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During HIV-1 assembly, the Gag structural protein specifically encapsidates two copies of viral genomic RNA in the form of a dimer. An RNA stem-loop structure (SL1) in the 5' untranslated region, known as the dimerization initiation site (DIS), is important for dimerization and packaging of HIV-1 genomic RNA; however, the mechanisms involved are not fully understood. The major goal of this PhD study was to further understand HIV-1 RNA dimerization, and to study the role of the Gag protein in the dimerization and packaging processes. Despite the known involvement of the DIS in RNA dimerization, DIS-mutated viruses still contain significant levels of dimerized RNA, and electron microscopy studies suggest that the RNA molecules are linked at the extreme 5' end. We show here that RNA sequences on both sides of the DIS are also required for HIVA genome dimerization, suggesting that multiple RNA elements are involved. We have also examined the contribution of specific amino acids within Gag to the dimerization and packaging processes. Previous work showed that partial deletion of the DIS impacted on viral replication capacity, but could largely be corrected by compensatory point mutations within Gag. To further elucidate the mechanism(s) of these compensatory mutations, we generated DIS mutants lacking the entire SL1, or only the SL1 loop sequences, and combined these deletions with various combinations of compensatory mutations. Analysis of virion-derived RNA showed that the relevant mutant viruses contained increased levels of spliced viral RNA compared to wild type, indicating that a defect in genome packaging specificity was present. However, this defect was corrected by our compensatory mutations, and a T121 substitution in p2 was shown to be solely responsible for this activity. These results suggest that the p2 spacer peptide plays a critical role in the specific packaging of viral genomic RNA. In summary, these findings provide new insig

Diss. (sammanfattning) Uppsala : Uppsala universitet, 2005. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2006. / Härtill 5 uppsatser.

Thesis (Ph.D. in Pharmaceutical Sciences) -- University of Colorado at Denver and Health Sciences Center, 2006. / Typescript. Includes bibliographical references (leaves 130-149, 177-182). Free to UCDHSC affiliates. Online version available via ProQuest Digital Dissertations;

Diss. (sammanfattning) Stockholm : Karol. inst., 2003. / Härtill 4 uppsatser.

Diss. (sammanfattning) Stockholm : Karol. inst., 2004. / Härtill 5 uppsatser.

Diss. (sammanfattning) Stockholm : Karolinska institutet, 2007. / Härtill 5 uppsatser.

Lee Kit Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 115-117). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Project Background --- p.1 / Chapter 1.2 --- Problem Specifications --- p.3 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Medical Data Mining --- p.8 / Chapter 2.1.1 --- General Information --- p.9 / Chapter 2.1.2 --- Related Research --- p.10 / Chapter 2.1.3 --- Characteristics and Difficulties Encountered --- p.11 / Chapter 2.2 --- DNA Sequence Analysis --- p.13 / Chapter 2.3 --- Hepatitis B Virus --- p.14 / Chapter 2.3.1 --- Virus Characteristics --- p.15 / Chapter 2.3.2 --- Important Findings on the Virus --- p.17 / Chapter 2.4 --- Bayesian Network and its Classifiers --- p.17 / Chapter 2.4.1 --- Formal Definition --- p.18 / Chapter 2.4.2 --- Existing Learning Algorithms --- p.19 / Chapter 2.4.3 --- Evolutionary Algorithms and Hybrid EP (HEP) --- p.22 / Chapter 2.4.4 --- Bayesian Network Classifiers --- p.25 / Chapter 2.4.5 --- Learning Algorithms for BN Classifiers --- p.32 / Chapter 3 --- Bayesian Network Classifier for Clinical Data --- p.35 / Chapter 3.1 --- Related Work --- p.36 / Chapter 3.2 --- Proposed BN-augmented Naive Bayes Classifier (BAN) --- p.38 / Chapter 3.2.1 --- Definition --- p.38 / Chapter 3.2.2 --- Learning Algorithm with HEP --- p.39 / Chapter 3.2.3 --- Modifications on HEP --- p.39 / Chapter 3.3 --- Proposed General Bayesian Network with Markov Blan- ket (GBN) --- p.40 / Chapter 3.3.1 --- Definition --- p.41 / Chapter 3.3.2 --- Learning Algorithm with HEP --- p.41 / Chapter 3.4 --- Findings on Bayesian Network Parameters Calculation --- p.43 / Chapter 3.4.1 --- Situation and Errors --- p.43 / Chapter 3.4.2 --- Proposed Solution --- p.46 / Chapter 3.5 --- Performance Analysis on Proposed BN Classifier Learn- ing Algorithms --- p.47 / Chapter 3.5.1 --- Experimental Methodology --- p.47 / Chapter 3.5.2 --- Benchmark Data --- p.48 / Chapter 3.5.3 --- Clinical Data --- p.50 / Chapter 3.5.4 --- Discussion --- p.55 / Chapter 3.6 --- Summary --- p.56 / Chapter 4 --- Classification in DNA Analysis --- p.57 / Chapter 4.1 --- Related Work --- p.58 / Chapter 4.2 --- Problem Definition --- p.59 / Chapter 4.3 --- Proposed Methodology Architecture --- p.60 / Chapter 4.3.1 --- Overall Design --- p.60 / Chapter 4.3.2 --- Important Components --- p.62 / Chapter 4.4 --- Clustering --- p.63 / Chapter 4.5 --- Feature Selection Algorithms --- p.65 / Chapter 4.5.1 --- Information Gain --- p.66 / Chapter 4.5.2 --- Other Approaches --- p.67 / Chapter 4.6 --- Classification Algorithms --- p.67 / Chapter 4.6.1 --- Naive Bayes Classifier --- p.68 / Chapter 4.6.2 --- Decision Tree --- p.68 / Chapter 4.6.3 --- Neural Networks --- p.68 / Chapter 4.6.4 --- Other Approaches --- p.69 / Chapter 4.7 --- Important Points on Evaluation --- p.69 / Chapter 4.7.1 --- Errors --- p.70 / Chapter 4.7.2 --- Independent Test --- p.70 / Chapter 4.8 --- Performance Analysis on Classification of DNA Data --- p.71 / Chapter 4.8.1 --- Experimental Methodology --- p.71 / Chapter 4.8.2 --- Using Naive-Bayes Classifier --- p.73 / Chapter 4.8.3 --- Using Decision Tree --- p.73 / Chapter 4.8.4 --- Using Neural Network --- p.74 / Chapter 4.8.5 --- Discussion --- p.76 / Chapter 4.9 --- Summary --- p.77 / Chapter 5 --- Adaptive HEP for Learning Bayesian Network Struc- ture --- p.78 / Chapter 5.1 --- Background --- p.79 / Chapter 5.1.1 --- Objective --- p.79 / Chapter 5.1.2 --- Related Work - AEGA --- p.79 / Chapter 5.2 --- Feasibility Study --- p.80 / Chapter 5.3 --- Proposed A-HEP Algorithm --- p.82 / Chapter 5.3.1 --- Structural Dissimilarity Comparison --- p.82 / Chapter 5.3.2 --- Dynamic Population Size --- p.83 / Chapter 5.4 --- Evaluation on Proposed Algorithm --- p.88 / Chapter 5.4.1 --- Experimental Methodology --- p.89 / Chapter 5.4.2 --- Comparison on Running Time --- p.93 / Chapter 5.4.3 --- Comparison on Fitness of Final Network --- p.94 / Chapter 5.4.4 --- Comparison on Similarity to the Original Network --- p.95 / Chapter 5.4.5 --- Parameter Study --- p.96 / Chapter 5.5 --- Applications on Medical Domain --- p.100 / Chapter 5.5.1 --- Discussion --- p.100 / Chapter 5.5.2 --- An Example --- p.101 / Chapter 5.6 --- Summary --- p.105 / Chapter 6 --- Conclusion --- p.107 / Chapter 6.1 --- Summary --- p.107 / Chapter 6.2 --- Future Work --- p.109 / Bibliography --- p.117

Ip Lai Nang. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 67-71). / Abstracts in English and Chinese. / ABSTRACT (ENGLISH) --- p.iii / ABSTRACT (CHINESE) --- p.iv / ACKNOWLEDGEMENTS --- p.v / TABLE OF CONTENTS --- p.vi / LIST OF TABLES --- p.viii / LIST OF FIGURES --- p.ix / LIST OF APPENDIX --- p.x / Chapter CHAPTER 1 --- INTRODUCTION --- p.1 / Chapter CHAPTER 2 --- BACKGROUND --- p.3 / Chapter 2.1 --- Structure of DNA --- p.3 / Chapter 2.2 --- Sequence dependent stability --- p.8 / Chapter 2.3 --- Thermodynamics of DNA --- p.9 / Chapter 2.4 --- Model for predicting thermodynamic parameters of DNA sequence --- p.15 / Chapter 2.4.1 --- The nearest-neighbor (NN) model / Chapter 2.4.1.1 --- Background --- p.15 / Chapter 2.4.1.2 --- Method for predicting thermodynamic parameters --- p.16 / Chapter 2.4.1.3 --- Limitation of the NN model --- p.19 / Chapter CHAPTER 3 --- EXPERIMENTAL METHOD --- p.20 / Chapter 3.1 --- Design of DNA sequences PAGE --- p.20 / Chapter 3.2 --- DNA synthesis and purification --- p.22 / Chapter 3.3 --- UV measurement --- p.23 / Chapter CHAPTER 4 --- THE NEXT NEAREST-NEIGHBOR (NNN) MODEL --- p.27 / Chapter 4.1 --- Method for extracting the NNN thermodynamic parameters --- p.30 / Chapter 4.2 --- Discussions --- p.34 / Chapter 4.2.1 --- Comparison of the NN model and the NNN model --- p.34 / Chapter 4.2.2 --- The NNN effect --- p.38 / Chapter 4.2.3 --- Sequence-specific local structure of DNA and the NNN effect / Chapter CHAPTER 5 --- SUMMARY AND FUTURE WORK --- p.49 / APPENDIX I´ؤ XVI --- p.51 / REFERENCE --- p.67