Echinoids have an endoskeletal system which is ideal for studying calcified structures such as development of vertebrate skeletons. However, understanding echinoid skeletal (test) growth has proven challenging to analyse solely on the basis of any one approach or process. Therefore, theoretical models have been developed to understand growth and form of echinoid tests. Herein, Holotestoid, a computational model of echinoid test growth is described. The model incorporates mathematical principles (e.g., close-packing), physical principles (e.g., interface between coalescing bubbles) and biological processes (e.g., echinoid ontogenic processes). It is the first computational model that emulates all five ontogenic processes involved in test growth (plate growth, plate addition, plate interaction, plate gapping, and visceral growth) using a geometrical representation and three analogies (coalescing bubble, circle-packing, and catenary chains). The emulated processes are used to predict plate size, plate shape, and test shape. The results from the simulations of the growth zones show that the ambulacral column angle (e.g., for A. punctulata α_am= 22° and for S. franciscanus α_am = 32°) is a crucial parameter that distinguishes between species when varied. The. comparison of simulated data with those from real specimens yielded high accuracies, thereby validating the model. The combination of the simulated processes produced patterns mimicking real biological specimens. The model was further used to investigate the test morphological disparity observed among echinoids, specifically between. regular echinoid (sea urchin) tests and irregular echinoid (sand dollar) tests. Both exhibit morphological similarities as imagines, however, they develop different test morphologies as adults. Thus, Holotestoid was used to explore the influence of each parameter on test height-todiameter ratio (h:d). The results showed that both ambulacral column widening and increase in total plate number cause the test h:d to decrease thereby leading to test flattening. Whereas the absolute size of the apical system and peristome does not influence test h:d, however, their growth with respect to column length caused an increase in the test h:d. These results provide an explanation of how the different test shapes were obtained. / Thesis / Doctor of Philosophy (PhD)

Metagenomics is a culture-independent framework for deciphering the complexity of biological communities, often with a focus on microbial communities in a specific environment. The applicability of this approach is widespread due to the ubiquity and presence of unculturable microbes in many environments which can only be investigated using culture-independent methods. With advances in DNA sequencing and the introduction of high-throughput sequencing technologies, studying microbial life as communities has become more accessible. However, the breadth of data generated dictates that computational processing steps must be in place to analyze the data. Due to the large diversity in computational and bioinformatic steps possible for metagenomic data, differences in methods of analysis can lead to discordant interpretations of results. The performance of different metagenomics methods must therefore be assessed to establish the effect on the interpretation of results. Taxonomic classification is an integral step in metagenomic analysis and many tools exist for this purpose. To determine which tools are better suited for particular types of metagenomic data, a comparative analysis of performance was conducted for numerous tools. The findings suggest that hybrid programs may have the best performance and warrant further investigation. Programs such as CLARK, KRAKEN, and MEGAN also performed well and are suitable for metagenomic analysis. Utilizing these methods, investigation into the bacterial populations of four freshwater beaches was examined. Bacterial communities in beach waters and sands were more distinct in terms of taxonomic composition than communities in different lakes. Functional capacity was stable between beach habitats, although enrichment of anaerobic and stress related genes in the sand suggests that this is a relatively harsh environment. The detection of sequences belonging to pathogens in the sands of these beaches also has implications for public health and warrants changes in water quality monitoring procedures. / Thesis / Master of Science (MSc)

Dating of sequence divergence from different species, genes, and strains is now commonplace in biological studies aimed at deciphering micro- and macro-evolutionary temporal patterns. With sequencing becoming increasingly cheaper, molecular datasets are expanding quickly in size. This expansion has necessitated the development of innovative and efficient methods to make the inference of large timetrees feasible from genome-scale datasets that routinely contain hundreds of species. In my dissertation research, I have focused on developing such methods that improved the accuracy, precision, and speed of calculations needed for divergence time inference. I have also conducted large-scale data analyses to reveal fundamental patterns of molecular evolution. The following five related projects were pursued in this dissertation. (1) Development of a machine learning method (CorrTest) for detecting the best-fit model for describing the variation of molecular evolution rates among branches and lineages for large phylogenies. Computer simulations show that the machine learning method outperforms the currently available state-of-the-art methods and is computationally efficient. (2) Development of an analytical method and a new approach to utilize probability densities as calibrations to calculate confidence intervals reliably for RelTime, a non-Bayesian method. Empirical analysis shows that RelTime produces confidence intervals that are comparable to those generated by Bayesian methods, and simulation analysis shows that RelTime confidence intervals often contain the actual values. (3) Application of CorrTest on empirical datasets reveals the extensive autocorrelation in molecular rate in nucleotide and amino acid sequence evolution in diverse taxonomic groups, suggesting that rate autocorrelation is a common phenomenon throughout the tree of life. (4) Investigation of the impact of substitution model complexity on the accuracy and precision of divergence time estimation. Analyses of large-scale empirical data show that the selection of substitution model only has a limited impact on time estimation, as the extremely simple models yield divergence time estimates and credibility intervals remarkably similar to those obtained from very complex models. (5) Inventory of non-Bayesian methods for dating species divergences, including their statistical bases, their performance of estimating divergence times, and the software packages in which they are implemented. A guide has provided for the use of non-Bayesian dating methods to produce reliable divergence times. / Biology

Equal segregation of the replicated genomic content to the two daughter cells is the major task of mitotic cells. The segregation is controlled by a complex system in the cell and relies mainly on the interaction between microtubules (MTs) of the mitotic spindle and kinetochores (KTs), specialized protein structures that assemble on each chromatid of each mitotic chromosome. By combining computational modeling and quantitative light microscopy, we established a quantitative model of the forces and regulators controlling metaphase chromosome movement in the mammalian cell line derived from Potorous tridactylis kidney epithelial cells (PtK1) (Chapter 2). This model can explain key features of metaphase chromosome dynamics and related chromosome structural changes experimentally observed. Moreover, the model made predictions, which we tested experimentally, on how changes in spindle dynamics affect certain aspects of chromosome structure. This quantitative model was next used to study the metaphase dynamics of chromosomes with erroneous KT-MT attachments (Chapter 3). Once again, the model predictions were tested experimentally and showed that erroneous KT-MT attachment alters the dynamics not only of the mis-attached KT, but also of its sister KT. Even more strikingly, experimental data showed that the presence of a single mis-attached KT could perturb the dynamics of all other, normally attached, KTs in anaphase. Chapter 3 also describe how MT poleward flux ensures correct KT-MT attachment and correct chromosome segregation. Indeed, reduced flux is associated with an increase in merotelically attached anaphase lagging chromosomes (LCs). These LCs form micronuclei (MNi) upon mitotic exit. The final effort of this work focused on the fate of MNi and micronuclated (MNed) cells (Chapter 4). Experimental observations showed that most of the chromosomes in MNi missegregated at the cell division following MN formation and that frequently the chromatin in the MN displayed delayed condensation. This work, thus, established a direct link between LCs and aneuploidy through the MN cell cycle. / Ph. D.

No / Membership in a protein domain database does not a domain make; a feature we realized when generating a consensus view of protein fold space with our consensus domain dictionary (CDD). This dictionary was used to select representative structures for characterization of the protein dynameome: the Dynameomics initiative. Through this endeavor we rejected a surprising 40% of the 1,695 folds in the CDD as being non-autonomous folding units. Although some of this was due to the challenges of grouping similar fold topologies, the dissonance between the cataloguing and structural qualification of protein domains remains surprising. Another potential factor is previously overlooked intrinsic disorder; predictions suggest that 40% of proteins have either local or global disorder. One thing is clear, filtering a structural database and ensuring a consistent definition for protein domains is crucial, and caution is prescribed when generalizations of globular domains are drawn from unfiltered protein domain datasets. / NIH

Sudden cardiac death kills 5-10 people per 10,000 population in Europe and the US each year. Individual propensity to arrhythmia and sudden cardiac death is typically assessed through clinical biomarkers. Variability in these biomarkers is a major challenge for risk stratification. Variability is observed at a wide range of spatio-temporal scales within the heart, from temporal fluctuations in ion channel behaviour, to inter-cell and inter-regional differences in ion channel expression, to structural differences between hearts. The extent to which variability manifests between spatial and temporal scales remains unclear but has a potentially crucial role in determining susceptibility to arrhythmia. In this dissertation we present a multi-scale study of the causes and consequences of variability in electrophysiology. At a sub-cellular level we demonstrate that, taking into account inter-individual variability in ion channel conductance, mRNA expression levels in failing human hearts predict the electrophysiological remodelling observed experimentally. On a tissue scale, we advocate the use of phenomenological models where information on subcellular processes is unavailable. We introduce a modification to a phenomenological model to capture beat-to-beat variability in action potential repolarisation recorded from four individual guinea pig myocytes. We demonstrate that, whilst temporal variability is dramatically reduced by inter-cell coupling, differences in their mean action potential duration may become apparent at a tissue level. The ventricular myocardium has a heterogeneous structure not captured by the simplified representation of conduction used above. In our final case study, we challenge a model of conduction by directly comparing simulations to optical mapping recordings of ventricular activation from failing and non-failing human hearts. We observe that good fits to experimental data are obtained only when endocardially bound structures are not in view, suggesting a role in conduction for these structures that are often ignored in cardiac simulations. Finally, we present future directions for the work presented. We make the case for reporting of inter-sample variability in experimental results and conclude that whilst variability may not always manifest across scales, its impact should be considered in both theoretical and experimental studies.

This project brings the bioinformatics community a new development concept in which users can access all data and applications hosted in the main research centers as if they were installed on their local machines, providing seamless integration between disparate services. The project moves to integrate the sequence similarity searching at EBI and NCBI by using web services. It also intends to allow molecular biologists to save their searches and act as a log book for their sequence similarity searches. The project will also allow the biologists to share their sequences and results with others.

Here we describe a finite element model of the mechanical stresses and strains involved in the growth and development of epithelial cancers, specifically pancreatic ductal adenocarcinoma (PDAC). We model a growing tumor swelling over time, modeled as fluid influx in response to changing solute concentrations. Stresses and strains are computed in surrounding material regions in response to this swelling. Further studies are conducted into the relative impacts of factors such as basement membrane thickness, stiffness, and duct radius. We observe that normal stresses are confined mostly to the basement membrane layer and hypothesize that there exists some threshold for axial stress beyond which the basement membrane ruptures and cancer is able to invade into the surrounding tissue.

One of the main goals of genetics has been to understand the link between genotype and phenotype. Using yeast (Saccharomyces cerevisiae) as our model organism, we take a closer look at the connection between genetic variation and gene expression to learn more about the mechanisms of gene regulation. We propose an algorithm based on ANOVA to detect causal relationships between coexpressed genes. We first identify expression quantitative trait loci (eQTLs) with strong effects on gene expression. The algorithm then uses these eQTLs with strong effects and the expression of all genes to identify how genes are affecting each other. This is done by analysing coexpressed gene pairs where both genes have an eQTL and finding if the eQTL of one gene affects the expression of the other. Genes that were found to affect the expression of other genes were named “causal genes”. We evaluate our method by comparing its results with known causal genes and conclude that it is a good predictor of known interactions. Using this algorithm, we found 741 genes having causal effects on gene expression, many of which affected the gene expression of many other genes across the genome (2278 total affected genes). Some of the causal genes clustered at six hotspot regions in the genome. Genes in hotspot regions were found to have lower heritability than genes outside these regions. We hypothesize that hotspot regions may be enriched for essential and/or fitness related genes.

<p>Orthology identification is central to comparative and evolutionary genomics and is an active area of research. Despite a recent shift towards tree reconciliation and other phylogenetic methods, previous comparisons between different algorithms relied on real datasets where true orthology relationships are unknown and did not conclusively show whether phylogenetic methods truly outperform sequence similarity-based methods. Using simulated datasets generated from programs we developed, we show that tree reconciliation does perform better than similarity-based methods when the true species phylogeny is known. Even slight deviations in the species phylogeny can have adverse effects on the performance of reconciliation algorithms and in those cases similarity-based methods may perform better. Fusion and fission complicate orthology identification and are not explicitly considered in most existing algorithms. Programs designed specifically to investigate fusion and fission events are either unavailable or are not specific enough to identify events affecting orthologous genes. We developed a pipeline of programs called FusionFinder that perform this task, gaining new insights to the contributions of fusion and fission to bacterial protein evolution and uncover an unexpected abundance of fissions in <em>Bacillus anthracis</em> that to our knowledge yet to be reported.</p> / Master of Science (MS)