Solid cancer tumors must recruit new blood vessels for growth and maintenance. Discovering drugs that block this tumor-induced development of new blood vessels (angiogenesis) is an important approach in cancer treatment. However, the complexity of angiogenesis and the difficulty in implementing and evaluating medical changes prevent the discovery of novel and effective new therapies. This paper presents a massively parallel computational search-based approach for the discovery of novel potential cancer treatments, using a high fidelity simulation of angiogenesis. Discovering new therapies is viewed as multi-objective combinatorial optimization over two competing objectives: minimizing the medical cost of the intervention while minimizing the oxygen provided to the cancer tumor by angiogenesis. Results show the effectiveness of the search process in finding simple interventions that are currently in use and more interestingly, discovering some new approaches that are counterintuitive yet effective. Distributed systems are becoming more prevalent as the demand for connectivity increases. Developers are faced with the challenge of creating software systems that meet these demands and adhere to good software practices. Technologies of today aid developers in this, but they may cause applications to suffer performance problems and require developers to abandon basic software concepts, such as modularization, performance, and maintainability. This work presents the Vitruvian framework that provides solutions to common distribution goals, and distributes applications using replication and transparency at varying stages of application development.

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Thesis (Ph. D.)--University of Idaho, 2006. / Abstract. "May 2006." Includes bibliographical references (leaves 100-106). Also available online in PDF format.

Maximum likelihood methods are used to estimate the phylogenetic trees for a set of species. The probabilities of DNA base substitutions are modeled by continuous-time Markov chains. We use these probabilities to estimate which DNA bases would produce the data that we observe. The topology of the tree is also determined using base substitution probabilities and conditional likelihoods. Felsenstein [2] introduced this method of finding an estimate for the maximum likelihood phylogenetic tree. We will explore this method in detail in this paper.

The first aim of this thesis is to examine relationships between microRNAs targeting gene networks, combining knowledge from microRNA prediction databases into our microRNA Data Integration Portal (mirDIP). Modeling the microRNA:transcript interactome – referred to as the micronome – to build microRNA interaction networks of signalling pathways, we find genes within signalling pathways to be co-targeted by common microRNAs suggesting an unexpected level of transcriptional control. We identify two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets, to be more studied and more involved in cancer signalling than their intrapathway counterparts. The second aim was to undertake a more focused view, analyzing the characteristics of microRNAs within the micronome itself beginning with a focus on the under-examined microRNA terminal loop across the micronome to determine if this region of the microRNA structure might contribute to microRNA functioning. We have identified 2 main classes of microRNAs based on loop structure – perfect and occluded, which show biological relevance. We found regulatory motifs within microRNA terminal loops and found a large number of Frequently Occurring Words (FOWs) significantly overrepresented across the micronome. Set analysis of in vitro secreted microRNAs, microRNA expression across a panel of normal tissues, and microRNAs shown to be secreted in lung cancer shows that specific microRNA loop motifs within these groups are significantly overreperesented – suggesting that microRNA terminal loops harbour sequences bearing microRNA processing and localization signals.

Post-transcriptional regulation is carried out by RNA-binding proteins (RBPs) that bind to specific RNA molecules and control their processing, localization, stability and degradation. Experimental studies have successfully identified RNA targets associated with specific RBPs. However, because the locations of the binding sites within the targets are unknown and because RBPs recognize both sequence and structure elements in their binding sites, identification of RBP binding preferences from these data remains challenging. The unifying theme of this thesis is to identify RBP binding preferences from experimental data. First, we propose a protocol to design a complex RNA pool that represents diverse sets of sequence and structure elements to be used in an in vitro assay to efficiently measure RBP binding preferences. This design has been implemented in the RNAcompete method, and applied genome-wide to human and Drosophila RBPs. We show that RNAcompete-derived motifs are consistent with established binding preferences. We developed two computational models to learn binding preferences of RBPs from large-scale data. Our first model, RNAcontext uses a novel representation of secondary structure to infer both sequence and structure preferences of RBPs, and is optimized for use with in vitro binding data on short RNA sequences. We show that including structure information improves the prediction accuracy significantly. Our second model, MaLaRKey, extends RNAcontext to fit motif models to sequences of arbitrary length, and to incorporate a richer set of structure features to better model in vivo RNA secondary structure. We demonstrate that MaLaRKey infers detailed binding models that accurately predict binding of full-length transcripts.

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Since Darwin proposed that all species on the earth have evolved from a common ancestor, evolution has played an important role in understanding biology. While the evolutionary relationships/histories of genes are represented using trees, the genomic evolutionary history may not be adequately captured by a tree, as some evolutionary events, such as horizontal gene transfer (HGT), do not fit within the branches of a tree. In this case, phylogenetic networks are more appropriate for modeling evolutionary histories. In this dissertation, we present computational algorithms to reconstruct phylogenetic networks from different types of data. Under the assumption that species have single copies of genes, and HGT and speciation are the only events through the course of evolution, gene sequences can be sampled one copy per species for HGT detection. Given the alignments of the sequences, we propose systematic methods that estimate the significance of detected HGT events under maximum parsimony (MP) and maximum likelihood (ML). The estimated significance aims at addressing the issue of overestimation of both optimization criteria in the search for phylogenetic networks and helps the search identify networks with the ``right" number of HGT edges. We study their performance on both synthetic and biological data sets. While the studies show very promising results in identifying HGT edges, they also highlight the issues that are challenging for each criterion. We also develop algorithms that estimate the amount of HGT events and reconstruct phylogenetic networks by utilizing the pairwise Subtree-Prune-Regraft (SPR) operation from a collection of trees. The methods produce good results in general in terms of quickly estimating the minimum number of HGT events required to reconcile a set of trees. Further, we identify conditions under which the methods do not work well in order to help in the development of new methods in this area. Finally, we extend the assumption for the genetic evolutionary process and allow for duplication and loss. Under this assumption, we analyze gene family trees of proteobacterial strains using a parsimony-based approach to detect evolutionary events. Also we discuss the current issues of parsimony-based approaches in the biological data analysis and propose a way to retrieve significant estimates. The evolutionary history of species is complex with various evolutionary events. As HGT contributes largely to this complexity, accurately identifying HGT will help untangle evolutionary histories and solve important questions. As our algorithms identify significant HGT events in the data and reconstruct accurate phylogenetic networks from them, they can be used to address questions arising in large-scale biological data analyses.

In recent years, RNA structural comparison becomes a crucial problem in bioinformatic research. Generally, it is a popular approach for representing the RNA secondary structures with arc-annotation sets. Several methods can be used to compare two RNA structures, such as tree edit distance, longest arc-preserving common subsequence (LAPCS) and stem-based alignment. However, these methods may be helpful only for small RNA structures because of their high time complexity. In this thesis, we propose a simplified method to compare two RNA structures in O(mn)time, where m and n are the lengths of the two RNA sequences, respectively. Our method transforms the RNA structures into specific sequences called object sequences, then compare these object sequences to find their common substructures. We test our comparison method with 118 RNA structures obtained from RNase P Database. For any two structures, we try to identify whether they are in the same family by both structure comparison and sequence comparison. In our experiment, we find that our method for comparing RNA structures can yield better hit rates and is faster than the traditional method to compare the RNA sequences. Therefore, our approach for comparing RNA secondary structures is more sensitive in biology and more efficient in time complexity.

The comparison methods for RNA or protein molecules are important basic tools in molecular biology. So far, most comparison methods are only applicable to the primary structures of biomolecules, such as the sequence alignment and comparison methods. The functions of biomolecules have close relationship with their structures. The recent methods for finding the structures of biomolecules are NMR spectroscopy, X-ray crystallography, and prediction with computational simulation. There are many biomolecules with known structures, but their functions are unknown. The RNA secondary structure alignment problem is to align two RNA molecules to get the structure similarity, where their secondary structures are given. In addition, it is also helpful to predict the functions of biomolecules and to classify them. In this thesis, we design a dynamic programming method for aligning two RNA secondary structures which do not contain any pseudoknot. The time complexity of our algorithm is O(N4), where N is the number of blocks contained in the given RNA sequences. We also apply our algorithm to the real biomolecules, the tRNAs of Homo sapiens mitochondrion, to evaluate the practicability our method. We take three tRNA genes, TRNG, TRNA and TRNV, to test the performance of our algorithm. From the view point of human eyes, in fact, the structure of TRNG is more similar to TRNA. Our algorithm also gets this result. Hence, our algorithm provides an effective method to measure the similarity of two RNA secondary structures.