic 'if-calculus, it is shown to successfully target oscillating and non-oscillating signals. This practical and versatile fitness function offers an alternate approach, worthy of consideration for use in algorithms that evaluate noisy or stochastic behaviour.

The Salmon Algorithm - A New Population Based Search Metaheuristic

This thesis introduces the Salmon Algorithm, a search meta-heuristic which can be used for a variety of combinatorial optimization problems. This algorithm is loosely based on the path finding behaviour of salmon swimming upstream to spawn. There are a number of tunable parameters in the algorithm, so experiments were conducted to find the optimum parameter settings for different search spaces. The algorithm was tested on one instance of the Traveling Salesman Problem and found to have superior performance to an Ant Colony Algorithm and a Genetic Algorithm. It was then tested on three coding theory problems - optimal edit codes, optimal Hamming distance codes, and optimal covering codes. The algorithm produced improvements on the best known values for five of six of the test cases using edit codes. It matched the best known results on four out of seven of the Hamming codes as well as three out of three of the covering codes. The results suggest the Salmon Algorithm is competitive with established guided random search techniques, and may be superior in some search spaces.

Evolutionary Origin and Maintenance of Sociality in the Small Carpenter Bees

Many arthropods exhibit behaviours precursory to social life, including adult longevity, parental care, nest loyalty and mutual tolerance, yet there are few examples of social behaviour in this phylum. The small carpenter bees, genus Ceratina, provide important insights into the early stages of sociality. I described the biology and social behaviour of five facultatively social species which exhibit all of the preadaptations for successful group living, yet present ecological and behavioural characteristics that seemingly disfavour frequent colony formation. These species are socially polymorphic with both / solitary and social nests collected in sympatry. Social colonies consist of two adult females, one contributing both foraging and reproductive effort and the second which remains at the nest as a passive guard. Cooperative nesting provides no overt reproductive benefits over solitary nesting, although brood survival tends to be greater in social colonies. Three main theories explain cooperation among conspecifics: mutual benefit, kin selection and manipulation. Lifetime reproductive success calculations revealed that mutual benefit does not explain social behaviour in this group as social colonies have lower per capita life time reproductive success than solitary nests. Genetic pedigrees constructed from allozyme data indicate that kin selection might contribute to the maintenance of social nesting -, as social colonies consist of full sisters and thus some indirect fitness benefits are inherently bestowed on subordinate females as a result of remaining to help their dominant sister. These data suggest that the origin of sociality in ceratinines has principal costs and the great ecological success of highly eusociallineages occurred well after social origins. Ecological constraints such as resource limitation, unfavourable weather conditions and parasite pressure have long been considered some of the most important selective pressures for the evolution of sociality. I assessed the fitness consequences of these three ecological factors for reproductive success of solitary and social colonies and found that nest sites were not limiting, and the frequency of social nesting was consistent across brood rearing seasons. Local weather varied between seasons but was not correlated with reproductive success. Severe parasitism resulted in low reproductive success and total nest failure in solitary nests. Social colonies had higher reproductive success and were never extirpated by parasites. I suggest that social nesting represents a form of bet-hedging. The high frequency of solitary nests suggests that this is the optimal strategy when parasite pressure is low. However, social colonies have a selective advantage over solitary nesting females during periods of extreme parasite pressure. Finally, the small carpenter bees are recorded from all continents except Antarctica. I constructed the first molecular phylogeny of ceratinine bees based on four gene regions of selected species covering representatives from all continents and ecological regions. Maximum parsimony and Bayesian Inference tree topology and fossil dating support an African origin followed by an Old World invasion and New World radiation. All known Old World ceratinines form social colonies while New World species are largely solitary; thus geography and phylogenetic inertia are likely predictors of social evolution in this genus. This integrative approach not only describes the behaviour of several previously unknown or little-known Ceratina species, bu~ highlights the fact that this is an important, though previously unrecognized, model for studying evolutionary transitions from solitary to social behaviour.

Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

Plant rhizosphere specificity and variability in the insect and plant adhesins, Madl and Mad2, within the genus Metarhizium suggest plant adaptation as an evolutionary force

Metarhizium is a soil-inhabiting fungus currently used as a biological control agent against various insect species, and research efforts are typically focused on its ability to kill insects. In section 1, we tested the hypothesis that species of Metarhizium are not randomly distributed in soils but show plant rhizosphere-specific associations. Results indicated an association of three Metarhizium species (Metarhizium robertsii, M. brunneum and M. guizhouense) with the rhizosphere of certain types of plant species. M. robertsii was the only species that was found associated with grass roots, suggesting a possible exclusion of M. brunneum and M. guizhouense, which was supported by in vitro experiments with grass root exudate. M. guizhouense and M. brunneum only associated with wildflower rhizosphere when co-occurring with M. robertsii. With the exception of these co-occurrences, M. guizhouense was found to associate exclusively with the rhizosphere of tree species, while M. brunneum was found to associate exclusively with the rhizosphere of shrubs and trees. These associations demonstrate that different species of Metarhizium associate with specific plant types. In section 2, we explored the variation in the insect adhesin, Madl, and the plant adhesin, Mad2, in fourteen isolates of Metarhizium representing seven different species. Analysis of the transcriptional elements within the Mad2 promoter region revealed variable STRE, PDS, degenerative TATA box, and TATA box-like regions. Phylogenetic analysis of 5' EF-Ia, which is used for species identification, as well as Madl and Mad2 sequences demonstrated that the Mad2 phylogeny is more congruent with 5' EF-1a than Madl. This suggests Mad2 has diverged among Metarhizium lineages, contributing to clade- and species-specific variation. While other abiotic and biotic factors cannot be excluded in contributing to divergence, it appears that plant associations have been the driving factor causing divergence among Metarhizium species.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Variability in the Insect and Plant Adhesins, Mad1 and Mad2, within the Fungal Genus Metarhizium Suggest Plant Adaptation as an Evolutionary Force

Several species of the insect pathogenic fungus Metarhizium are associated with certain plant types and genome analyses suggested a bifunctional lifestyle; as an insect pathogen and as a plant symbiont. Here we wanted to explore whether there was more variation in genes devoted to plant association (Mad2) or to insect association (Mad1) overall in the genus Metarhizium. Greater divergence within the genus Metarhizium in one of these genes may provide evidence for whether host insect or plant is a driving force in adaptation and evolution in the genus Metarhizium. We compared differences in variation in the insect adhesin gene, Mad1, which enables attachment to insect cuticle, and the plant adhesin gene, Mad2, which enables attachment to plants. Overall variation for the Mad1 promoter region (7.1%), Mad1 open reading frame (6.7%), and Mad2 open reading frame (7.4%) were similar, while it was higher in the Mad2 promoter region (9.9%). Analysis of the transcriptional elements within the Mad2 promoter region revealed variable STRE, PDS, degenerative TATA box, and TATA box-like regions, while this level of variation was not found for Mad1. Sequences were also phylogenetically compared to EF-1a, which is used for species identification, in 14 isolates representing 7 different species in the genus Metarhizium. Phylogenetic analysis demonstrated that the Mad2 phylogeny is more congruent with 59 EF-1a than Mad1. This would suggest that Mad2 has diverged among Metarhizium lineages, contributing to clade- and species-specific variation, while it appears that Mad1 has been largely conserved. While other abiotic and biotic factors cannot be excluded in contributing to divergence, these results suggest that plant relationships, rather than insect host, have been a major driving factor in the divergence of the genus Metarhizium.

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

Predicting Physical, Verbal, Social, Racial, and Sexual Bullying with Individual and Environmental Factors from an Evolutionary Ecological Framework

Evidence exists for subtypes of bullying, but there is a lack of studies simultaneously investigating the factors that influence each subtype. The purpose of my thesis was to investigate how individual and environmental factors independently and interactively predict physical, verbal, social, racial, and sexual bullying using an evolutionary ecological framework. Adolescents (N = 225, M = 14.05, SD = 1.54) completed self-reports on demographics, HEXACO personality, Rothbart’s temperament, parenting, friendship quality, school connectedness, and socio-economic status. Subtypes were predicted by low Honesty-Humility in addition to other personality and demographic factors with the exception of physical bullying, which was predicted by environmental factors. Results suggest adolescents adaptively and selectively use bullying to exploit victims and obtain resources, although the subtype used may depend on individual factors bullies possess within Bronfenbrenner’s microsystem, instead of the meso- and exo- systems. Anti-bullying efforts should target these factors and reinforce alternative strategies to obtain resources.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Variability in the Insect and Plant Adhesins, Mad1 and Mad2, within the Fungal Genus Metarhizium Suggest Plant Adaptation as an Evolutionary Force

Several species of the insect pathogenic fungus Metarhizium are associated with certain plant types and genome analyses suggested a bifunctional lifestyle; as an insect pathogen and as a plant symbiont. Here we wanted to explore whether there was more variation in genes devoted to plant association (Mad2) or to insect association (Mad1) overall in the genus Metarhizium. Greater divergence within the genus Metarhizium in one of these genes may provide evidence for whether host insect or plant is a driving force in adaptation and evolution in the genus Metarhizium. We compared differences in variation in the insect adhesin gene, Mad1, which enables attachment to insect cuticle, and the plant adhesin gene, Mad2, which enables attachment to plants. Overall variation for the Mad1 promoter region (7.1%), Mad1 open reading frame (6.7%), and Mad2 open reading frame (7.4%) were similar, while it was higher in the Mad2 promoter region (9.9%). Analysis of the transcriptional elements within the Mad2 promoter region revealed variable STRE, PDS, degenerative TATA box, and TATA box-like regions, while this level of variation was not found for Mad1. Sequences were also phylogenetically compared to EF-1a, which is used for species identification, in 14 isolates representing 7 different species in the genus Metarhizium. Phylogenetic analysis demonstrated that the Mad2 phylogeny is more congruent with 59 EF-1a than Mad1. This would suggest that Mad2 has diverged among Metarhizium lineages, contributing to clade- and species-specific variation, while it appears that Mad1 has been largely conserved. While other abiotic and biotic factors cannot be excluded in contributing to divergence, these results suggest that plant relationships, rather than insect host, have been a major driving factor in the divergence of the genus Metarhizium.

Psychopathy and the Ability to Detect Cheaters: An Evolutionary Perspective

This research examined psychopathy as an evolutionary adaptation that involves cheating and deception. I theorized that this strategy should be associated with certain abilities. This research examined the association between psychopathic traits and the ability to detect cheaters, altruism, deception, and psychopathic traits. Results indicated that psychopathic traits were not significantly associated with the ability to detect cheaters or altruism. Results indicated that high Factor 1 psychopathy scores, and low Factor 2 psychopathy scores, were indicative of higher ratings of deception when viewing deceptive videos. Conversely, when viewing truthful videos, Factor 1 was a significant predictor of higher ratings of deception. Finally, our results indicated that total psychopathy scores were associated the ability to identify psychopathic traits in others. Taken together the results provide mixed support for the evolutionary perspective of psychopathy.