Experiments were performed using a modification of the technique developed by Spanner and Prebble 1962 to monitor the translocation of tracers along the petiole of Nymohoides peltata. Three tracers were used, Br and Na and at least three experiments were performed using each isotope. A very complete record of tracer distribution in time and space was obtained for each isotope. Computer simulation of translocation was developed based on a physical model consisting of a centrally located conducting channel in which a mass flow occurs and a surrounding non-conducting ground tissue. Reversible lateral exchange takes place between these two. The simulation is characterised by an inputfunction and three dimensionless parameters, V, R and K which are related to the velocities of longitudinal flow and reversible lateral leakage and which can be varied with both time and distance. The simulation is possessed of considerable versatility and in this way has many advantages over existing mathematical models. The information it provides is useful in its own right and suggests that the importance accorded the linear nature of the semi-logarithmic profiles in the past is unwarrented. In association with the above simulation a simple direct search optimisation programme was developed based on the least squares criterion to match the simulation to the experimental data. It was found that an acceptable match to the experiments could only be obtained if the time course accumulation of activity in the root-stock was reduced by an arbitrary factor. Although this part of the work failed in its main objective of obtaining estimates of the translocation parameters, it at least suggested a way in which they may be estimated and is encouraging for future work.

Quantitative structure-activity relationships (QSAR) rationalize interrelation between molecular structure and biological response in terms of either physicochemical parameters, as in linear free energy relationships (LFER), or via purely empirical parameters, as is the case for De Novo schemes. In LFER the leading process is often the partitioning of a compound between two solvent phases, taken to represent the transfer of a drug molecule across a biological membrane. This study has investigated the partitioning behaviour of three series of hydroxybenzoate esters, viz. o-, m- and predominantly p-esters, the latter being preservatives in pharmaceutical formulations. The thermodynamic parameters AH, AG and AS for the transfer process were derived in an attempt to establish a QSAR. on a fundamental thermodynamic basis. Such parameters have identifiable physicochemical meaning and lend themselves more readily to interpretation. This facilitates application to alternative systems. A new Gibbs function factor analysis was developed and utilized to obtain thermodynamic contributions for parent and incremental methylene group portions of thestudy molecules. The empirical Collander equation for interrelation of various solute/solvent systems was also rationalized on a thermodynamic basis. Further extension of the Gibbs function factor analysis allowed scaling of "solvent" systems including chromatographic packings, solvents and liposomes. The scheme indicated capacity for optimized selection of bulk solvent systems to mimic biological membranes. A novel analytical procedure for direct measurement of biological response was developed. The bioassay appeared capable of discrimination i) between the closely related structural homologues, ii) between gram-negative and gram-positive bacteria, and further, iii) between certain cell batches of the same bacteria type. Also, the bioassay demonstrated a Collander interrelation between the two bacteria types. Flow microcalorimetry was the technique employed to measure thermal response of respiring E. coli and Staph, aur. bacteria. The modification of biological response with drug concentration was quantitated and a log dose max term was derived for each homologue. The results indicated potential for a predictive, additive structure-activity scheme based on assessment of biological response (BR) direct rather than through f(BR) via physicochemical or empirical parameters.

abstract: In species with highly heteromorphic sex chromosomes, the degradation of one of the sex chromosomes can result in unequal gene expression between the sexes (e.g., between XX females and XY males) and between the sex chromosomes and the autosomes. Dosage compensation is a process whereby genes on the sex chromosomes achieve equal gene expression which prevents deleterious side effects from having too much or too little expression of genes on sex chromsomes. The green anole is part of a group of species that recently underwent an adaptive radiation. The green anole has XX/XY sex determination, but the content of the X chromosome and its evolution have not been described. Given its status as a model species, better understanding the green anole genome could reveal insights into other species. Genomic analyses are crucial for a comprehensive picture of sex chromosome differentiation and dosage compensation, in addition to understanding speciation. In order to address this, multiple comparative genomics and bioinformatics analyses were conducted to elucidate patterns of evolution in the green anole and across multiple anole species. Comparative genomics analyses were used to infer additional X-linked loci in the green anole, RNAseq data from male and female samples were anayzed to quantify patterns of sex-biased gene expression across the genome, and the extent of dosage compensation on the anole X chromosome was characterized, providing evidence that the sex chromosomes in the green anole are dosage compensated. In addition, X-linked genes have a lower ratio of nonsynonymous to synonymous substitution rates than the autosomes when compared to other Anolis species, and pairwise rates of evolution in genes across the anole genome were analyzed. To conduct this analysis a new pipeline was created for filtering alignments and performing batch calculations for whole genome coding sequences. This pipeline has been made publicly available. / Dissertation/Thesis / Masters Thesis Biology 2016

The study of proteins and their function is key to understanding how the cell works in normal and disease states. Historically, the study of protein function was limited to biochemical characterisation, but as computing power and the number of available protein sequences and structures increased this allowed the relationship between sequence, structure and function to be explored. As the number of sequences and structures grows beyond the capacity for experimental groups to study them, computational approaches to inferring function become more important. Enzymes make up approximately half of the known protein sequences and structures, and most of the work in this thesis focuses on the relationship between the sequence, structure and function in enzymes.Firstly, the differences in sequence and structural features between enzymes of the six main functional classes are explored. Features that exhibited the most significant differences between the six classes were further studied to explore their link with function. This study suggested reasons as to why groups of functionally similar but non-homologous enzymes share similar sequence and structural features. A computational tool to predict EC class was then developed in an attempt to exploit the differences in these features. In order to calculate features relating to a particular active site to be used in the EC class prediction method, it was first necessary to predict the active site location. A comprehensive analysis of currently-available functional site prediction tools identified an approach previously developed by this group as amongst the best-performing methods. Here, a tool was created to deliver this approach via a publicly-available web-server, which was subsequently used in the attempt to predict EC class. The study of differences in sequence and structural features between classes revealed differences in oligomeric status between functions. High-order oligomers were linked to an increase in metabolic control in the lyases, possibly via mechanisms such as cooperativity. To further test this idea, it was necessary to be able to computationally identify oligomeric enzymes that act cooperatively. Since no such method currently exists, the degree of coupling of dynamic fluctuations between subunits was explored as a possible way of detecting cooperativity. Whilst this was unsuccessful, the study highlighted the existence of a pattern of correlated motions that were conserved over a wide range of non-homologous and functionally diverse proteins. These observations shed further light on the link between sequence, structure and function and highlight the functional importance of dynamics in protein structures.

The Hannenhalli-Pevzner algorithm for computing the evolutionary distance between two genomes is very efficient when the genomes are signed and totally ordered. But in real comparative maps, the data suffer from problems such as coarseness, missing data, no signs, paralogy, order conflicts and mapping noise. In this thesis we have developed a suite of algorithms for genome rearrangement analysis in the presence of noise and incomplete information. For coarseness and missing data, we represent each chromosome as a partial order, summarized by a directed acyclic graph (DAG). We augment each DAG to a directed graph (DG) in which all possible linearizations are embedded. The chromosomal DGs representing two genomes are combined to produce a single bicoloured graph. The major contribution of the thesis is an algorithm for extracting a maximal decomposition of some subgraph into alternating coloured cycles, determining an optimal sequence of rearrangements, and hence the genomic distance. Also based on this framework, we have proposed an algorithm to solve all the above problems of comparative maps simultaneously by adding heuristic preprocessing to the exact algorithm approach. We have applied this to the comparison of maize and sorghum genomic maps on the GRAMENE database. A further contribution treats the inflation of genome distance by high levels of noise due to incorrectly resolved paralogy and error at the mapping, sequencing and alignment levels. We have developed an algorithm to remove the noise by maximizing strips and tested its robustness as noise levels increase.

The basic rearrangement phylogeny methods require that the genomic content be the same in all the organisms being compared, and so are not applicable when one or more of the genomes being compared derive from ancestral whole genome doubling (WGD) events. In this thesis I developed algorithms for rearrangement phylogeny for sets of related genomes that include both descendants of WGD and unduplicated genomes. Furthermore I investigated the properties of these algorithms and validated them by applying them to real data. I defined varions possible local configurations of doubled and unduplicated genomes in a given phylogeny, each of which requires a different strategy for integrating genomic distance, halving and rearrangement median algorithms. The genome halving algorithm of El-Mabrouk and Sankoff efficiently reconstructs an ancestral pre-doubling genome from the chromosomal distribution of duplicate genes created by this event and remaining today in the descendant genome. However, this algorithm can produce many alternate optimal solutions. To reduce this non-uniqeness, hopefully to only one solution, I developed the guided genome halving algorithm. This rapidly and accurately constructs an optimal ancestor closest to one or more outgroups. As I refined this algorithm, I applied it to successively larger data sets, increasing in size over more than two orders of magnitude. Thus I constructed the ancestors of cereals, based on duplicate markers in maize and using rice and sorghum as outliers. I reconstructed genomes corresponding to the ancestral nodes of yeasts in the Saccharomyces complex, as well as the ancestor of poplar, based on grapevine and papaya as outgroups. I studied two cases involving two WGD descendants, one where the doubling precedes a speciation event and another where doubling occurs independently in both lineages initiated by a speciation event. I developed combinatorial algorithms permitting us to decide which of these options best explains the data. To take into account the massive loss of genes following ancestral genome doubling, I developed a method to incorporate the defective gene sets into consideration and evaluate the effects of these lost genes on the reconstruction of the ancestor.

Utilizing high throughput gene expression data stored in public archives not only saves research time and cost but also enhances the power of its statistical support. However, gene expression profiling data can be obtained from many different technical platforms. Same gene expressions quantified by different platforms have different distributional properties, which makes the data integration across multiple platforms challenging. Several cross-platform normalization methods developed and tried to remove the differences caused by the platform discrepancy but they also remove the important biological signals as well. Zhang and Jiang (2015) introduced a new method focusing on eliminating platform effect among systematic effects by employing matched samples which are measured by different platforms for getting a benchmark model. Since the matched sample have no biological difference, their approach is robust to get rid of solely the platform effect. They showed that the new method performs better than Distance Weighted Discrimination (DWD) method. This paper is a follow-up study of their work and we attempt to improve the new method by incorporating Fast Linear Mixed Regression (FLMER) model. The result indicates that the FLMER model works better than the original proposed model, OLS (Ordinary Least Squares) model in after-normalization concordance comparison and Differential Expression(DE) analysis. Also, we compare our methods to other existing cross-platform normalization methods not only DWD but also Empirical Bayes methods, XPN and GQ methods. The results showed that the proposed method performs much better than other cross-platform normalization methods for removing platform differences and keeping the biological information. / A Dissertation submitted to the Department of Statistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester 2018. / October 15, 2018. / Cross-platform normalization / Includes bibliographical references. / Jinfeng Zhang, Professor Directing Dissertation; Qing-Xiang Amy Sang, University Representative; Wei Wu, Committee Member; Xu-Feng Niu, Committee Member.

Over 232,000 women will be diagnosed with breast cancer in 2014 in the United States, and approximately 40,000 women will die from this disease. Similarly, it is estimated that 230,000 men will be diagnosed with prostate cancer in the United States, and over 29,000 men will die from this disease. These figures make breast cancer and prostate cancer the two most diagnosed cancers in women and men, respectively, and they are both the second leading cause of cancer mortality in their respective genders. These alarming numbers show that we have a long way to go before finding a cure for cancer. Because cancer is a multifaceted disease, systems biology approaches provide excellent ways to fully appreciate and understand its complexity. The overall theme of this dissertation is the application of data analysis and bioinformatic techniques in order to gain insight to the signaling pathways involved in breast and prostate cancer. High-throughput genomics and proteomics allow for an unprecedented glimpse into the inner workings of biology, particularly in the case of cancer. These relatively inexpensive, high-throughput experiments have given rise to a glut of data that has not been thoroughly analyzed. This means that data analysis and bioinformatics techniques can be applied to large data sets in order to answer questions and unlock new directions in cancer research. Here, a comprehensive differential gene expression analysis and pathway analysis was performed using the breast cancer data from The Cancer Genome Atlas in order to understand health disparity in African American breast cancer. Furthermore, proteomics and phosphoproteomics experimental techniques were applied to better understand the protein expression differences and signaling pathways of an advanced metastatic prostate cancer cell model. Finally, data analysis of patient models for aggressive prostate cancer was performed in order to compare and contrast the differences with the advanced metastatic prostate cancer cell model. Attached to this manuscript is a zipped file containing supplementary tables. These supplementary tables support results presented in Chapters 2, 3, and 4. There are 13 supplementary tables in total. / A Dissertation submitted to the Department of Chemistry and Biochemistry in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester, 2014. / September 15, 2014. / bioinformatics, breast cancer, pathway analysis, prostate cancer, signaling networks, signaling pathways / Includes bibliographical references. / Qing-Xiang Sang, Professor Directing Thesis; Richard Bertram, University Representative; Oliver Steinbock, Committee Member; Wei Yang, Committee Member.

Recently, genome-wide surveys for non-coding RNAs have provided evidence for tens of thousands of previously undescribed evolutionary conserved RNAs with distinctive secondary structures. The annotation of these putative ncRNAs, however, remains a difficult problem. Here we describe an SVM-based approach that, in conjunction with a non-stringent filter for consensus secondary structures, is capable of efficiently recognizing microRNA precursors in multiple sequence alignments. The software was applied to recent genome-wide RNAz surveys of mammals, urochordates, and nematodes.

Background: A small class of RNA molecules, in particular the tiny genomes of viroids, are circular. Yet most structure prediction algorithms handle only linear RNAs. The most straightforward approach is to compute circular structures from ‘internal’ and ‘external’ substructures separated by a base pair. This is incompatible, however, with the memory-saving approach of the Vienna RNA Package which builds a linear RNA structure from shorter (internal) structures only. Result: Here we describe how circular secondary structures can be obtained without additional memory requirements as a kind of ‘post-processing’ of the linear structures.