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.

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.

Genetic and Electrophysiological Correlates of Self-Regulation in Adolescence

Self-regulation is considered a powerful predictor of behavioral and mental health outcomes during adolescence and emerging adulthood. In this dissertation I address some electrophysiological and genetic correlates of this important skill set in a series of four studies. Across all studies event-related potentials (ERPs) were recorded as participants responded to tones presented in attended and unattended channels in an auditory selective attention task. In Study 1, examining these ERPs in relation to parental reports on the Behavior Rating Inventory of Executive Function (BRIEF) revealed that an early frontal positivity (EFP) elicited by to-be-ignored/unattended tones was larger in those with poorer self-regulation. As is traditionally found, N1 amplitudes were more negative for the to-be-attended rather than unattended tones. Additionally, N1 latencies to unattended tones correlated with parent-ratings on the BRIEF, where shorter latencies predicted better self-regulation. In Study 2 I tested a model of the associations between selfregulation scores and allelic variations in monoamine neurotransmitter genes, and their concurrent links to ERP markers of attentional control. Allelic variations in dopaminerelated genes predicted both my ERP markers and self-regulatory variables, and played a moderating role in the association between the two. In Study 3 I examined whether training in Integra Mindfulness Martial Arts, an intervention program which trains elements of self-regulation, would lead to improvement in ERP markers of attentional control and parent-report BRIEF scores in a group of adolescents with self-regulatory difficulties. I found that those in the treatment group amplified their processing of attended relative to unattended stimuli over time, and reduced their levels of problematic behaviour whereas those in the waitlist control group showed little to no change on both of these metrics. In Study 4 I examined potential associations between self-regulation and attentional control in a group of emerging adults. Both event-related spectral perturbations (ERSPs) and intertrial coherence (ITC) in the alpha and theta range predicted individual differences in self-regulation. Across the four studies I was able to conclude that real-world self-regulation is indeed associated with the neural markers of attentional control. Targeted interventions focusing on attentional control may improve self-regulation in those experiencing difficulties in this regard.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.