Genetic Algorithm (GA) as a class of Evolutionary Algorithm (EA) is a search algorithm based on the mechanics of natural selection and natural genetics. This dissertation presents the description, solving procedures and application of GA. The definitions of selection, crossover and mutation operators are given in details and an application based on GA in Time Table Problem (TTP) is performed in a new way. Due to its high capability of overall search, GA is particularly appropriate for solving timetabling and scheduling problems. TTP (Time Table Problem) which belongs to NP-hard problem is a special problem concerning resource management. In this dissertation, a new chromosome coding is designed in order to solve TTP more effectively. And the result presented by MATLAB will converge to a steady condition.

The catechol meta-fission pathway, a degradation pathway for simple aromatic compounds, is rich in enzyme chemistry and replete with structural and evolutionary diversity. Vinyl pyruvate hydratase (VPH) and MhpD catalyze the same reaction in this pathway, but in different bacterial species. These metal ion-dependent enzymes reportedly catalyze a 1,5-keto-enol tautomerization reaction followed by a Michael addition of water. MhpD, and most likely VPH, are members of the fumarylacetoacetate hydrolase (FAH) superfamily. The crystal structure of MhpD and the sequence of VPH identified four potential active site residues, Lys-60, Leu-72, Asp-78, and Ser-160 (Ser-161 in VPH). The K60A and D78N mutants of VPH and MhpD had the most damaging effects on catalysis. Moreover, the K60A mutant seemingly uncoupled tautomerization from hydration and provided evidence for an [alpha, beta]-unsaturated ketone in the reaction. The effects of the L72A and S160A (S161A in VPH) mutants were smaller, suggesting less important roles in the mechanism. 5-(carboxymethyl)-2-Oxo-3-hexene-1,6-dioate decarboxylase (COHED) is a metal ion-dependent enzyme in the homoprotocatechuate (HPC) pathway, a chromosomally encoded meta-fission pathway from Escherichia coli C that parallels the catechol meta-fission pathway. COHED is also a member of the FAH superfamily. It is a monomeric protein with two domains. It is postulated that the C-terminal domain catalyzes the decarboxylation reaction and the N-terminal domain carries out the 1,3-keto-enol tautomerization reaction. Site-directed mutagenesis, NMR, and kinetic analysis with different substrates and inhibitors have identified three potential active-site residues Glu-276, Glu-278 (in the C-terminal domain), and Lys-110 (in the N-terminal domain). Replacement of either glutamate with a glutamine eliminated both the decarboxylase and tautomerase activities. The K110A mutant also diminished both activities, but more importantly eliminated the C-3 proton/deuteron exchange reaction observed for substrate analogs. The enzymes of the catechol and homoprotocatechuate pathways provide examples of enzyme optimization toward a specific substrate even among related compounds, as reflected by the FAH superfamily. Hence, the results of these studies add to the growing body of information about how enzymes evolve and how pathways are assembled. / text

In this dissertation we prove that if an n-stranded pretzel knot K has an essential Conway sphere, then there exists an Alexander grading s such that the rank of knot Floer homology in this grading, [mathematical equation], is at least two. As a consequence, we are able to easily classify pretzel knots admitting L-space surgeries. We conjecture that this phenomenon occurs more generally for any knot in S³ with an essential Conway sphere. We also exhibit an infinite family of knots, each of which admits a nontrivial genus two mutant which shares the same total dimension of knot Floer homology, while being distinguished by knot Floer homology as a bigraded invariant. Additionally, the genus two mutation interchanges the [mathematical symbol]-graded knot Floer homology groups in [mathematical symbol]-gradings k and -k. This infinite family of examples supports a second conjecture, namely that the total rank of knot Floer homology is invariant under genus two mutation. / text

published_or_final_version / Pathology / Master / Master of Philosophy

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

published_or_final_version / Biochemistry / Doctoral / Doctor of Philosophy

Mutation testing and adequacy testing are two major technologies to assure the quality of software. In this thesis, we present the first work that alleviates the high cost of mutation testing and ineffectiveness of adequacy testing for context-aware applications. We also present large-scale multi-subject case studies to evaluate how our work successfully alleviates these problems. Mutation testing incurs a high execution cost if randomly selected test inputs kill a small percentage of remaining live mutants. To address this problem, we formulate the notion of context diversity to measure the context changes inherent in test inputs, and propose three context-aware strategies in the selection of test inputs. The empirical results show that the use of test inputs with higher context diversity can significantly benefit mutation testing in terms of resulting in fewer test runs, fewer test case trials, and smaller resultant test suites that achieve a high mutation score level. The case study also shows that at the test case level, the context diversity of test inputs positively and strongly correlates with multiple types of adequacy metrics, which provide a foundation on why context diversity contributes to the effectiveness of test cases in revealing faults in context-aware applications. In adequacy testing, many strategies randomly select test cases to construct adequate test suites with respect to program-based adequacy criteria. They usually exclude redundant test cases that are unable to improve the coverage of the test requirements of an adequacy criterion achieved by constructing test suites. These strategies have not explored in the diversity in test inputs to improve the test effectiveness of test suites. To address this problem, we propose three context-aware refined strategies to check whether redundant test cases can replace previously selected test cases to achieve the same coverage level but with different context diversity levels. The empirical study shows that context diversity can be significantly injected into adequate test suites, and favoring test cases with higher context diversity can significantly improve the fault detection rates of adequate test suites for testing context-aware applications. In conclusion, this thesis makes the significant contributions to the research in testing context-aware applications: (1) It has formulated context diversity, a novel metric to measure context changes inherent in test inputs. (2) It has proposed three context-aware strategies to select test cases with different levels of context diversity. Compared with the baseline strategy, the strategy CAS-H that uses test cases with higher context diversity can significantly reduce the cost of mutation testing over context-aware applications in terms of less number of test runs, smaller adequate test suites, and less number of test inputs used to construct test suites. (3) It has defined three context-aware refined strategies to construct adequate test suites with different context diversity levels. Compared with the baseline strategy, the strategy CARS-H that favors test cases with higher context diversity can significantly improve the effectiveness of adequacy testing in terms of higher fault detection rates. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

The rapidly developing sequencing technology has brought up an opportunity to scientists to look into the detailed genotype information in human genome. Computational programs have played important roles in identifying disease related genomic variants from huge amount of sequencing data. In the past years, a number of computational algorithms have been developed, solving many crucial problems in sequencing data analysis, such as mapping sequencing reads to genome and identifying SNPs. However, many difficult and important issues are still expecting satisfactory solutions. A key challenge is identifying disease related mutations in the background of non-pathogenic polymorphisms. Another crucial problem is detecting INDELs especially the long deletions under the technical limitations of second generation sequencing technology. To predict disease related mutations, we developed a machine learning-based (Random forests) prediction tool, EFIN (Evaluation of Functional Impact of Nonsynonymous mutations). We build A Multiple Sequence Alignment (MSA) for a querying protein with its homologous sequences. MSA is later divided into different blocks according to taxonomic information of the sequences. After that, we quantified the conservation in each block using a number of selected features, for example, entropy, a concept borrowed from information theory. EFIN was trained by Swiss-Prot and HumDiv datasets. By a series of fair comparisons, EFIN showed better results than the widely-used algorithms in terms of AUC (Area under ROC curve), accuracy, specificity and sensitivity. The web-based database is provided to worldwide user at paed.hku.hk/efin. To solve the second problem, we developed Linux-based software, SPLindel that detects deletions (especially long deletions) and insertions using second generation sequencing data. For each sample, SPLindel uses split-read method to detect the candidate INDELs by building alternative references to go along with the reference sequences. And then we remap all the relevant reads using both original references and alternative allele references. A Bayesian model integrating paired-end information was used to assign the reads to the most likely locations on either the original reference allele or the alternative allele. Finally we count the number of reads that support the alternative allele (with insertion or deletions comparing to the original reference allele) and the original allele, and fit a beta-binomial mixture model. Based on this model, the likelihood for each INDEL is calculated and the genotype is predicted. SPLindel runs about the same speed as GATK and DINDEL, but much faster than DINDEL. SPLindel obtained very similar results as GATK and DINDEL for the INDELs of size 1-15 bps, but is much more effective in detecting INDELs of larger size. Using machine learning method and statistical modeling technology, we proposed the tools to solve these two important problems in sequencing data analysis. This study will help identify novel damaging nsSNPs more accurately and efficiently, and equip researcher with more powerful tool in identifying INDELs, especially long deletions. As more and more sequencing data are generated, methods and tools introduced in this thesis may help us extract useful information to facilitate identification of causal mutations to human diseases. / published_or_final_version / Paediatrics and Adolescent Medicine / Doctoral / Doctor of Philosophy

abstract / Biochemistry / Doctoral / Doctor of Philosophy

In my dissertation I have broadly studied the evolution of RNA as well as the phylogenetic history of the actin protein family. In the first chapter I examined how various evolutionary processes interact at high mutation rates, which led to simple prediction based on the strength of selection. In the second chapter, I tested mRNA secondary structure stability at the beginning of genes as a way of identifying whether putative genes might be functional or not. Finally, I reconstructed the phylogenetic history of the actin protein family in vertebrates, revealing that a novel isoform is actively evolving in contrast to the remaining protein isoforms.