Dense Matrices for Biofluids Applications

In this report, we focus on Biofluids problems, specifically the Stokes Equation. The method of regularized Stokeslets can be derived from bound- ary integral equations derived from the Lorentz reciprocal identity. When body forces are known, this is a direct numerical approximation of an in- tegral, resulting in a summation to determine the fluid velocity. In certain cases, which this report is focused on, we know the velocity and want to determine the forces on a structure immersed in a fluid. This results in a lin- ear system Af = u, where A is a square dense matrix. We study different methods to solve this system of equations to determine the force f on the structure. For solving a linear system with a dense coefficient matrix, the backslash command in MATLAB can be used. This will use an efficient and robust direct method for solving a smaller matrix, but this is not an efficient method for a large, dense coefficient matrix. For a large, dense coefficient ma- trix, we will explore other direct methods as well as several iterative methods to determine computation time and error on a test case with an exact solu- tion. For direct methods, we will study backslash, LU factorization and QR factorization methods. For iterative methods, we stuied Jacobi, Gauss-Seidel, SOR, GMRES, CG, CGS, BICGSTAB and Schulz CG methods for these bioflu- ids applications. All of these methods have different requirements. For our coefficient matrix A, we identified specific properties and then used proper methods, both direct and iterative. Result showed that iterative methods are more efficient then direct method for large size A. Schulz CG was slower but had a smaller error for the test case where there was an exact solution

Dolphin and whale: development, evaluation and application of novel bioinformatics tools for metabolite profiling in high throughput 1H-NMR analysis

El perfilat de metabòlits es la tasca més difícil dins l'anàlisi espectral de RMN. El seu objectiu es comprendre els processos biològics que tenen lloc en un moment concret mitjançant la identificació i quantificació dels metabòlits presents en mescles d' RMN complexes. Un espectre de RMN està compost per ressonàncies d'un gran nombre de metabòlits, i aquestes se solen solapar entre elles, canviar de posició depenent del pH de la mostra i poden quedar emmascarades per senyals de macromolècules. Tots aquests problemes compliquen la identificació i quantificació de metabòlits, pel que obtenir un perfil de metabòlits curat en una mostra pot ser un gran repte inclús per usuaris experts. En aquest context, la motivació d'aquesta tesi va néixer amb l'objectiu de donar automatismes i funcions fàcils de fer servir per al perfilat de metabòlits en RMN, millorant la qualitat dels resultats i reduint el temps d'anàlisi. Per fer-ho, es van implementar un conjunt d'algoritmes que van acabar empaquetats en dos programes, Dolphin i Whale.El perfilado de metabolitos es la tarea más difícil dentro del análisis espectral de RMN. Su objetivo es comprender los procesos biológicos que tienen lugar en un momento concreto a través de la identificación y cuantificación de los metabolitos presentes en mezclas de RMN complejas. Un espectro de RMN está compuesto por resonancias de un gran numero de metabolitos, y éstas a menudo se solapan entre ellas, cambian de posición dependiendo del pH de la muestra y pueden quedar enmascaradas por señales de macromoléculas. Todos estos problemas complican la identificación y cuantificación de metabolitos, por lo que obtener un perfilado de metabolitos curado en una muestra puede ser un gran reto incluso para usuarios expertos. En este contexto, la motivación de esta tesis nació con el objetivo de dar automatismos y funciones fáciles de usar para el perfilado de metabolitos en RMN, mejorando la calidad de los resultados y reduciendo el tiempo de análisis. Para hacerlo, se implementaron un conjunto de algoritmos que acabaron empaquetados en dos programas, Dolphin y Whale.Metabolite profiling is the most challenging approach in NMR spectral analysis. It aims to comprehend biological processes occurring in a certain moment through identifying and quantifying metabolites present in complex NMR mixtures. An NMR spectrum is composed by resonances of a huge number of metabolites, and these resonances often overlap between them, shift position depending on the sample pH and can be masked by macromolecules signals. All these drawbacks hinder metabolite identification and quantification, so obtaining a cured metabolite profile of a sample can be a very big issue even for expert users. In this context, the motivation of this thesis was born with the aim to provide automatisms and user-friendly interactive functions for NMR metabolite profiling, improving the quality of the results and reducing the time span of the analysis. To do so, several algorisms were implemented and embedded into two software packages, Dolphin and Whale

Passively parallel regularized stokeslets

Stokes flow, discussed by G.G. Stokes in 1851, describes many microscopic biological flow phenomena, including cilia-driven transport and flagellar motility; the need to quantify and understand these flows has motivated decades of mathematical and computational research. Regularized stokeslet methods, which have been used and refined over the past twenty years, offer significant advantages in simplicity of implementation, with a recent modification based on nearest-neighbour interpolation providing significant improvements in efficiency and accuracy. Moreover this method can be implemented with the majority of the computation taking place through built-in linear algebra, entailing that state-of-the-art hardware and software developments in the latter, in particular multicore and GPU computing, can be exploited through minimal modifications ('passive parallelism') to existing MATLAB computer code. Hence, and with widely-available GPU hardware, significant improvements in the efficiency of the regularized stokeslet method can be obtained. The approach is demonstrated through computational experiments on three model biological flows: undulatory propulsion of multiple C. Elegans, simulation of progression and transport by multiple sperm in a geometrically confined region, and left-right symmetry breaking particle transport in the ventral node of the mouse embryo. In general an order-of-magnitude improvement in efficiency is observed. This development further widens the complexity of biological flow systems that are accessible without the need for extensive code development or specialist facilities.Comment: 21 pages, 7 figures, submitte

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas

The art of coarse Stokes: Richardson extrapolation improves the accuracy and efficiency of the method of regularized stokeslets

The method of regularised stokeslets is widely used in microscale biological fluid dynamics due to its ease of implementation, natural treatment of complex moving geometries, and removal of singular functions to integrate. The standard implementation of the method is subject to high computational cost due to the coupling of the linear system size to the numerical resolution required to resolve the rapidly-varying regularised stokeslet kernel. Here we show how Richardson extrapolation with coarse values of the regularisation parameter is ideally-suited to reduce the quadrature error, hence dramatically reducing the storage and solution costs without loss of accuracy. Numerical experiments on the resistance and mobility problems in Stokes flow support the analysis, confirming several orders of magnitude improvement in accuracy and/or efficiency.Comment: 22 pages, 4 figure

Characterisation of xenometabolome signatures in complex biomatrices for enhanced human population phenotyping

Metabolic phenotyping facilitates the analysis of low molecular weight compounds in complex biological samples, with resulting metabolite profiles providing a window on endogenous processes and xenobiotic exposures. Accurate characterisation of the xenobiotic component of the metabolome (the xenometabolome) is particularly valuable when metabolic phenotyping is used for epidemiological and clinical population studies where exposure of participants to xenobiotics is unknown or difficult to control/estimate. Additionally, as metabolic phenotyping has increasingly been incorporated into toxicology and drug metabolism research, phenotyping datasets may be exploited to study xenobiotic metabolism at the population level. This thesis describes novel analytical and data-driven strategies for broadening xenometabolome coverage to allow effective partitioning of endogenous and xenobiotic metabolome signatures. The data driven strategy was multi-faceted, involving the generation of a reference database and the application of statistical methodologies. The database contains over 100 common xenobiotics profiles - generated using established liquid chromatography-mass-spectrometry methods – and provided the basis for an empirically derived screen for human urine and blood samples. The prevalence of these xenobiotics was explored in an exemplar phenotyping dataset (ALZ; n = 650; urine), with 31 xenobiotics detected in an initial screen. Statistical based methods were tailored to extract xenobiotic-related signatures and evaluated using drugs with well-characterised human metabolism. To complement the data-driven strategies for xenometabolome coverage, a more analytical based strategy was additionally developed. A dispersive solid phase extraction sample preparation protocol for blood products was optimised, permitting efficient removal of lipids and proteins, with minimal effect on low molecular weight metabolites. The suitability and reproducibility of this method was evaluated in two independent blood sample sets (AZstudy12; n=171, MARS; n=285). Finally, these analytical and statistical strategies were applied to two existing large-scale phenotyping study datasets: AIRWAVE (n = 3000 urine, n=3000 plasma samples) and ALZ (n= 650 urine, n= 449 serum) and used to explore both xenobiotic and endogenous responses to triclosan and polyethylene glycol exposure. Exposure to triclosan highlighted affected pathways relating to sulfation, whilst exposure to PEG highlighted a possible perturbation in the glutathione cycle. The analytical and statistical strategies described in this thesis allow for a more comprehensive xenometabolome characterisation and have been used to uncover previously unreported relationships between xenobiotic and endogenous metabolism.Open Acces

System-level metabolic effects of trematode infections in rodent models

Background: Trematode infections impose a great burden on the developing world by impairing life quality, productivity and life span of an individual. The prerequisite for efficient treatment and control of the diseases is the use of a quick and sensitive diagnostic tool which could replace the multi-diagnostic approach that is still used. The metabolic profiling approach implies the use of spectroscopic tools such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) on potentially interesting biofluids and tissues, and is coupled with multivariate mathematical data modelling. It has been applied to many different field of research, such as biochemistry, medical sciences, drug pathway discovery, non-communicable diseases, nutrition and psychological disorders, and has been established as an efficient method for biomarker recovery and pathway deciphering. There is growing interest in metabolic profiling in systems biology. The first attempt to bring together metabolic profiling with the epidemiology of neglected tropical diseases was in mid-2002, when urine samples were obtained from more than 500 individuals in a rural Western part of Côte d’Ivoire. However, it was impossible to extract any meaningful information with regard to specific parasitic infection. The high degree of metabolic variation of the studied population in terms of age, genetic and nutritional background and the fact that the majority of individuals harboured three or more parasites concurrently might explain this observation. My thesis outline was put forward once the metabolic profiles of selected parasitic infections in suitable rodent models, namely Schistosoma mansoni and Trypanosoma brucei brucei in the mouse, and Schistosoma japonicum in the hamster, were established as an alternative to directly exploring human populations in order to ascertain if characteristic biomarkers of infection could be found for single host-parasite scenarios. The success of these experimental investigations encouraged further studies, including the extension of metabolic profiling to other host-parasite models, in order to gain insight into specificity of biomarkers and to reveal the diagnostic potential of this metabolic profiling approach. Goal and objectives: The overarching goal of this Ph.D. project was to deepen our understanding of trematode-induced metabolic changes in selected rodent models, and to critically assess the potential of a metabolic profiling approach applied to biofluids and tissue samples for biomarker recovery that may contain diagnostic and prognostic properties. The specific objectives were (i) to optimise faecal sample preparation for subsequent 1H NMR spectroscopy, and to assess metabolic variation in faecal samples with regard to species (i.e. human, rat and mouse), gender and age, (ii) to assess longitudinally the biochemical changes in urine, plasma and faecal water of E. caproni-infected mice, and to compare the diagnostic capacity of different biofluids collected from infected and uninfected control mice, (iii) to gain information about E. caproni-induced changes in selected tissue samples e.g. (liver, kidney, spleen, ileum, jejunum and colon) of infected mice and correlate identified biomarkers with the previously extracted markers in the biofluids, which might reveal infection-related systems level changes (iv) to evaluate the remote and direct impact of three different trematodes (E. caproni, F. hepatica, S. mansoni) on the rodent host neural metabolic composition. Findings: Comparing the diagnostic templates, all three biofluids showed interesting deviations between uninfected control and E. caproni-infected mice. Urine and plasma were considered as most suitable biofluids due to the large number of potential biomarkers identified and because faecal water showed high fluctuations in the metabolic concentrations over time and a high degree of variation from one animal to another which was significantly higher than in urine and plasma. More detailed metabonomic investigations were performed with E. caproni to assess systems impact on the mouse host. Resulting changes in the metabolic profiles of biofluids and tissue samples were correlated with each other, and revealed new insights into the biological pathomechanisms of this trematode, e.g. impact on gut microbial species and a trematode-induced imbalance of the transporter system in the gut. Whereas E. caproni did not induce any biochemical changes in the neural profile, rats infected with F. hepatica, and mice infected with S. mansoni showed strong deviations from uninfected control animals. F. hepatica-induced changes in the rat brain nucleotide metabolism was correlated to certain cytokine levels, e.g. IFN-γ, IL-5 and IL-13, and was consistent with modulation of the immune mechanisms.This finding provides a rationale for deeper analysis into the interaction of parasitic worms with the central nervous system of the host organism

Metabonomic characterisation of the thoroughbred racehorse

Mammalian metabolism is known to be influenced by a number of physiological and environmental factors and the metabolic phenotype of an individual includes contributions from diet and the intestinal microbiota. Intestinal wellbeing is paramount for mammalian health and it is increasingly evident that intestinal bacteria have the ability to influence the development of an array of diseases. The horse is a hindgut fermenter- a sophisticated fermentation vat, housing a plethora of gut microbes that liberate energy from high cellulose diets. Investigating the horse will further enhance our knowledge of the symbiotic relationship between the mammalian host and its consortium of gut microbes. Plasma, urine and faecal biological matrices were explored using nuclear magnetic resonance spectroscopy to identify the dominant metabolites present in a healthy racehorse population. Multivariate statistics allowed differences in metabolic profiles to be analysed between horses and within individual horses. 106 metabolites were catalogued, providing a reference tool for ‘normal’ horse NMR data. Urine samples provided the highest percentage of gut microbial derived metabolites. 32 racehorses were subsequently longitudinally sampled to investigate sources of metabolic variation such as yard origin, exercise intensity and behavioural phenotype. Gut microbial co-metabolites; such as hippurate, quinate and p-cresol glucuronide were found to be significantly associated with a number of sources of variation. Equine oral stereotypical behaviour (EOS), abrupt dietary change and high-starch diets are risk factors for colic. Gut microbes can indirectly influence behaviour and it has been postulated that stereotypical abnormalities, such as autism and EOS could be related to changes in gut microbial composition and metabolism. Urinary quinate- a dietary and gut microbial co-metabolite was found to be significantly increased in horses that displayed crib-biting behaviour compared to matched controls. Metabolic profiles from biofluids of horses on a diet trial exploring 3 diets; a traditional high-starch racing diet; a high-fat alternative and a grass only diet highlighted significant differences in gut microbial metabolism. A grass only diet had the highest level of gut microbial co-metabolites such as hippurate in comparison to the other diets and the high-fat alternative was most similar to this ‘natural’ grass metabolome. Conversely, a high-starch diet was associated with higher faecal lactic acid levels, suggesting a shift in pH and therefore microbial environment.Open Acces