Scholarship & Research
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Item Computational investigation on protein sequencing and genome rearrangement problems(Montana State University - Bozeman, College of Engineering, 2018) Qingge, Letu; Chairperson, Graduate Committee: Binhai ZhuDe novo protein sequencing and genome rearrangement problems are the classical problems in bioinformatics. De novo protein sequencing problem try to determine the whole sequence of amino acids based on the mass spectrometry data without using the database search. Genome rearrangement problems try to recognize the evolutionary process between two species. In this dissertation, first, we describe the process of constructing target protein sequences by utilizing mass spectrometry based data from both top-down and bottom-up tandem mass spectra. In addition to using data from mass spectrometry analysis, we also utilize techniques for de novo protein sequencing using a homologous protein sequence as a reference to attempt to fill in any remaining gaps in the constructed protein scaffold. Initial results for analysis on real datasets yield over 96-100% coverage and 73-91% accuracy with the target protein sequence. Second, we use different genome rearrangement operations to transform one genome to another such that the similarity between two genomes is maximized. We explore these problems in terms of theoretical and experimental analysis. For sorting unsigned genome problem by double cut and join (DCJ) operation, we design a randomized fixed parameter tractable (FPT) approximation algorithm for computing the DCJ distance with an approximation factor 4/3 + Epsilon, and the running time O*(2 d*), where d* represents the optimal DCJ distance. For one-sided exemplar adjacency number problem, we reformulate the problem as maximum independent set in a colored interval graph and hence reduce the appearance of each gene at most twice. Moreover, we design a factor-2 approximation and also show that the approximation factor can not be improved less than 2 by some local search technique. At last, we apply integer linear programming to solve the reduced instance exactly. For the minimum copy number generation problem, we analyze the complexity of different variations of this problem and show a practical algorithm for the general case based on greedy method.Item Scheduling for optimized network resource utilization #smartgrid #cloud(Montana State University - Bozeman, College of Engineering, 2017) Yaw, Sean; Chairperson, Graduate Committee: Brendan MumeyThe performance of distributed applications is heavily dependent on the interplay between the applications and the underlying network. Disparity between the requirements of the applications and the capabilities of the network leads to degraded application performance, which in turn results in a drop in application usage or revenue. For example, many real-time interactive applications require lower latency than the public Internet provides, resulting in a poor experience for application users. At other times though, applications fail to effectively utilize all network capabilities. For example, conventional electrical appliances are currently unable to leverage the increased communication capabilities provided by the future smart power grid to decrease costs or modify consumption. Scheduling is an optimization technique to temporally and spatially allocate resources in such a way as to achieve some desired parameter optimization, such as minimized cost. In this dissertation, I study the use of scheduling techniques to counteract application performance degradation present due to the disparity between application requirements and network capabilities. I explore this disparity in both the smart grid and cloud networks, and propose novel algorithms that rely on numerous algorithmic techniques to realize application performance increases.