Dissertations / Theses on the topic 'Biological methods'

Thesis (Ph.D.)--Indiana University, Dept. of Mathematics, 2007.<br>Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6740. Adviser: Elizabeth A. Housworth. Title from dissertation home page (viewed May 20, 2008).

Lo scopo di questa tesi è di elaborare e analizzare metodi rigorosi dal punto di vista matematico per l’analisi di due tipi di dati biologici: dati relativi a pan-genomi e filogenesi. Con il termine “pan-genoma” si indica, in generale, un insieme di sequenze genomiche strettamente correlate (tipicamente appartenenti a individui della stessa specie) che si vogliano utilizzare congiuntamente come sequenze di riferimento per un’intera popolazione. Una filogenesi, invece, rappresenta le relazioni evolutive in un gruppo di entità, che siano esseri viventi, geni, lingue naturali, manoscritti antichi o cellule tumorali. Con l’eccezione di uno dei risultati presentati in questa tesi, relativo all’analisi di filogenesi tumorali, il taglio della dissertazione è prevalentemente teorico: lo scopo è studiare gli aspetti combinatori dei problemi affrontati, più che fornire soluzioni efficaci in pratica. Una conoscenza approfondita degli aspetti teorici di un problema, del resto, permette un'analisi matematicamente rigorosa delle soluzioni già esistenti, individuandone i punti deboli e quelli di forza, fornendo preziosi dettagli sul loro funzionamento e aiutando a decidere quali problemi vadano ulteriormente investigati. Oltretutto, è spesso il caso che nuovi risultati teorici (algoritmi, strutture dati o riduzioni ad altri problemi più noti) si possano direttamente applicare o adattare come soluzione ad un problema pratico, o come minimo servano ad ispirare lo sviluppo di nuovi metodi efficaci in pratica. La prima parte della tesi è dedicata a nuovi metodi per eseguire delle operazioni fondamentali su un testo elastico-degenerato, un oggetto computazionale che codifica in maniera compatta un insieme di testi simili tra loro, come, ad esempio, un pan-genoma. Nello specifico, si affrontano il problema di cercare una sequenza di lettere in un testo elastico-degenerato, sia in maniera esatta che tollerando un numero prefissato di errori, e quello di confrontare due testi degenerati. Nella seconda parte si considerano sia filogenesi tumorali, che ricostruiscono per l'appunto l'evoluzione di un tumore, sia filogenesi "classiche", che rappresentano, ad esempio, la storia evolutiva delle specie viventi. In particolare, si presentano nuove tecniche per confrontare due o più filogenesi tumorali, necessarie per valutare i risultati di diversi metodi che ricostruiscono le filogenesi stesse, e una nuova e più efficiente soluzione a un problema di lunga data relativo a filogenesi "classiche", consistente nel determinare se sia possibile sistemare, in presenza di dati mancanti, un insieme di specie in un albero filogenetico che abbia determinate proprietà.<br>The main goal of this thesis is to develop new algorithmic frameworks to deal with (i) a convenient representation of a set of similar genomes and (ii) phylogenetic data, with particular attention to the increasingly accurate tumor phylogenies. A “pan-genome” is, in general, any collection of genomic sequences to be analyzed jointly or to be used as a reference for a population. A phylogeny, in turn, is meant to describe the evolutionary relationships among a group of items, be they species of living beings, genes, natural languages, ancient manuscripts or cancer cells. With the exception of one of the results included in this thesis, related to the analysis of tumor phylogenies, the focus of the whole work is mainly theoretical, the intent being to lay firm algorithmic foundations for the problems by investigating their combinatorial aspects, rather than to provide practical tools for attacking them. Deep theoretical insights on the problems allow a rigorous analysis of existing methods, identifying their strong and weak points, providing details on how they perform and helping to decide which problems need to be further addressed. In addition, it is often the case where new theoretical results (algorithms, data structures and reductions to other well-studied problems) can either be directly applied or adapted to fit the model of a practical problem, or at least they serve as inspiration for developing new practical tools. The first part of this thesis is devoted to methods for handling an elastic-degenerate text, a computational object that compactly encodes a collection of similar texts, like a pan-genome. Specifically, we attack the problem of matching a sequence in an elastic-degenerate text, both exactly and allowing a certain amount of errors, and the problem of comparing two degenerate texts. In the second part we consider both tumor phylogenies, describing the evolution of a tumor, and “classical” phylogenies, representing, for instance, the evolutionary history of the living beings. In particular, we present new techniques to compare two or more tumor phylogenies, needed to evaluate the results of different inference methods, and we give a new, efficient solution to a longstanding problem on “classical” phylogenies: to decide whether, in the presence of missing data, it is possible to arrange a set of species in a phylogenetic tree that enjoys specific properties.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2014.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>169<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 153-170).<br>Biological systems are extremely complex, and our ability to experimentally measure interactions in these systems is limited by inherent noise. Technological advances have allowed us to collect unprecedented amounts of raw data, increasing the need for computational methods to disentangle true interactions from noise. In this thesis, we focus on statistical methods to infer two classes of important biological interactions: protein-protein interactions and the link between genotypes and phenotypes. In the first part of the thesis, we introduce methods to infer protein-protein interactions from affinity purification mass spectrometry (AP-MS) and from luminescence-based mammalian interactome mapping (LUMIER). Our work reveals novel context dependent interactions in the MAPK signaling pathway and insights into the protein homeostasis machinery. In the second part, we focus on methods to understand the link between genotypes and phenotypes. First, we characterize the effects of related individuals on standard association statistics for genome-wide association studies (GWAS) and introduce a new statistic that corrects for relatedness. Then, we introduce a statistically powerful association testing framework that corrects for confounding from population structure in large scale GWAS. Lastly, we investigate regularized regression for phenotype prediction from genetic data.<br>by George Jay Tucker.<br>Ph. D.

Les processus biologiques sont des phénomènes complexes, multi-échelles, présentant une hétérogénéité importante à travers l’espace, la structure et la fonction. De plus, ils impliquent des événements fortement corrélés et présentent des boucles de rétroaction à travers les échelles. Dans cette thèse, nous utilisons des représentations spatio-structuro-temporelles en grande dimension pour étudier l'hétérogénéité biologique à travers l'espace, la fonction biologique et le temps, et appliquons cette méthode à divers problèmes importants en biologie et en clinique.Nous commençons par introduire un nouveau cadre spatio-structuro-temporel, basé sur équations aux dérivées partielles, pour le cas d’un système biologique dont la fonction dépend de la dynamique dans le temps et l’espace des récepteurs membranaires, des ligands et du métabolisme. Afin d’étudier les solutions de ces équations, nous utilisons un schéma numérique de différences finies ainsi que divers résultats analytiques. Pour tester la validité de nos approches numériques nous prouvons un théorème sur la stabilité de notre schéma.Le cancer est un problème croissant pour la population mondiale, car ses taux d'incidence et sa résistance aux médicaments augmentent. D’abord nous modélisons l’invasion du cancer du sein agressif via sa capacité à produire des enzymes dégradant la matrice extracellulaire, et nous montrons la génération de structures spatiales anatomo-pathologiques difficiles à enlever par la chirurgie. Ensuite, nous développons des modèles mathématiques de tumeurs résistantes au traitement et appliquons ces modèles à la résistance aux thérapies ciblées (inhibiteurs de BRAF et de MEK) du mélanome cutané. Nous constatons que les tumeurs développent une résistance à la fois à travers des processus d'adaptations génétiques ou par le remodelage de leur métabolisme, mais montrons que seules les tumeurs métaboliquement plastiques manifestent une re-sensibilisation à ces thérapies. Enfin, via une approche basée sur des données d’expression en cellule unique (RNA-seq), nous montrons que la dynamique spatiale contribue à l'hétérogénéité tumorale et à la résistante aux traitements de façon liée au statut prolifératif des cellules cancéreuses.Nous appliquons nos méthodes à deux autres systèmes. Dans le contexte de la réponse immunitaire à l’infection virale, nous étudions la production et la dynamique spatiale de l’interféron (IFN) et l’apparent paradoxe de la conservation de molécules d’IFN avec affinités faibles et fortes. Nous constatons que les molécules IFN de faible affinité sont plus capables de se propager dans l'espace, alors que les molécules de haute affinité sont capables de maintenir le signal localement. L’addition de ligands de faible affinité à un système ne comprenant que des ligands de moyenne ou grande affinité peut entraîner une diminution de la charge virale d’environ 23%. Ensuite, nous explorons le contexte de la sélection sexuelle de l'apparence masculine dans l'évolution darwinienne. Nous constatons que les systèmes biologiques conservent les traits sélectionnés sexuellement, même si cela entraîne une diminution générale de la population.Enfin, nous introduisons deux autres techniques de modélisation: pour augmenter la dimensionnalité de notre approche, nous développons une approche pseudo-spectrale basée sur les polynômes de Chebyshev et l’appliquons au même scénario de résistance aux médicaments phénotypiques que ci-dessus. Ensuite, pour étudier un scénario coopératif dans lequel des cellules cancéreuses prolifératives et invasives sont co-injectées, induisant des comportements invasifs dans les cellules prolifératives, nous développons une nouvelle méthode de simulation combinant des automates cellulaires et systèmes d’agents. Nous trouvons que cette méthode est capable de reproduire les résultats de l'expérience de coinjection et d'autres expériences dans lesquelles des cellules ont été placées dans des micropistes de collagène<br>Biological processes are complex, multi-scale phenomena displaying extensive heterogeneity across space, structure, and function. Moreover, these events are highly correlated and involve feedback loops across scales, with nuclear transcription being effected by protein concentrations and vice versa, presenting a difficulty in representing these through existing mathematical approaches. In this thesis we use higher-dimensional spatio-structuro-temporal representations to study biological heterogeneity through space, biological function, and time and apply this method to various scenarios of significance to the biological and clinical communities.We begin by deriving a novel spatio-structuro-temporal, partial differential equation framework for the general case of a biological system whose function depends upon dynamics in time, space, surface receptors, binding ligands, and metabolism. In order to simulate solutions for this system, we present a numerical finite difference scheme capable of this and various analytic results connected with this system, in order to clarify the validity of our predictions. In addition to this, we introduce a new theorem establishing the stability of the central differences scheme.Despite major recent clinical advances, cancer incidence continues to rise and resistance to newly synthesised drugs represents a major health issue. To tackle this problem, we begin by investigating the invasion of aggressive breast cancer on the basis of its ability to produce extracellular matrix degrading enzymes, finding that the cancer produced a surgically challenging morphology. Next, we produce a novel structure in which models of cancer resistance can be established and apply this computational model to study genetic and phenotypic modes of resistance and re-sensitisation to targeted therapies (BRAF and MEK inhibitors). We find that both genetic and phenotypic heterogeneity drives resistance but that only the metabolically plastic, phenotypically resistant, tumour cells are capable of manifesting re-sensitisation to these therapies. We finally use a data-driven approach for single-cell RNA-seq analysis and show that spatial dynamics fuel tumour heterogeneity, contributing to resistance to treatment accordingly with the proliferative status of cancer cells.In order to expound this method, we look at two further systems: To investigate a case where cell-ligand interaction is particularly important, we take the scenario in which interferon (IFN) is produced upon infection of the cell by a virus and ask why biological systems evolve and retain multiple different affinities of IFN. We find that low affinity IFN molecules are more capable of propagating through space; high affinity molecules are capable of sustaining the signal locally; and that the addition of low affinity ligands to a system with only medium or high affinity ligands can lead to a ~23% decrease in viral load. Next, we explore the non-spatial, structuro-temporal context of male elaboration sexual and natural selection in Darwinian evolution. We find that biological systems will conserve sexually selected traits even in the event where this leads to an overall population decrease, contrary to natural selection.Finally, we introduce two further modelling techniques: To increase the dimensionality of our approach, we develop a pseudo-spectral Chebyshev polynomial-based approach and apply this to the same scenario of phenotypic drug resistance as above. Next, to deal with one scenario in which proliferative and invasive cancer cells are co-injected, inducing invasive behaviours in the proliferative cells, we develop a novel agent-based, cellular automaton method and associated analytic theorems for generating numerical solutions. We find that this method is capable of reproducing the results of the co-injection experiment and further experiments, wherein cells migrate through artificially produced collagen microtracks

L'alignement de séquences biologiques est une technique fondamentale en bioinformatique, et consiste à identifier des séries de caractères similaires (conservés) qui apparaissent dans le même ordre dans les deux séquences, et à inférer un ensemble de modifications (substitutions, insertions et suppressions) impliquées dans la transformation d'une séquence en l'autre. Cette technique permet de déduire, sur la base de la similarité de séquence, si deux ou plusieurs séquences biologiques sont potentiellement homologues, donc si elles partagent un ancêtre commun, permettant ainsi de mieux comprendre l'évolution des séquences. Cette thèse aborde les problèmes de comparaison de séquences dans deux cadres différents: la détection d'homologies et le séquençage à haut débit. L'objectif de ce travail est de développer des méthodes d'alignement qui peuvent apporter des solutions aux deux problèmes suivants: i) la détection d'homologies cachées entre des protéines par comparaison de séquences protéiques, lorsque la source de leur divergence sont les mutations qui changent le cadre de lecture, et ii) le mapping de reads SOLiD (séquences de di-nucléotides chevauchantes codés par des couleurs) sur un génome de référence. Dans les deux cas, la même idée générale est appliquée: comparer implicitement les séquences d'ADN pour la détection de changements qui se produisent à ce niveau, en manipulant, en pratique, d'autres représentations (séquences de protéines, séquences de codes di-nucléotides) qui fournissent des informations supplémentaires et qui aident à améliorer la recherche de similarités. Le but est de concevoir et d'appliquer des méthodes exactes et heuristiques d'alignement, ainsi que des systemes de scores, adaptés à ces scénarios<br>Biological sequence alignment is a fundamental technique in bioinformatics, and consists of identifying series of similar (conserved) characters that appear in the same order in both sequences, and eventually deducing a set of modifications (substitutions, insertions and deletions) involved in the transformation of one sequence into the other. This technique allows one to infer, based on sequence similarity, if two or more biological sequences are potentially homologous, i.e. if they share a common ancestor, thus enabling the understanding of sequence evolution.This thesis addresses sequence comparison problems in two different contexts: homology detection and high throughput DNA sequencing. The goal of this work is to develop sensitive alignment methods that provide solutions to the following two problems: i) the detection of hidden protein homologies by protein sequence comparison, when the source of the divergence are frameshift mutations, and ii) mapping short SOLiD reads (sequences of overlapping di-nucleotides encoded as colors) to a reference genome. In both cases, the same general idea is applied: to implicitly compare DNA sequences for detecting changes occurring at this level, while manipulating, in practice, other representations (protein sequences, sequences of di-nucleotide codes) that provide additional information and thus help to improve the similarity search. The aim is to design and implement exact and heuristic alignment methods, along with scoring schemes, adapted to these scenarios

Filtration is the most widely utilised air quality control technique used in a Heating, Ventilating and Air Conditioning (HVAC) duct systems in an indoor environment. Biological particles may be deposited onto, and re-aerosolized from, the filter surface, however, a range of variables such as types of microorganisms, filter characteristics, humidity, temperature and others factors can contribute to re-aerosolization. In this study, new methods are investigated to remove or inactivate the microorganism in the simulated HVAC system and controlled environment by adding various technologies to the current filtration systems. Two new technologies were tested; the first, emitting ions in the testing chamber, and the second, coating tea tree oil (TTO) onto the fibrous filter surface, in order to determine the most efficient and cost effective method for airborne biological particle control. Two commercial low-efficiency HVAC filters were tested in this investigation using a number of monodisperse polystyrene latex (PSL) spheres with diameters 0.5, 0.8, 1.0, and 1.5 μm. An influence of air ionization on the filtration process was investigated by running of an ionizer that was placed at a range of distances apart from the filter (5, 15, 25, and 45cm). The results of this investigation show that the current theoretical model underestimates the efficiencies of the particle deposition onto the filter fibres. However, this could be explained by the fact that some incoming particles are repelled from the filter, due to repelling electrostatic forces caused by unipolar ions captured by the filter. This investigation concluded that emitted air ions enhance the filtration efficiency, but found that the efficiency depends upon the filter type and the distance from the ion emitter to the filter surfaces. This investigation has provided strong evidence that ionization enhances the filtration efficiency, and that there are significant enhancements when the ion emitter is operated at 5 cm upstream of the filter. The results of this study illustrate that the continuous emission of negative ions in the vicinity of a low-efficiency HVAC filter significantly enhances its performance of removal airborne biological particles including bacteria, fungal spores and virus in the ambient air environment. Finally, bacteria (B. subtilis, E. coli and P. fluorescens) and fungal spores (Rhizopus stolonifer and A. niger) were examined by coating the filter fibres with biologically active TTO and biologically neutral light mineral oil (MO). It was found experimentally that pre-coating of the filter fibres with TTO and then using this filter for bioaerosols control, resulted in rapid inactivation of captured microorganisms and minimising a number of viable particles possibly blown off from the filter by the air. It was shown that 90 percent of the robust B. subtilis bacterial strain was inactivated during 30 minutes of the technology operation. Furthermore, the inactivation efficiency of some more sensitive bacterial aerosols of E. coli and P. fluorescens were found to be above 99 percent for the same time interval of 30 minutes. Fungal strains were found to be more robust with only 52-54 percent inactivation achieved over 60 minutes experimental run.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>Griffith School of Engineering<br>Science, Environment, Engineering and Technology<br>Full Text

Biological systems are complex in that they comprise large number of interacting entities, and their dynamics follow mechanic regulations for movement and biological function organization. Established computational modeling deals with studying and manipulating biologically relevant systems as a powerful approach. Inner structure and behavior of complex biological systems can be analyzed and understood by computable biological networks. In this thesis, models and computation methods are proposed for biological networks. The study of Genetic Regulatory Networks (GRNs) is an important research topic in genomic research. Several promising techniques have been proposed for capturing the behavior of gene regulations in biological systems. One of the promising models for GRNs, Boolean Network (BN) has gained a lot of attention. However, little light has been shed on the analysis of internal connection between the dynamics of biological molecules and network systems. Inference and completion problems of a BN from a given set of singleton attractors are considered to be important in understanding the relationship between dynamics of biological molecules and network systems. Discrete dynamic systems model has been recently proposed to model time-course microarray measurements of genes, but delay effect may be modeled as a realistic factor in studying GRNs. A delay discrete dynamic systems model is developed to model GRNs. Inference and analysis of networks is one of the grand challenges in modern statistical biology. Machine learning method, in particular, Support Vector Machine (SVM), has been successfully applied in predictions of internal connections embedded in networks. Kernels in conjunction with SVM demonstrate strong ability in performing various tasks such as biomedical diagnosis, function prediction and motif extractions. In biomedical diagnosis, data sets are always high dimensional which provide a challenging research problem in machine learning area. Novel kernels using distance-metric that are not common in machine learning framework are proposed for possible tumor differentiation discrimination problem. Protein function prediction problem is a hot topic in bioinformatics. The K-spectrum Kernel is among the top popular models in description of protein sequences. Taking into consideration of positive-semi-definiteness in kernel construction, Eigen-matrix translation technique is introduced in novel kernel formulation to give better prediction result. In a further step, power of Eigen-matrix translation technique in feature selection is demonstrated through mathematical formulation. Due to structure complexity of carbohydrates, the study of carbohydrate sugar chains has lagged behind compared to that of DNA and proteins. A weighted q-gram kernel is constructed in classifying glycan structures with limitations in feature extractions. A biochemically-weighted tree kernel is then proposed to enhance the ability in both classification as well as motif extractions. Finally the problem of metabolite biomarker discovery is researched. Human diseases, in particular metabolic diseases, can be directly caused by the lack of essential metabolites. Identification of metabolite biomarkers has significant importance in the study of biochemical reaction and signaling networks. A promising computational approach is proposed to identify metabolic biomarkers through integrating biomedical data and disease-specific gene expression data.<br>published_or_final_version<br>Mathematics<br>Doctoral<br>Doctor of Philosophy

Analyses based upon rank correlation methods, such as Spearman's Rho and Kendall's Tau, can provide quick insights into large biological data sets. Comparing expression levels between different technologies and models is problematic due to the different units of measure. Here again, rank correlation provides an effective means of comparison between the two techniques. Massively Parallel Signature Sequencing (MPSS) transcript abundance levels to microarray signal intensities for Arabidopsis thaliana are compared. Rank correlations can be applied to subsets as well as the entire set. Results of subset comparisons can be used to improve the capabilities of predictive models, such as Predicted Highly Expressed (PHX). This is done for Escherichia coli. Methods are given to combine predictive models based upon feedback from experimental data. The problem of feature selection in supervised learning situations is also considered, where all features are drawn from a common domain and are best interpreted via ordinal comparisons with other features, rather than as numerical values. This is done for synthetic data as well as for microarray experiments examining the life cycle of Drosophila melanogaster and human leukemia cells. Two novel methods are presented based upon Rho and Tau, and their efficacy is tested with synthetic and real world data. The method based upon Spearman's Rho is shown to be more effective.<br>Ph. D.

Often in biology, rare individuals within a population dominate the population’s overall behavior, and we wish to extract those individuals for further analysis. We design a sorting instrument as a flexible platform for the development of novel microfluidic sorting techniques. We demonstrate a microfluidic cell sorter, which screens cells at rates approaching those of commercially-available fluorescence-activated cell sorters. This device incorporates a three-dimensional flow-focusing nozzle with a slanted ceiling groove to enhance the capabilities of a surface acoustic wave (SAW) transducer by harnessing the component of the SAW oriented normal to the plane of the substrate. The device achieves sorting at a rate of 9000 events/s with 54% purity and yields 89% purity, while operating at 1000 events/s; this level of performance approaches that of a FACS operating in its high-purity mode. We also present a rare event sorting technique, which can successfully extract desired droplets from a sample containing nearly a billion droplets. The technique yields pure samples after two rounds. The preliminary round is fast, capable of screening 10 ml of droplets at 100 ml/h, but each sort also captures many other droplets together with the droplet of interest. The second round of sorting enriches the sample to nearly 100% purity, using known designs for high purity drop-by-drop sorting. Thus, we devise a method, which can sort droplets rapidly and achieve high purity of few droplets from samples containing large numbers of droplets. The two disparate approaches to microfluidic sorting use a common platform to create new methods for sorting with biological applications.<br>Engineering and Applied Sciences - Engineering Sciences

The cell is a complex system. In this system, the different layers of biological information establish complex links converging in the space of functions; processes and pathways talk each other defining cell types and organs. In the space of biological functions, this lead to a higher order of “emergence”, greater than the sum of the single parts, defining a biological entity a complex system. The introduction of omic techniques has made possible to investigate the complexity of each biological layer. With the different technologies we can have a near complete readout of the different biomolecules. However, it is only through data integration that we can let emerge and understand biological complexity. Given the complexity of the problem, we are far from having fully understood and developed exhaustive computational methods. Thus, this make urgent the exploration of biological complexity through the implementation of more powerful tools relying on new data and hypotheses. To this aim, Bioinformatics and Computational Biology play determinant roles. The present thesis describes computational methods aimed at deciphering biological complexity starting from genomic, interactomic, metabolomic and functional data. The first part describes NET-GE, a network-based gene enrichment tool aimed at extracting biological functions and processes of a set of gene/proteins related to a phenotype. NET-GE exploits the information stored in biological networks to better define the biological events occurring at gene/protein level. The first part describes also eDGAR, a database collecting and organizing gene-disease associations. The second part deals with metabolomics. I describe a new way to perform metabolite enrichment analysis: the metabolome is explored by exploiting the features of an interactome. The third part describes the methods and results obtained in the CAGI experiment, a community experiment aimed at assessing computational methods used to predict the impact of genomic variation on a phenotype.

<p>The work in this dissertation presents microfluidic methods developed for the study of biological dynamics. The requirements for the methods development was to create approaches with the ability to perform dynamic cell stimulation, measurement, and sample preparation. The methods presented herein were initially developed for the study of pancreatic islet biology but are expected to be translatable to other applications. In another study, a method to interface transmission electron microscopy (TEM) with microfluidics methods was developed. </p><p> The primary biological topic of interest investigated was the mechanisms of inter-islet synchronization. To test this, a microfluidic device fabricated from poly(dimethylsiloxane) (PDMS) was used to culture and stimulate pancreatic islets. Intracellular calcium ([Ca²⁺]i) imaging was performed with a fluorescent indicator, Fura-2-acetoxymethyl ester (Fura-2 AM). Under constant glucose (11 mM), islets demonstrated asynchronous and heterogeneous [Ca²⁺]i oscillations that drifted in period. However, when exposed to a glucose wave (11+/&ndash; 1 mM, 5 min period) islets were entrained to a common and consistent [Ca²⁺]i oscillation mode. The effect of islet entrainment on cellular function was investigated by measuring gene expression levels with microarray profiling. Calcium-dependent genes were found to be differentially expressed. Furthermore, it was speculated that islet entrained produced a beneficial effect on cell function and upkeep. </p><p> While [Ca²⁺]i imaging is an acceptable proxy measurement for insulin, it is not a viable reporter for other islet peptides and direct measurement is desired. Electrophoretic affinity assays can be performed on a microfluidic device in a serial manner to measure peptide release from an on-chip cell culture in near real-time. Successful analysis of electrophoretic affinity assays depends strongly on the preservation of the affinity complex during separations. Elevated separation temperatures due to Joule heating promotes complex dissociation leading to a reduction in sensitivity. To address this limitation, a method to cool a glass microfluidic chip for performing an affinity assay for insulin was achieved by a Peltier cooler localized over the separation channel. The Peltier cooler allowed for rapid stabilization of temperatures, with 21&nbsp;&deg;C the lowest temperature that was possible to use without producing detrimental thermal gradients throughout the device. Kinetic capillary electrophoresis analysis was utilized as a diagnostic of the affinity assay and indicated that optimal conditions were at the highest attainable separation voltage, 6&nbsp;kV, and the lowest separation temperature, 21&nbsp;&deg;C, leading to 3.4% dissociation of the complex peak during the separation. These optimum conditions were used to generate a calibration curve and produced 1&nbsp;nM limits of detection (LOD), representing a 10-fold improvement over non-thermostated conditions. </p><p> To date, most approaches for measurement of rapid changes in insulin levels rely on separations, making the assays difficult to translate to non-specialist laboratories. To enable rapid measurements of secretion dynamics from a single islet in a manner that will be more suitable for transfer to non-specialized laboratories, a microfluidic online fluorescence anisotropy immunoassay was developed. A single islet was housed inside a microfluidic chamber and stimulated with varying glucose levels from a gravity-based perfusion system. The total effluent of the islet chamber containing the islet secretions was mixed with gravity-driven solutions of insulin antibody and cyanine-5 (Cy5) labeled insulin. After mixing was complete, a linearly polarized 635 nm laser was used to excite the immunoassay mixture and the emission was split into parallel and perpendicular components for determination of anisotropy. Key factors for reproducible anisotropy measurements, including temperature homogeneity and flow rate stability were optimized, which resulted in a 4 nM LOD for insulin with &lt; 1% RSD of anisotropy values. The capability of this system for measuring insulin secretion from single islets was shown by stimulating an islet with varying glucose levels. As the entire analysis is performed optically, this system should be readily transferable to other laboratories. </p><p> To increase the number of analytes that can be simultaneously monitored by a fluorescence anisotropy immunoassay, frequency encoding was introduced. As a demonstration of the method, simultaneous competitive immunoassays for insulin and glucagon were performed by measuring the ratio of bound and free Cy5-insulin and fluorescein isothiocyanate (FITC)-glucagon in the presence of their respective antibodies. A vertically polarized 635 nm laser was pulsed at 73 Hz and used to excite Cy5-insulin, while a vertically polarized 488 nm laser pulsed at 137 Hz excited FITC-glucagon. The total emission was split into parallel and perpendicular polarizations and collected onto separate photomultiplier tubes. The signals from each channel were demodulated using a fast Fourier transform, resolving the contributions from each fluorophore. Anisotropy calculations were carried out using the magnitude of the peaks in the frequency domain. The method produced the expected shape of the calibration curves with LOD of 0.6 and 5 nM for insulin and glucagon, respectively. (Abstract shortened by ProQuest.) </p><p>

Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2004.<br>Includes bibliographical references (p. 98-108).<br>The costs to produce and utilize hydrogen are extremely high per unit of energy when compared to fossil fuel energy sources such as natural gas or gasoline. The cheapest hydrogen production approaches today are also the most polluting, as they use fossil fuels in even more inefficient ways than cars do. Renewable approaches to hydrogen production are- at best- three times more expensive per unit energy than the cost to produce the same amount of natural gas. The production of hydrogen through biological systems is one area of particularly promising research. There are countless biological systems that produce energy from sunlight, and countless others that produce energy from the metabolism of organic molecules such as glucose. Many microbial organisms produce hydrogen under certain conditions. Optimizing their innate ability to produce hydrogen and developing biohydrogen plants whose economics compete with current commercial plants are key hurdles that must be overcome. Economic models for the production of hydrogen through biological systems are examined in detail in this thesis. The key technical hurdles which drive the capital and production costs are identified. Fruitful areas of potential research are suggested to bring biological hydrogen production to commercial scale as rapidly as possible.<br>by Richard J. Resnick.<br>S.M.M.O.T.

With the advent of modern sequencing technologies massive amounts of biological data, from protein sequences to entire genomes, are becoming increasingly available. This poses the need for the automatic analysis and classification of such a huge collection of data, in order to enhance knowledge in the Life Sciences. Although many research efforts have been made to mathematically model this information, for example finding patterns and similarities among protein or genome sequences, these approaches often lack structures that address specific biological issues. In this thesis, we present novel computational methods for three fundamental problems in molecular biology: the detection of remote evolutionary relationships among protein sequences, the identification of subtle biological signals in related genome or protein functional sites, and the phylogeny reconstruction by means of whole-genome comparisons. The main contribution is given by a systematic analysis of patterns that may affect these tasks, leading to the design of practical and efficient new pattern discovery tools. We thus introduce two advanced paradigms of pattern discovery and filtering based on the insight that functional and conserved biological motifs, or patterns, should lie in different sites of sequences. This enables to carry out space-conscious approaches that avoid a multiple counting of the same patterns. The first paradigm considered, namely irredundant common motifs, concerns the discovery of common patterns, for two sequences, that have occurrences not covered by other patterns, whose coverage is defined by means of specificity and extension. The second paradigm, namely underlying motifs, concerns the filtering of patterns, from a given set, that have occurrences not overlapping other patterns with higher priority, where priority is defined by lexicographic properties of patterns on the boundary between pattern matching and statistical analysis. We develop three practical methods directly based on these advanced paradigms. Experimental results indicate that we are able to identify subtle similarities among biological sequences, using the same type of information only once. In particular, we employ the irredundant common motifs and the statistics based on these patterns to solve the remote protein homology detection problem. Results show that our approach, called Irredundant Class, outperforms the state-of-the-art methods in a challenging benchmark for protein analysis. Afterwards, we establish how to compare and filter a large number of complex motifs (e.g., degenerate motifs) obtained from modern motif discovery tools, in order to identify subtle signals in different biological contexts. In this case we employ the notion of underlying motifs. Tests on large protein families indicate that we drastically reduce the number of motifs that scientists should manually inspect, further highlighting the actual functional motifs. Finally, we combine the two proposed paradigms to allow the comparison of whole genomes, and thus the construction of a novel and practical distance function. With our method, called Unic Subword Approach, we relate to each other the regions of two genome sequences by selecting conserved motifs during evolution. Experimental results show that our approach achieves better performance than other state-of-the-art methods in the whole-genome phylogeny reconstruction of viruses, prokaryotes, and unicellular eukaryotes, further identifying the major clades of these organisms.<br>Con l'avvento delle moderne tecnologie di sequenziamento, massive quantità di dati biologici, da sequenze proteiche fino a interi genomi, sono disponibili per la ricerca. Questo progresso richiede l'analisi e la classificazione automatica di tali collezioni di dati, al fine di migliorare la conoscenza nel campo delle Scienze della Vita. Nonostante finora siano stati proposti molti approcci per modellare matematicamente le sequenze biologiche, ad esempio cercando pattern e similarità tra sequenze genomiche o proteiche, questi metodi spesso mancano di strutture in grado di indirizzare specifiche questioni biologiche. In questa tesi, presentiamo nuovi metodi computazionali per tre problemi fondamentali della biologia molecolare: la scoperta di relazioni evolutive remote tra sequenze proteiche, l'individuazione di segnali biologici complessi in siti funzionali tra loro correlati, e la ricostruzione della filogenesi di un insieme di organismi, attraverso la comparazione di interi genomi. Il principale contributo è dato dall'analisi sistematica dei pattern che possono interessare questi problemi, portando alla progettazione di nuovi strumenti computazionali efficaci ed efficienti. Vengono introdotti così due paradigmi avanzati per la scoperta e il filtraggio di pattern, basati sull'osservazione che i motivi biologici funzionali, o pattern, sono localizzati in differenti regioni delle sequenze in esame. Questa osservazione consente di realizzare approcci parsimoniosi in grado di evitare un conteggio multiplo degli stessi pattern. Il primo paradigma considerato, ovvero irredundant common motifs, riguarda la scoperta di pattern comuni a coppie di sequenze che hanno occorrenze non coperte da altri pattern, la cui copertura è definita da una maggiore specificità e/o possibile estensione dei pattern. Il secondo paradigma, ovvero underlying motifs, riguarda il filtraggio di pattern che hanno occorrenze non sovrapposte a quelle di altri pattern con maggiore priorità, dove la priorità è definita da proprietà lessicografiche dei pattern al confine tra pattern matching e analisi statistica. Sono stati sviluppati tre metodi computazionali basati su questi paradigmi avanzati. I risultati sperimentali indicano che i nostri metodi sono in grado di identificare le principali similitudini tra sequenze biologiche, utilizzando l'informazione presente in maniera non ridondante. In particolare, impiegando gli irredundant common motifs e le statistiche basate su questi pattern risolviamo il problema della rilevazione di omologie remote tra proteine. I risultati evidenziano che il nostro approccio, chiamato Irredundant Class, ottiene ottime prestazioni su un benchmark impegnativo, e migliora i metodi allo stato dell'arte. Inoltre, per individuare segnali biologici complessi utilizziamo la nozione di underlying motifs, definendo così alcune modalità per il confronto e il filtraggio di motivi degenerati ottenuti tramite moderni strumenti di pattern discovery. Esperimenti su grandi famiglie proteiche dimostrano che il nostro metodo riduce drasticamente il numero di motivi che gli scienziati dovrebbero altrimenti ispezionare manualmente, mettendo in luce inoltre i motivi funzionali identificati in letteratura. Infine, combinando i due paradigmi proposti presentiamo una nuova e pratica funzione di distanza tra interi genomi. Con il nostro metodo, chiamato Unic Subword Approach, relazioniamo tra loro le diverse regioni di due sequenze genomiche, selezionando i motivi conservati durante l'evoluzione. I risultati sperimentali evidenziano che il nostro approccio offre migliori prestazioni rispetto ad altri metodi allo stato dell'arte nella ricostruzione della filogenesi di organismi quali virus, procarioti ed eucarioti unicellulari, identificando inoltre le sottoclassi principali di queste specie.

Cette thèse est consacrée à l'analyse qualitative des systèmes biologiques, modélisés comme des systèmes d'équations différentielles ou d'équations aux différences, en utilisant des méthodes algébriques. Nous avons étudié les problèmes de la détection d’états d’équilibre, de l'analyse de stabilité et de différents types de bifurcations, et de la construction de cycles limites pour des modèles biologiques continus et discrets. Nous montrons comment réduire les problèmes de l'analyse qualitative aux problèmes de résolution de systèmes polynomiaux ou semi-algébriques. Ensuite, nous expliquons comment ces problèmes formulés peuvent être résolus en utilisant une approche algébrique basée sur les méthodes d'ensembles triangulaires, des bases de Gröbner, d’élimination des quantificateurs, et de l’isolement et la classification des solutions réelles. De nombreuses expériences ont été réalisées sur les modèles biologiques différents et certains d'entre eux ont été présentés dans cette thèse, en démontrant l'efficacité des méthodes algébriques pour l'analyse qualitative de ces modèles. Les statistiques de temps en forme de tableau ont également été fournies pour comparer les performances des différentes méthodes algébriques. Nous avons développé un progiciel Maple pour l'analyse qualitative des modèles biologiques. Pour le système d’auto-assemblage de micelle avec puits chimiques, un modèle biochimique plane non linéaire, sa stabilité, trois types de bifurcations, et cycles limites ont été analysés en détail. Les conditions algébriques exactes sur les paramètres de ce système sont dérivées pour décrire les types de bifurcations et la stabilité et les types des points de bifurcation.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biological Engineering, 2016.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Cytokine therapy can activate potent antitumor responses, yet collateral toxicity often limits dosages. Although immunocytokines have been designed with the intent to localize cytokine activity, systemic dose-limiting side effects are not fully ameliorated by attempted tumor targeting. In the first part of this work, we used the B 1 6F 10 melanoma model to demonstrate that a nontoxic dose of IL-2 immunocytokine synergized with tumor-specific antibody to significantly enhance therapeutic outcomes compared to monotherapy with immunocytokine, concomitant with increased tumor saturation and intratumoral cytokine responses. Examination of cell subset biodistribution showed that the immunocytokine associated mainly with IL-2R-expressing innate immune cells, with more bound immunocytokine present in systemic organs than in the tumor microenvironment. More surprisingly, immunocytokine antigen specificity and Fc[gamma]R interactions did not appear necessary for therapeutic efficacy or biodistribution patterns, as immunocytokines with irrelevant specificity and/or inactive mutant Fc domains behaved similarly to tumor-specific immunocytokine. IL-2-IL-2R interactions, rather than antibody-antigen targeting, dictated immunocytokine localization; however, the lack of tumor targeting did not preclude successful antibody combination therapy. This study presents a safe, straightforward strategy for augmenting immunocytokine efficacy via supplementary antibody dosing and explores underappreciated factors that can subvert efforts to purposefully alter cytokine biodistribution. Numerous studies have identified cancer immunotherapy combinations that exhibit synergistic antitumor activity, but surprisingly, these studies rarely consider the effects of relative dose timing. In the second part of this work, using established syngeneic tumor models, we found that staggering IFN[alpha] administration after, rather than simultaneously with, serum-persistent IL-2 and tumor-specific antibody significantly increased long-term survival and generated immunological memory. Successful combination therapy required IFNa-induced activation of cross-presenting CD8[alpha]+ DCs following release of antigenic tumor debris by the IL-2-and-antibody-mediated immune response. Due to decreased phagocytic ability post-maturation, DCs activated too early captured much less antigen and could not effectively prime CD8+ T cells. Temporally programming DC activation to occur after tumoricidal activity enhanced tumor control by multiple combination immunotherapies that act through distinct mechanistic pathways, presenting a facile strategy for augmenting efficacy in the combinatorial treatment setting and highlighting dose schedule as an overlooked factor that can profoundly affect the success of multi-component immunotherapies.<br>by Alice Tzeng.<br>Ph. D.

Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of network motifs is still invalidated and currently no databases exist for this purpose. In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs. In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques. We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins.

Proteomics refers to global characterization of the full set of proteins present in a biological sample. Various analytical disciplines contribute to proteomics but mass spectrometry became method of choice for analysis of complex protein samples. Mass spectrometry allows for high throughput analysis of the proteome but, moreover, it has the ability to acquire higher-order information such as post-translational modifications (PTM). Glycosylation is the most abundant PTM on eukaryotic proteins. This dissertation will focus on method development for structural proteomics that will be utilized to explain the glycoproteome of obligate intracellular protozoan parasite Toxoplasma gondii as a model system. Optimization of sample preparation is addressed in the first part of this dissertation. Sample preparation for mass spectrometry analysis is a critical step in the proteomics workflow because the quality and reproducibility of sample extraction and preparation significantly impacts the separation and identification capabilities of mass spectrometers. Also, there are problems unique to intracellular parasites as limited amount, host cell impurity and choice of the host. The additional obstacle is to extract only glycosylated proteins for which there is no one standard method. Here we report the optimal sample preparation method utilizing agarose bound Concanavalin A (Con A) beads to efficiently pull down glycoproteins, dialyze and analyze them using MuDPIT. This method was further enhanced by passing the non-retained protein fraction (first flow-through) through a second Con A column and then passing the second non-retained protein fraction (second flow-through) through the third Con A column (3 sequential pull-downs) yielding 394 benchmark proteins. Glycoproteome of Toxoplasma gondii is not yet fully understood. However, evidence suggests that glycosylation could be essential for cyst formation and maintenance which is characteristic of chronic stage of disease. The focus of the second part of dissertation is to better understand the differences in glycoproteomes of tachizoites and tissue cysts. Cyst proteins pulled down using optimized sample preparation method that do not appear in the tachyzoites pulldowns could be critical elements in the structural stability of the tissue cyst.

Des de la invenció del microscopi al segle XVII, l’ús de tecnologies d’imatge ha estat fonamental per a l’estudi de teixits biològics. Al llarg dels segles, s’han anat desenvolupant i implementant noves tecnologies d’imatge per tal de millorar la visualització del teixits biològics i facilitar la comprensió de la seva estructura a partir de la mesura d’algunes de les seves propietats físiques. En aquest context, la polarimetria és una interessant tècnica òptica no invasiva que s’ha utilitzat per a la millora d’imatges en diversos camps com l’astronomia, la teledetecció i la caracterització de materials. A més, la polarimetria es pot combinar amb altres tècniques òptiques per millorar, encara més, la visualització de mostres. La polarimetria compren un conjunt de mètodes òptics basats en mesurar la polarització de la llum i com aquesta varia a l’interactuar amb les mostres. En aquesta tesi, s’estudien, s’implementen experimentalment i s’apliquen per primera vegada en l’anàlisi de teixits biològics alguns mètodes polarimètrics recentment proposats en la literatura (així com de nous) per tal de millorar la visualització de teixits animals i vegetals. En l’àmbit de la biomedicina, el potencial d’utilitzar la polarització està demostrat en una gran varietat d’estudis. Aquests estudis solen basar-se en dos grups de tècniques polarimètriques: tècniques anomenades Portes de Polarització (de l’anglès “Polarization Gating”) i tècniques basades en la matriu de Mueller. En aquesta tesi, s’investiga la relació que hi ha entre aquests dos grups de tècniques polarimètriques i proposem un nou mètode polarimètric més general que permet analitzar diferents configuracions de “Polarization Gating” a partir de la mesura experimental d’una sola matriu de Mueller. Respecte els estudis biomèdics basats en la matriu de Mueller, diferents propietats polarimètriques (diatenuació, retard i despolarització) són analitzades en base a observables per tal d’obtenir informació física relacionada amb l’estructura dels teixits biològics i també per incrementar la seva visualització. En aquests estudis, el retard és analitzat en profunditat mitjançant la descomposició de Lu-Chipman i el càlcul del retard lineal, el retard circular, i l’orientació de l’eix ràpid, entre d’altres. En canvi, l’anàlisi de la despolarització es restringeix al càlcul d’observables que quantifiquen la despolarització global de les mostres i no permeten l’estudi d’informació més específica, com poden ser possibles anisotropies en aquest procés de despolarització. Per aquest motiu, en aquesta tesi s’estudien diferents observables que descriuen amb més detall les propietats de despolarització de la mostra per, posteriorment, ser aplicats per millorar la visualització dels teixits animals mesurats. En aquest sentit, uns observables anomenats Índexs de Puresa Polarimètrica (de l’anglès “Indices of Polarimetric Purity”) són aplicats en l’estudi de teixits d’origen animal. Aquests observables són utilitzats per millorar la visualització dels teixits, revelant certes estructures ocultes en els canals de despolarització estàndard, i també per classificar diferents teixits d’origen animal amb una millor eficiència. Finalment, també estudiem l’ús de la polarització per a l’anàlisi de teixits d’origen vegetal. A diferència dels teixits d’origen animal, la polarimetria és molt menys utilitzada en l’anàlisi de plantes, sent els mètodes basats en la matriu de Mueller molt poc usats. Per aquesta raó, en aquesta tesi s’estudia el potencial de la polarimetria de Mueller per a l’anàlisi de teixits d’origen vegetal i es comparen els resultats amb els obtinguts amb algunes tècniques polarimètriques i no polarimètriques comunament utilitzades. Com a resultat, la polarimetria de Mueller és un mètode polarimètric òptim per a l’obtenció d’imatges de teixits d’origen vegetal que, a més a més, es pot utilitzar com una eina complementària a les altres tècniques òptiques no polarimètriques.<br>Desde la invención del microscopio en el siglo XVII, el uso de tecnologías de imagen ha sido fundamental para el estudio de tejidos biológicos. A lo largo de los siglos, se han desarrollado e implementado nuevas tecnologías de imágenes para mejorar la visualización de los tejidos biológicos y facilitar la comprensión de su estructura a partir de la medición de algunas de sus propiedades físicas. En este contexto, la polarimetría es una interesante técnica óptica no invasiva que se ha utilizado para la mejora de imágenes en diversos campos como la astronomía, la teledetección y la caracterización de materiales. Además, la polarimetría se puede combinar con otras técnicas ópticas para mejorar aún más la visualización de muestras. La polarimetría comprende un grupo de métodos ópticos que se basan en medir la polarización de la luz y cómo esta varía al interactuar con las muestras. En esta tesis, se estudian, se implementan experimentalmente y se aplican por primera vez en el análisis de tejidos biológicos algunos métodos polarimétricos propuestos recientemente en la literatura (así como nuevos) para mejorar la visualización de tejidos animales y vegetales. En el campo de la biomedicina, el potencial de la polarimetría se demuestra en una amplia variedad de estudios. Estos estudios se basan generalmente en dos grupos de técnicas polarimétricas: técnicas denominadas Puertas de Polarización (del inglés “Polarización Gating”) y técnicas basadas en matrices de Mueller. En esta tesis, investigamos la relación entre estos dos grupos de técnicas polarimétricas y proponemos un nuevo método polarimétrico más general que permite el análisis de diferentes configuraciones de “Polarization Gating” a partir de la medida experimental de una única matriz de Mueller. Respecto a los estudios biomédicos basados en la matriz de Mueller, diferentes propiedades polarimétricas (diatenuación, retardo y despolarización) son analizadas a partir de un grupo de observables para obtener información física relacionada con la estructura de los tejidos biológicos y también para mejorar su visualización. En estos estudios, el retardo es analizado en profundidad mediante la descomposición de Lu-Chipman y el cálculo del retardo lineal, el retardo circular y la orientación del eje rápido, entre otros. Por el contrario, el análisis de la despolarización se restringe al cálculo de observables que cuantifican la despolarización global de las muestras y no permiten el estudio de información más específica, como pueden ser posibles anisotropías en ese proceso de despolarización. Por ello, en esta tesis se estudian diferentes observables que describen con más detalle las propiedades de despolarización de la muestra para, posteriormente, ser aplicados para mejorar la visualización de los tejidos animales medidos. En ese sentido, los parámetros denominados Índices de Pureza Polarimétrica (del inglés “Indices of Polarimetric Purity”) se aplican para inspeccionar los tejidos de origen animal. Estos observables de despolarización se utilizan para mejorar la visualización de tejidos, revelando ciertas estructuras ocultas en canales de despolarización estándar, y también para clasificar con mayor eficiencia diferentes tejidos de origen animal. Finalmente, también estudiamos el uso de la polarimetría para el análisis de tejidos de origen vegetal. A diferencia de los tejidos de origen animal, la polarimetría se utiliza mucho menos en el ámbito del análisis de plantas, siendo las técnicas basadas en Mueller muy poco utilizadas. Por este motivo, esta tesis estudia el potencial de la polarimetría de Mueller para el análisis de tejidos de origen vegetal y compara los resultados obtenidos con los obtenidos con algunas técnicas polarimétricas y no polarimétricas de uso común. Como resultado, la polarimetría de Mueller es un método polarimétrico óptimo para la obtención de imágenes no invasivas de tejidos de origen vegetal que, además, puede utilizarse como herramienta complementaria a otras técnicas ópticas no polarimétricas.<br>Since the invention of the microscope in the 17th century, the use of imaging technologies has been fundamental in the study of biological tissues. Over the centuries, new imaging technologies have been developed and implemented to enhance the visualization of tissues and ease the understanding of their structure from the measurement of some of their physical properties. In that context, polarimetry is an interesting non-contact and non-invasive optical technique that has been used for image enhancement in a wide range of fields such as astronomy, remote sensing, and characterization of materials. Moreover, polarimetry can be combined with other optical techniques to further improve the visualization of samples. Polarimetry comprises a group of optical methods that are based on measuring the polarization of light and how it varies when interacting with samples. In this thesis, some polarimetric methods recently proposed in the literature (as well as new ones) are studied, experimentally implemented, and applied for the first time in the analysis of biological tissues to improve the visualization of animal and plant tissues. In the field of biomedicine, the potential of polarimetry is demonstrated in a wide variety of studies. These studies are usually based on two groups of polarimetric techniques: Polarization Gating techniques and Mueller matrix-based techniques. In this thesis, we investigate the relationship between these two groups of polarimetric techniques and we propose a new generalized polarimetric method that allows the analysis of different Polarization Gating configurations from a single Mueller matrix measurement. Concerning to the biomedical studies based on the Mueller matrix, different polarimetric properties (diattenuation, retardance, and depolarization) are analyzed from a group of observables to obtaining physical information related to the structure of biological tissues and also to enhance their visualization. In these studies, retardance is completely studied through the Lu-Chipman decomposition and the calculation of the linear retardance, the circular retardance, and the orientation of the fast axis, among others. By contrast, the analysis of depolarization content is restricted to the calculation of observables that quantify the overall depolarization of samples and do not allow the study of more specific information, as can be possible anisotropies in that depolarization process. For that reason, in this thesis, different observables that further describe the depolarization properties of the sample are studied to, afterwards, be applied for visualization enhancement of the measured animal tissues. In that sense, the parameters called Indices of Polarimetric Purity are applied to inspect animal tissues. These depolarizing observables are used to improve tissue visualization, revealing certain structures hidden in standard depolarization channels, and also to classify, with improved efficiency, different animal tissues. Finally, we also study the use of polarimetry for the analysis of plant tissues. Unlike animal tissues, polarimetry is much less used in the plant analysis framework, being Mueller-based techniques scarcely used. For this reason, this thesis studies the potential of Mueller polarimetry for plant tissue analysis and compares the obtained results with those obtained with some commonly used polarimetric and non-polarimetric techniques. As a result, Mueller polarimetry is an optimal polarimetric method for obtaining non-invasive images of plant tissues that, in addition, can be used as a complementary tool to other non-polarimetric optical techniques.<br>Universitat Autònoma de Barcelona. Programa de Doctorat en Física

This thesis explores the use of advanced fluorescence imaging and spectroscopic methods for investigating various properties of biological membranes. The cell membrane is a complex environment comprised of a variety of important molecules all necessary for maintaining cellular function. The dynamics of processes involved in membranes are typically over very short time and spatial frames. Advanced fluorescence imaging and spectroscopic methods present an opportunity for probing the dynamic nature of this environment due to their high levels of both spatial and temporal resolution. The following thesis consists of three biological problems centred on the cellular membrane, investigated through high resolution techniques. The first area of investigation focusses on the insulin regulated metabolism of glucose in fat and muscle tissue. Traditional experiments are performed using either isolated rat adipocytes or differentiated fibroblasts which both required lengthy and expensive culturing procedures. A new modified HeLa cell line was investigated to determine its efficacy as a homologue to the well characterised adipocyte model as a method for investigating factors affecting glucose metabolism. A direct comparison of the dynamic recruitment of the molecule Glucose Transporter 4 (GLUT4) to the plasma membrane was undertaken using a custom built Total Internal Reflection Fluorescence Microscopy (TIRFM) system. Utilising TIRFM the time dependant translocation of GLUT4 to the plasma membrane under insulin stimulation was investigated in the two cell lines. This was achieved through analysis of the increase in normalised fluorescence signal found within the 110 nm illuminated region of the TIRFM system. It was found that in the HeLa cell line responsiveness to insulin stimulation was present but with a significant difference in GLUT4 levels to the imaged adipocytes. It was also seen that this observed response occurred over a significantly longer time frame than in adipocyte cells with a half rise time in fluorescence intensity taking, on average, 5 minutes longer. In addition, the dynamic mobility of GLUT4 Storage Vesicles (GSVs) within the vicinity of the membrane was assessed through image analysis techniques. The abundance of mobile and stationary vesicles was assessed. In the adipocyte cells a sharp increase in mobile GSVs was observed over the initial 5 minutes after insulin stimulation. The amount of immobilised GSVs was seen to increase at a constant rate over the time course of experimentation. In the HeLa cell line, a similar rate of mobile GSV activity was observed, however, a decline in stationary GSVs was found. The increased accumulation of mobile vesicles at the plasma membrane is in accordance with previously proposed models of GSV recruitment. However, the reduction in stationary vesicles at the membrane surface in the HeLa cell line suggested differences in the machinery associated with vesicle fusion. The second area of study focussed on the analysis of the environmentally sensitive class of fluorophores known as molecular rotors, in particular the meso-substituted BODIPY rotor. Molecular rotors are said to report on the viscosity of the environment in which they reside but questions still remained over their efficacy of assessing viscosity in complex environments such as lipid bilayers. A combined Fluorescence Correlation Spectroscopy (FCS) and fluorescence lifetime system was optimised to simultaneously probe the lateral mobility and viscosity sensitive fluorescent lifetime of the dye in artificial bilayer systems. The diffusion coefficients measured directly through FCS were compared with those inferred from the lifetime values by conversion through the Saffman-Delbruck model. Those measured by FCS were found to be similar to previously simulated values suggesting a well working experimental system. The values found through lifetime analysis were of the same order to those measured by FCS but differed by as much as a factor of 2 in some cases. The reasons for this most likely lie through the inherent assumptions made using the Saffman-Delbruck model. In addition, the probes were assessed in bilayers of differing degrees of phospholipid saturation. It was observed that the viscosity of the environment increased with decreasing saturation in the hydrocarbon tail regions of the lipids. This was noted through the diffusion coefficients measured with both methods. The final chapter focussed on the creation of a system to increase the resolution of Fluorescence Lifetime Imaging Microscopy (FLIM) by implementing a TIRFM illumination scheme. The focus of this work was to increase resolution for imaging of membrane viscosity through the use of molecular rotors. Molecular rotors in cellular systems are susceptible to endocytosis over certain time frames, limiting their use in physiologically relevant studies in vitro. A gated FLIM system was constructed through the combination of pulsed laser diodes and a gated Intensified Charge Coupled Device (ICCD) camera. To investigate the abilities of the system to selectively image a surface localised signal relating to membrane viscosity, Supported Lipid Bilayers (SLBs) were used. It was found, through both lifetime and FCS, that the substrate on which the bilayer was deposited reduced the mobility of the probe and the measured fluorescence lifetime. The effect was a change in diffusion coefficient by a factor or 2-3 which was taken into account when assessing the viscosity measured through FLIM of SLBs. The TIRF-FLIM principle was then demonstrated through imaging of SLBs containing the molecular rotor BODIPY against a highly fluorescent background of the fluorophore FITC. FITC provided a background with a distinctly longer lifetime to that of the rotor in the bilayer. The system was able to resolve the surface localised lifetime signal over a range of concentrations of background signal from nano-molar to micro-molar. The critical point, where the bilayer lifetime became indistinguishable from the background, came at a fluorophore ratio of 2:1 BODIPY to FITC.

The processes which govern the function of biological organisms are inherently dynamic and studying their behaviour over time is critical for gaining insight into their underlying mechanisms. They are also incredibly complex with tens of thousands of interacting variables comprising their state. In recent years, the development of high-throughput assaying technologies such as microarrays and nuclear magnetic resonance spectroscopy have revolutionised the fields of genomics and metabolomics respectively with their ability to quickly and easily interrogate these states at a single moment in time. When these assaying technologies are used to collect measurements repeatedly on the same biological unit, such as a human patient, laboratory rat or cell line, then the temporal behaviour of the system can begin to emerge. Furthermore, when several of these units are studied simultaneously then the experiment is said to be biologically replicated and such data sets permit the inference of systemic behaviour in the population as a whole. The time series data sets arising from these replicated `omics experiments possess unique characteristics that make for challenging statistical analysis. They are very short (3-10 time points is typical), heterogeneous, noisy, frequently irregularly sampled and often have missing observations, in addition to being very highly dimensional. To overcome some of these difficulties, researchers in the field of genomics have turned to functional data analysis, which has proven to be successful in modelling unreplicated data sets. Replicated data sets, however, have received far less attention, due to the complexity introduced by the extremely small sample sizes and multiple levels of variation - the between-variable and the between-replicate. Furthermore, despite the remarkable similarities between genomics and metabolomics time series data sets, these methods have been far less successful at establishing themselves in the latter field. In this thesis we present a general statistical framework for the analysis of replicated, high-dimensional biological time series data sets. Supported by three case studies, we develop novel models and algorithms for tackling the unique challenges that each data set presents. We show how these fitted models can be used in dimensionality reduction, summarising the thousands of observed time series into a small number of representative temporal profiles that are eminently biologically interpretable. We introduce a novel moderated functional t -statistic that can be used for detecting variables that differ significantly between two biological groups, leveraging the high dimensionality of the data in order to increase power. In all instances detailed simulation studies are used to demonstrate that the methods outperform existing state-of-the-art approaches. With practical data analysis in mind, careful consideration is given to the implementation of the methods in software that is computationally efficient, with parallel programming exploited wherever possible. In most instances, the methods have resulted in novel biological findings when applied to real data, and represent, as far as we are aware, the first application of such functional data analysis models to metabolomics time series experiments.

L’objectif de cette thèse est la modélisation et l’étude de systèmes biologiques par l’intermédiaire de méthodes formelles. Les systèmes biologiques démontrent des comportements continus mais sont aussi susceptibles de montrer des changements abrupts dans leur dynamiques. Les équations différentielles ordinaires, ainsi que les systèmes dynamiques hybrides, sont deux formalismes mathématiques utilisés pour modéliser clairement de tels comportements. Un point critique de la modélisation de systèmes biologiques est la recherche des valeurs des paramètres du modèle afin de reproduire de manière précise un ensemble de données expérimentales. Si aucun jeu de paramètres valides n’est trouvé, il est nécessaire de réviser le modèle. Une possibilité est alors de remplacer un paramètre, ou un ensemble de paramètres, définissant un processus biologique par une fonctiondépendante du temps.Dans le cadre de cette thèse, nous exposons tout d’abord une méthode pour la révision de modèles hybrides. Pour cela, nous proposons une approche gloutonne appliquée à une méthode de contrôle optimal utilisant les mesures d’occupations etla relaxation convexe. Ensuite, nous étudions comment analyser les propriétés dynamiques d’un modèle à temps discret en utilisant la simulation ensembliste. Dans cet objectif, nous proposons deux méthodes basées sur deux outils mathématiques.La première méthode, qui se repose sur les polynômes de Bernstein, est une extension aux systèmes dynamiques hybrides, de l’outil de calcul ensembliste Sapo [1]. La seconde méthode utilise les représentations de Krivine-Stengle [2] pour permettre l’analyse d’atteignaiblité de systèmes dynamiques polynomiaux. Enfin, nous proposons aussi une méthodologie pour générer des systèmes dynamiques hybrides modélisant des protocoles biologiques expérimentaux. Les méthodes précédemment proposées sont appliquées sur divers études biologiques. Nous étudions tout d’abord un modèle de la production d’hémoglobinedurant la différentiation des érythrocytes dans la moelle [3]. Pour permettre la construction de ce modèle, nous avons dans un premier temps généré un ensemble de jeux de paramètres valides à l’aide d’une méthode de type Monte-Carlo. Dans un second temps, nous avons appliqué la méthode de révision de modèle afin de reproduire plus précisément les données expérimentales [4]. Nous proposons aussi un modèle préliminaire des effets à faibles doses du Cadmium sur la réponse du métabolisme à différentes étapes de la vie d’un rat. Enfin, nous appliquons les techniques d’analyse ensembliste pour la validation d’hypothèses sur un modèle d’homéostasie du fer [6] dans le cas où des paramètres varient dans de larges intervalles.Dans cette thèse, nous montrons aussi que le protocole associé à l’étude de la production d’hémoglobine, ainsi que le protocole étudiant l’intégration du Cadmium durant la vie d’un rat, peuvent être formalisés comme des systèmes dynamiques hybrides, et servent ainsi de preuves de concepts pour notre méthode de modélisation de protocoles expérimentaux<br>The purpose of this thesis is the modelling and analysis of biological systems with mechanistic models (in opposition with black-box models).In particular we use two mathematical formalisms for mechanism modelling: hybrid dynamical systems and polynomial Ordinary Differential Equations (ODEs).Biological systems modelling give rise to numerous problem and in this work we address three of them.First, the parameters in the differential equations are often uncertain or unknown.Consequently, we aim at generating a subset of valid parameter sets such that the models satisfy constraints deducted from some experimental data.This problem is addressed in the literature under the denomination of parameter synthesis, parameter estimation, parameter fitting, or parameter identification following the context.Then, if no valid parameter is found, one solution is to revise the model. This can be done by substituting a law in place of a constant parameter.In the literature, models with uncertain parts are known as grey models, and their studies can be found under the term of model identification.Finally, it may be necessary to ensure the correctness of the built models using validation, or verification, methods for a continuous over-approximation of the determined valid parameters.In this thesis we study the parameter synthesis problem, in the Haemoglobin production model case study, using an adaptation of the classical method based on Monte-Carlo sampling, and numerical simulations.To perform model revision of hybrid dynamical systems we propose an iterative scheme of an optimal control method based on occupation measures, and convex relaxations.Finally, we assess the quality of a model using set-based simulations, and reachability analysis.For this purpose, we propose two methods: the first one, which relies on Bernstein expansion, is an extension for hybrid dynamical systems of the reachability tool sapo , while the other uses Krivine-Stengle representations to perform the reachability analysis of polynomial ODEs.We also provide a methodology to generate hybrid dynamical systems modelling biological experimental protocols.All of these proposed methods were applied in different case studies.We first propose a model of haemoglobin production during the differentiation of an erythrocyte in the bone marrow.To develop this model we first applied the Monte-Carlo based parameters synthesis, followed by the model revision to correctly fit to the experimental data.We also propose a hybrid model of Cadmium flux in rats in the context of an experimental protocol, as well as a preliminary study of the effect of low dose Cadmium on a Glucose response.Finally, we apply the reachability analysis techniques for the validation on large parameters set of the iron homoeostasis model developed by Nicolas Mobilia during his Phd.We note the haemoglobin production model, as well as the glucose reponse model can be formalised, in their experimental context, as hybrid dynamical systems. Thus, they serve as proof of concept for the methodology of biological experimental protocols modelling

Methamphetamine is the most common amphetamine used and, along with 3,4-methylenedioxymethamphetamine (MDMA, Ecstasy), is considered part of a worldwide drug epidemic. Monitoring metham-phetamine levels in the body is important for purposes of drug screening for employment, criminal investigations, and therapeutic drug monitoring. While methamphetamine is suitable for detection using immunoassay techniques, these methods tend to have significant cross reactivity with other compounds. Over the last decade, more than eighty different quantitative, confirmatory analytical methods for measuring methamphetamine in biological samples have been published in the scientific literature. Analytical instrumentation used in these methods includes gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), capillary electrophoresis (CE), among others. These assays are capable of quantifying methamphetamine concentrations in a variety of biological matrices, including blood, plasma, urine, hair, and fingernails. Some of these techniques can achieve detection as low as 0.1 ng/mL (1 ppb) concentra-tions. The strengths and limitations of these methodologies will be discussed in the context of methamphetamine analysis. Additionally, methods that can simultaneously measure methamphetamine levels as well as metabolites and other drugs of abuse will be highlighted.

Cette thèse se développe sur deux fronts: nous nous concentrons sur les méthodes numériques des problèmes de contrôle optimal, en particulier sur le Principe de la Programmation Dynamique et sur le Model Predictive Control (MPC) et nous présentons des applications de techniques de contrôle en biologie. Dans la première partie, nous considérons l'approximation d'un problème de contrôle optimal avec horizon infini, qui combine une première étape, basée sur MPC permettant d'obtenir rapidement une bonne approximation de la trajectoire optimal, et une seconde étape, dans la quelle l¿équation de Bellman est résolue dans un voisinage de la trajectoire de référence. De cette façon, on peux réduire une grande partie de la taille du domaine dans lequel on résout l¿équation de Bellman et diminuer la complexité du calcul. Le deuxième sujet est le contrôle des méthodes Level Set: on considère un problème de contrôle optimal, dans lequel la dynamique est donnée par la propagation d'un graphe à une dimension, contrôlé par la vitesse normale. Un état finale est fixé, l'objectif étant de le rejoindre en minimisant une fonction coût appropriée. On utilise la programmation dynamique grâce à une réduction d'ordre de l'équation utilisant la Proper Orthogonal Decomposition. La deuxième partie est dédiée à l'application des méthodes de contrôle en biologie. On présente un modèle décrit par une équation aux dérivées partielles qui modélise l'évolution d'une population de cellules tumorales. On analyse les caractéristiques du modèle et on formule et résout numériquement un problème de contrôle optimal concernant ce modèle, où le contrôle représente la quantité du médicament administrée<br>This thesis is divided in two parts: in the first part we focus on numerical methods for optimal control problems, in particular on the Dynamic Programming Principle and on Model Predictive Control (MPC), in the second part we present some applications of the control techniques in biology. In the first part of the thesis, we consider the approximation of an optimal control problem with an infinite horizon, which combines a first step based on MPC, to obtain a fast but rough approximation of the optimal trajectory and a second step where we solve the Bellman equation in a neighborhood of the reference trajectory. In this way, we can reduce the size of the domain in which the Bellman equation can be solved and so the computational complexity is reduced as well. The second topic of this thesis is the control of the Level Set methods: we consider an optimal control, in which the dynamics is given by the propagation of a one dimensional graph, which is controlled by the normal velocity. A final state is fixed and the aim is to reach the trajectory chosen as a target minimizing an appropriate cost functional. To apply the Dynamic Programming approach we firstly reduce the size of the system using the Proper Orthogonal Decomposition. The second part of the thesis is devoted to the application of control methods in biology. We present a model described by a partial differential equation that models the evolution of a population of tumor cells. We analyze the mathematical and biological features of the model. Then we formulate an optimal control problem for this model and we solve it numerically

This study demonstrated the presence of hyperthermophilic organisms in the upper Jurassic Smackover formation in Womack Hills, AL. Evidence for the presence of these organisms was shown by the cultivation of an aerobic and an anaerobic, oil-degrading hyperthermophilic culture from the cuttings of an oil well in the Jurassic Smackover at 90¢ªC. Viability of microorganisms in the formation was established through electron microscopy, by carbon dioxide production, and by protein production during incubation in medium at 90¢ªC. Not only was the presence of viable microorganisms in the reservoir established, but as a result of this study, new cultivation methods were also developed that may prove useful in future studies of these types of organisms.

Thesis (M. Eng.)--Massachusetts Institute of Technology, Biological Engineering Division, 2006.<br>Includes bibliographical references (leaves 56-59).<br>Gene Fabrication technology involves the development and optimization of methods relevant to the in vitro synthesis of any given target gene sequence(s) in the absence of template. The driving purpose of this field of research is to bring about the capability for on-demand fabrication of a DNA construct of arbitrary length and sequence quickly, efficiently, and cost-effectively. The first part of this document describes many of the important considerations in performing successful de novo gene synthesis from a survey of the literature as well as from our own work. Recommendations are made for a universally effective, robust, and simple protocol for potential users of gene synthesis, discussing important factors such as choice of protocol, source of commercial oligonucleotides, and polymerase choice. The second part of this document focuses on error correction. Reducing error rates is one of the main challenges in gene fabrication because high error rates preclude the possibility of fabricating long gene targets in a practical and economical manner. Improvements in error rates are essential for continued progress in the development of gene fabrication technology. I discuss the importance of error rate in gene synthesis from a practical standpoint and show results in the development of novel methods for the removal of errors from a pool of synthesized DNA.<br>by Jason Sun-hyung Park.<br>M.Eng.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2012.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Joint injuries are common and often result in damage to cartilage, which has a limited ability to repair itself. Tissue engineering is a promising approach for improving cartilage healing in which biomaterials and chemical factors are supplied to direct cells to create a new tissue. The objective of this thesis was to optimize cartilage-like extracellular matrix production by investigating the effects of Dexamethasone (Dex) and HB-IGF- I (heparin-binding insulin-like growth factor-1) on cells encapsulated in the self-assembling peptide RAD and agarose hydrogels. Dex is a synthetic corticosteroid that has been shown to improve cartilage-like tissue production by bone marrow stromal cells (BMSCs), but the mechanisms underlying BMSC response to Dex are not understood. The hypothesis that the addition of Dex to chondrogenic medium would affect matrix production and aggrecanase activity of human and bovine BMSCs in RAD and agarose hydrogels was tested. The effects of Dex were dependent on the hydrogel material and the species/age of the BMSCs. Importantly, Dex reduced aggrecanase-mediated degradation of matrix in both agarose and RAD hydrogels and for both young bovine and adult human BMSCs. HB-IGF-1, a fusion protein of the heparin binding domain of HB-EGF and IGF-1, can be retained in cartilage matrix and stimulate proteoglycan synthesis with a single dose, whereas unmodified IGF-1 easily diffuses out of cartilage tissue. The RAD peptide was used as a scaffold for retaining growth factor to stimulate encapsulated chondrocytes and adjacent cartilage tissue. RAD was modified by adsorption of HB-IGF-1 before and after RAD assembly, as well as adsorption of heparan sulfate (HS) and IGF-1. The RAD material retained HS adsorbed pre-assembly and HB-IGF-1 delivered in both adsorption methods. Adsorbed HB-IGF-1 and IGF-1 led to increased aggrecan content regardless of the method of adsorption. A trend was found for increased proteoglycan synthesis in adjacent explants as well. RAD self-assembling hydrogels are a promising material for culturing BMSCs undergoing chondrogenesis, retaining, and delivering HB-IGF-1. Dex decreases aggrecanase activity of differentiating BMSCs and adsorbed HB-IGF-1 appears to enhance aggrecan production by encapsulating chondrocytes and adjacent tissue. These findings show potential for improving cartilage repair in vivo.<br>by Emily Marie Florine.<br>Ph.D.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biological Engineering, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references.<br>Mammalian noses can detect and distinguish an inestimable number of odors at minute concentrations. Four classes of G protein-coupled receptors (GPCRs) are responsible for this remarkable sensitivity: olfactory receptors (ORs), vomeronasal receptors (VNRs), trace amine-associate receptors, and formyl peptide receptors. Structural knowledge of these receptors is necessary to understand the molecular basis of smell. However, no structure exists for three main reasons. First, milligrams of protein are needed for crystallization screens, but most are expressed at low levels endogenously or in heterologous expression systems. Second, detergents capable of solubilizing and stabilizing these proteins in aqueous solution must be found. Third, the flexible nature of GPCRs can inhibit crystal lattice formation. Methods for overcoming each obstacle were developed. Milligrams of a VNR were expressed in HEK293 cells, and milligrams of 13 GPCRs were expressed in a cell-free system. All could be purified to >90%. The purified receptors had correct secondary structures, and could bind their ligands. The HEK293 and cell-free receptors had nearly identical structures and binding affinities, demonstrating that cell-free expression can be used for GPCR production and mutational studies. To demonstrate this, six variants of mOR103-15 with single amino acid substitutions were expressed. Ligand-binding measurements indicated which residues were involved in ligand recognition. The choice of detergent used in the cell-free system was critical, and significantly affected expression levels. A class of amphiphilic peptide detergents was designed and tested with the receptors. These detergents could be used to express milligrams of functional receptors. The peptide tail and head group properties did not significantly affect their function, suggesting that they may be a class of surfactants usable with multiple olfactory-related receptors, and even other membrane proteins. Lastly, the protein T4 Lysozyme (T4L) was fused in the 3rd intracellular loop of two receptors to increase potential crystal lattice contact points. Purified T4L variants had correct secondary structures, and could bind their ligands and initiate intracellular signaling. The methods described generated sufficient quantities of pure receptors for crystal screens. The large number of functionally expressed GPCRs indicates that these techniques can be applied to other olfactory-related receptors, and even other membrane proteins.<br>by Karolina Corin.<br>Ph.D.

Thesis (doctoral)--Swedish University of Agricultural Sciences, 2004.<br>Appendix consists of reprints of five manuscripts, four of which are co-authored with others. Includes bibliographical references. Also partially available online in PDF format; online version lacks appendix.

New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening.  It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that  produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions.  HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.

Investigations into the application of catalysts, of the type [Ir(COD)(PR₃)(NHC)]PF₆, within the realm of alkyne dimerization have been undertaken. These novel catalysts, previously synthesised within our research group, feature both a bulky phosphine ligand and a sterically-encumbered N-heterocyclic carbene ligand. The use of these iridium complexes in alkyne dimerization has been examined, with particular emphasis being placed upon tuning the selectivity of the dimerization whilst maintaining high yields. The relative paucity of iridium-mediated (Z)-selective dimerization procedures detailed in the literature rendered this transformation an appealing process to investigate. Subsequent studies led to a broadly employable system being developed which was applied to a range of aryl alkynes, resulting in the formation of the analogous (Z)-enynes in good yield. Following this, a programme of research in collaboration with the Beatson Institute for Cancer Research, Glasgow, describes contributions towards the construction of specific '3D libraries' for potential application in fragment-based drug discovery. The chemistry investigated during this time forms the basis of the Beatson's contribution to the recently formed '3D libraries consortium' concerned with the fragment-based drug discovery. The ultimate goal was the preparation of an array of compounds featuring a non-planar conformation. It is hypothesised that the fragments will play key roles in inhibiting protein-protein interactions in key oncological processes. Further, it is envisaged that the conformational complexity imparted to the compounds will provide an advantage in overcoming difficulties associated with protein specificity. The aim of the project was to design a synthetic route to a novel pyridyl cyclopropane scaffold. The goal was that the preparative approach would allow for rapid access to a key late-stage intermediate, which in turn would then be able to undergo a series of transformations to allow for a range of fragments, based around a common scaffold, to be synthesised. The isolated compounds will form the basis of biophysical and biochemical based screening assays examining the compound's anti-cancer profile, with particular focus on identifying inhibitors of proteinprotein interactions. The final section of research centred on efforts towards the total synthesis of Agariblazeispirol C. As a result, significant steps towards the synthesis of the natural product have been achieved and a functionalised advanced intermediate has been reached. In this regard, a robust and efficient preparative pathway to the advanced intermediate has been designed. In addition, the key oxygenated sidechain has been installed in a late-stage species and represents an auspicious step towards the synthesis of the target molecule. The introduction of this key moiety was achieved following sustained synthetic efforts focusing on olefination and organometallic addition chemistry. The stereochemistry of the resulting intermediate has been deduced based on NMR studies. Subsequent synthetic investigations facilitated the formation of a suitable precursor for the ultimate synthetic transformation, a Pauson-Khand reaction. Preliminary attempts to promote the annulation protocol are discussed and it is likely that this work will significantly enhance the likelihood of accessing the natural product for the first time.

The integration of top-down (lithographic) and bottom-up (chemical synthesis) fabrication and the ability to fabricate over macroscopic areas, are both major challenges in nanoscience. Overcoming these challenges would offer significant advances in biological sciences and applications. In the present work, we investigate how large area nano-lithographic techniques, interference lithography and nanoimprint lithography, can be combined with the self-assembly of organic molecules to produce nanostructured functional arrays over macroscopic areas. Organic films containing photocleavable protecting groups have been used as resists and patterned by photolithographic methods. Investigations into photodeprotection at 244 nm and 325 nm have been conducted. Contact angle analysis and X-ray photoelectron spectroscopy (XPS) confirmed that photodeprotection occurred much faster at 244 nm compared to 325 nm. A non-fouling polymer has been grown from exposed regions of the photo-reactive film by surface initiated atom transfer radical polymerisation (SI-ATRP). Mask patterns of the polymer have been fabricated and were observed to pattern adsorbed proteins. Interference lithography has fabricated periodic arrays over a macroscopic area by irradiating at 244 nm. Atomic force microscopy (AFM) confirmed an array of polymer features ca. 100 nm in width. Scanning near-field photolithography (SNP) has fabricated a multifunctional pattern; a pattern of 8 lines with varied exposure and a pattern of 6 lines with varied line width. AFM confirmed an increase in polymer line height with increasing exposure and no change in polymer line height with varied line width between 500 nm and 800 nm. Nanoimprint lithography (NIL) has been used to fabricate a chemically patterned surface for the patterning of biological molecules over a macroscopic area. An imprinted film of poly(methyl methacrylate) PMMA resist has been etched to remove the residual layer and used to direct the adsorption of (3-aminopropyl)triethoxysilane (APTES) and 2-[Methoxy(polyethyleneoxy)propyl]-trichlorosilane (PEG-silane) onto the substrate. Fluorescence microscopy confirmed protein adsorption with a high degree of control over the proteins spatial organisation on micro-scale patterns. AFM confirmed the fabrication of an array of APTES dots within a film of PEG-silane with diameters of 70 nm, and the subsequent attachment of streptavidin through bioconjugation with biotin. Finally both photolithographic methods and NIL have fabricated arrays of TiO2 that have been demonstrated as switchable platforms for protein adsorption and protein degradation. Wet and dry etching methods were used to form TiO2 structures that, in combination with an adsorbed film of PEG-silane, adsorbed protein with control over the spatial organisation of the protein. Fluorescence microscopy suggested degradation of the proteins following UV exposure and then the re-adsorption of a second protein following emersion in a protein solution. An array pattern fabricated by NIL has demonstrated as many as 10 cycles of protein adsorption and protein degradation without exhibiting a change in fluorescence or non-specific adsorption.