The extant medically and socially important cobras have been the subject to several comparative taxonomic studies since the 1940s, but still lack an inclusive and thorough phylogenetic tree. With recent major advancements in phylogenetic analysis, it is now common to use multiple independent loci for studying the phylogenetic relationships within groups. For the first time, 27 from the 29 identified Naja species, alongside 5 putative new or elevated species had 4426 base pairs across 1701 sequences of mitochondrial and nuclear DNA sequence data analysed. The results continue to support the monophyletic core cobra clade encompassing the genera Walterinnesia, Aspidelaps, Hemachatus, Pseudohaje and Naja (1.0 Bayesian posterior probability (BPP)), in addition to the grouping of four monophyletic subgenera within Naja. The group of African spitting cobras, Afronaja, is positioned as the sister group to the rest of the genus. Moderate support (0.8 BPP) is found for the grouping of the Asian cobras, Naja, with the African non-spitting cobras, Ureaus. The closest relative to the genus Naja is Pseudohaje goldii, a genus and species never before included in phylogenetic analysis, followed by the sister taxa Hemachatus haemachatus. The king cobra continues to be positioned outside the core cobra group, sister to Hemibungarus calligaster. The results support the hypothesis of three independent origins of spitting, once in the monotypic Hemachatus haemachatus, once within the subgenus Afronaja, and the final origin within the Asian cobras, subgenus Naja. The relationships found were broadly consistent with previous studies, with the additional inclusion of more species creating the most comprehensive cobra phylogeny to date. Further molecular analysis, specifically species delimitation, must be undertaken to ascertain the position of the 5 putative new species included in this study.

Fast-evolving RNA viruses are a leading cause of morbidity and mortality among human and animal populations, contributing significantly to both global health and economic burden. The advent and revolution of high-throughput sequencing has empowered phylogenetic analyses with increasing amounts of temporally and spatially sampled viral data. Moreover, the parallel advancement in molecular evolution and phylogenetic methods has provided investigators with a unique opportunity to gain detailed insight into the evolutionary and epidemiological dynamics of emerging viral pathogens. Using state-of-the-art statistical approaches, this thesis addresses some of the important but controversial questions in viral emergence. Chapter 2 introduces a new framework to quantify and investigate reassortment events in influenza A viruses. By developing a computationally efficient algorithm to calculate the largest common subtree for a pair of tree sets, which are estimated from diffe rent parts of the genome for the same taxa set, the level of phylogenetic incongruency due to reassortment can be appropriately ascertained. Chapters 3, 4 and 5 investigate the evolutionary origins of three diff erent viruses: the novel emergence and cross-species transmission of SARSCoV, the genesis and dissemination of the unique HCV circulating recombinant form, and the ancient divergence of all influenza viruses, respectively. Moreover, Chapter 4 presents an improved statistical framework, which provides more precise evolutionary estimates, by utilizing the hierarchical bayes approach to investigate recombination events in emerging RNA viruses. The last empirical study, presented in Chapter 6, applies the recently developed Bayesian phylogeography models to a large viral sequence dataset sampled from southern Viet Nam to examine the fine-scale spatiotemporal dynamics of endemic dengue in Southeast Asia. The work presented here reflects both the advancements made in sequencing technology and statistical phylogenetics, along with some of the challenges that remain in studying the emergence of fast-evolving RNA viruses. This thesis proposes new and improved solutions to these evolutionary problems, such as incorporating non-vertical evolution (i.e. homologous recombination and reassortment) into the phylodynamic framework, with the aim of facilitating future investigations of emerging viral diseases.

This thesis examines the evolutionary biology of the RNA viruses, a diverse group of pathogens that cause significant diseases. The focus of this work is the relationship between the processes driving the evolution of virus populations within individual hosts and at the epidemic level. First, Chapter One reviews the basic biology of RNA viruses, the current state of knowledge in relevant topics of evolutionary virology, and the principles that underlie the most commonly used methods in this thesis. In Chapter Two, I develop and test a novel framework to estimate the significance of phylogeny-trait association in viral phylogenies. The method incorporates phylogenetic uncertainty through the use of posterior sets of trees (PST) produced in Bayesian MCMC analyses. In Chapter Three, I conduct a comprehensive analysis of the substitution rate of hepatitis C virus (HCV) in within- and between-host data sets using a relaxed molecular clock. I find that within-host substitution rates are more rapid than previously appreciated, that heterotachy is rife in within-host data sets, and that selection is likely to be a primary driver. In Chapter Four I apply the techniques developed in Chapter Two to successfully detect compartmentalization between peripheral blood and cervical tissues in a large data set of human immunodeficiency virus (HIV) patients. I propose that compartmentalization in the cervix is maintained by selection. I extend the framework developed in Chapter Two in Chapter Five and explore the Type II error of the statistics used. In Chapter Six I review the findings of this thesis and conclude with a general discussion of the relationship between within- and among-host evolution in viruses, and some of the limitations of current techniques.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.

Extreme haustorial parasites have long captured the interest of naturalists and scientists with their greatly reduced and highly specialized morphology. Along with the reduction or loss of photosynthesis, the plastid genome often decays as photosynthetic genes are released from selective constraint. This makes it challenging to use traditional plastid genes for parasitic plant phylogenetics, and has driven the search for alternative phylogenetic and molecular evolutionary markers. Thus, evolutionary studies, such as molecular clock-based age estimates, are not yet available for all parasitic lineages. In the present study, we extracted 14 nuclear single copy genes (nSCG) from Illumina transcriptome data from one of the “strangest plants in the world”, Hydnora visseri (Hydnoraceae). A ~15,000 character molecular dataset, based on all three genomic compartments, shows the utility of nSCG for reconstructing phylogenetic relationships in parasitic lineages. A relaxed molecular clock approach with the same multi-locus dataset, revealed an ancient age of ~91 MYA for Hydnoraceae. We then estimated the stem ages of all independently originated parasitic angiosperm lineages using a published dataset, which also revealed a Cretaceous origin for Balanophoraceae, Cynomoriaceae and Apodanthaceae. With the exception of Santalales, older parasite lineages tend to be more specialized with respect to trophic level and have lower species diversity. We thus propose the “temporal specialization hypothesis” (TSH) implementing multiple independent specialization processes over time during parasitic angiosperm evolution.