Dissertations / Theses: 'GEME] Engineering Sciences'

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Gustafsson, Mika. "Gene networks from high-throughput data : Reverse engineering and analysis." Doctoral thesis, Linköpings universitet, Kommunikations- och transportsystem, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54089.

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Experimental innovations starting in the 1990’s leading to the advent of high-throughput experiments in cellular biology have made it possible to measure thousands of genes simultaneously at a modest cost. This enables the discovery of new unexpected relationships between genes in addition to the possibility of falsify existing. To benefit as much as possible from these experiments the new inter disciplinary research field of systems biology have materialized. Systems biology goes beyond the conventional reductionist approach and aims at learning the whole system under the assumption that the system is greater than the sum of its parts. One emerging enterprise in systems biology is to use the high-throughput data to reverse engineer the web of gene regulatory interactions governing the cellular dynamics. This relatively new endeavor goes further than clustering genes with similar expression patterns and requires the separation of cause of gene expression from the effect. Despite the rapid data increase we then face the problem of having too few experiments to determine which regulations are active as the number of putative interactions has increased dramatic as the number of units in the system has increased. One possibility to overcome this problem is to impose more biologically motivated constraints. However, what is a biological fact or not is often not obvious and may be condition dependent. Moreover, investigations have suggested several statistical facts about gene regulatory networks, which motivate the development of new reverse engineering algorithms, relying on different model assumptions. As a result numerous new reverse engineering algorithms for gene regulatory networks has been proposed. As a consequent, there has grown an interest in the community to assess the performance of different attempts in fair trials on “real” biological problems. This resulted in the annually held DREAM conference which contains computational challenges that can be solved by the prosing researchers directly, and are evaluated by the chairs of the conference after the submission deadline. This thesis contains the evolution of regularization schemes to reverse engineer gene networks from high-throughput data within the framework of ordinary differential equations. Furthermore, to understand gene networks a substantial part of it also concerns statistical analysis of gene networks. First, we reverse engineer a genome-wide regulatory network based solely on microarray data utilizing an extremely simple strategy assuming sparseness (LASSO). To validate and analyze this network we also develop some statistical tools. Then we present a refinement of the initial strategy which is the algorithm for which we achieved best performer at the DREAM2 conference. This strategy is further refined into a reverse engineering scheme which also can include external high-throughput data, which we confirm to be of relevance as we achieved best performer in the DREAM3 conference as well. Finally, the tools we developed to analyze stability and flexibility in linearized ordinary differential equations representing gene regulatory networks is further discussed.

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Bolliet, Catherine. "Gene-supplemented collagen scaffolds for non-viral gene delivery for brain tissue engineering." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/38586.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2007.
Includes bibliographical references (p. 90-95).
Recent advances in tissue engineering, combining an extracellular matrix (ECM)-like vehicle with therapeutic molecules, cells and/or genes has yielded promising results for brain injury repair. The purpose of this thesis was to develop a collagen scaffold for the non-viral delivery of the gene encoding for Glial Cell-Derived Neurotrophic Factor (GDNF); hence to provide a local, long-term release and overexpression of GDNF via transfection of cells seeded into the scaffold or endogenous cells. The first part of the thesis aimed to investigate the in vitro transfection of marrow stromal stem cells (also referred to as mesenchymal stem cells, MSCs) in monolayer with plasmid GDNF ([mu]gDNF). Several parameters were evaluated: the choice of a transfer reagent (GenePorter2 versus Lipofectamine 2000), the doses of plasmid incorporated in the liposomes (ranging from 0.2[mu]g to 2[mu]g), the post-transfection medium (Medium 1: DMEM low glucose, 20% FBS and 1% antibiotic versus Medium 2: DMEM low glucose, 20% FBS, 1% antibiotic and 10ng/ml FGF-2) and the culture environment during transfection (static versus dynamic). The objective of the second part was to determine the conditions, including the design of the scaffold and the method of seeding, under which MSCs could attach and grow on the scaffold.
(cont.) Collagen scaffolds were made by a freeze-drying technique and prepared with various amounts of collagen, cross-link densities, and freezing temperatures. The effect of gene supplementation on the cross-link density was evaluated using the swelling ratio. Finally, the aim of the third part was to evaluate different parameters to optimize the transfection of cells grown in the scaffolds. The profile of production of GDNF was studied for different cross-link density, initial plasmid dose (2 and 10 [mu]g) and plasmid-transfection reagent ratio. Finally the effect of the pore diameter and static and dynamic culture environments were tested to optimize the in vitro conditions for the plasmid uptake and expression by the MSCs. The results demonstrated the possibility of using non-viral transfection conditions in vitro to enable MSCs to express a selected neurotrophic factor, GDNF, in therapeutic doses. MSCs were shown to over-express GDNF for at least a two-week period of time. Lipoplexes loaded with as little as 0.2 [mu]g could result in a significant production of GDNF by MSCs for several days, before falling off to control levels after one week. For the highest loading of plasmid (2 [mu]g), the level of GDNF production was still above the control after 2 weeks.
(cont.) Dynamic transfection had a dramatic effect on the production of GDNF. The accumulated amount of GDNF during the 2-week period reached 65 ng/ml compared to 20 ng/ml produced in static conditions. The growth factor bFGF, which is used in transdifferentiation of MSCs for a neuronal phenotype, was shown to promote a high level of cell death when used in the post-transfection medium. Collagen scaffolds can be prepared to incorporate the plasmid DNA-lipid complexes for subsequent release. Also, gene and subsequent cross-link density have an effect on the mechanical behavior of scaffolds. Finally, the gene-supplemented collagen scaffolds can serve as a carrier for lipoplexes and modified MSCs and provide a long-term overexpression of GDNF. The level of gene expression in the collagen constructs was lower than those obtained in MSC monolayers but high enough to result in therapeutic doses previously found in vitro. The cross-linking treatment did not affect significantly the release profile of GDNF. The application of orbital shaking during the 4 hours of transfection had a positive effect on the production of GDNF but not as strong as reported in monolayer studies. The load of plasmid DNA is a prominent parameter in the three-dimensional (3-D) transfection.
(cont.) In this study, the highest level of GDNF expression was observed for 10 [mu]g of plasmid DNA and 6 days after transfection. Overall, these results demonstrated that the combination of tissue engineering and non-viral transfection of MSCs for the over-expression of GDNF was a promising approach for the long-term production of selected neurotrophic growth factors. This approach could provide benefits in the treatment of conditions involving the loss of brain tissue.
by Catherine Bolliet.
S.M.

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Capito, Ramille M. (Ramille Marie). "Gene-supplemented collagen-glycosaminoglycan scaffolds for nonviral gene delivery in articular cartilage tissue engineering." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36203.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2006.
Includes bibliographical references.
Three-dimensional scaffolds and growth factors have been shown to be important for articular cartilage tissue engineering. A major problem in using recombinant proteins in vivo, however, is the inability to maintain therapeutic levels over prolonged times due to degradation or diffusion from the defect site. The goal of this thesis was to develop a method to employ type II collagen-glycosaminoglycan (CG) scaffolds for the nonviral delivery of the gene encoding for insulin-like growth factor (IGF)-I, as a novel means to provide a local, elevated, and prolonged release of a therapeutic growth factor via transfection of cells seeded or migrating within the scaffold. In vitro studies were performed to evaluate gene-supplemented CG (GSCG) scaffolds, including: 1) the type of expansion medium to use for growing chondrocytes prior to transfection, 2) methods of incorporating genes within scaffolds, 3) additional incorporation of transfection enhancers, and 4) the use of mesenchymal stem cells (MSCs) as an alternative cell source for articular cartilage tissue engineering.
(cont.) The medium used during monolayer expansion not only had a significant effect on subsequent biosynthesis and chondrogenesis in CG scaffolds, but also on gene transfer to chondrocyte monolayers. The expansion medium that resulted in enhanced 3-D biosynthesis and gene transfer to cells in monolayer was used throughout the rest of the studies. Greater plasmid retention within GSCG scaffolds was achieved by chemically cross-linking the plasmid IGF-1 (pIGF-1) to the scaffold (compared to simple plasmid absorption), and resulted in more steady and prolonged IGF-1 overexpression by seeded chondrocytes. Incorporation of a lipid transfection reagent or gelatin nanoparticles encapsulating pIGF-1 significantly enhanced gene expression. The method of gene incorporation and the type of transfection enhancer were important variables that controlled the initiation, amount, and duration of growth factor release. IGF-1 overexpression by cells successfully transfected within GSCG scaffolds also increased biosynthesis of cartilage matrix molecules and chondrogenesis. Finally, MSCs seeded into GSCG scaffolds were able to be successfully transfected and maintained IGF-1 overexpression for at least 2 weeks post-seeding.
(cont.) These findings show promise in using GSCG scaffolds for providing a local, prolonged, and therapeutic release of desired growth factors using nonviral transfection methods for tissue engineering or regenerative medicine applications.
by Ramille M. Capito.
Ph.D.

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St, John Oliver Tudor Lockhart. "Genome engineering and gene drive in the mosquito aedes aegypti." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:1251080e-cf7b-4bdd-b01e-d01748ead2d2.

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Genetic control strategies are a novel method for reducing populations of pest insects such as the yellow fever mosquito Aedes aegypti, a major vector of several important arboviral diseases. This thesis describes efforts to develop new tools to engineer the Ae. aegypti genome and to better understand existing tools, and furthermore to use these to engineer a gene drive system in Ae. aegypti. The piggyBac transposon was found to be extremely stable in the germline of Ae. aegypti, and transposons engineered into the germline could not be remobilized with either an endogenous or exogenous source of piggyBac transposase. Conversely, somatic remobilization of piggyBac transposons was found to be readily detectable in the presence of a source of active transposase, the first report of such remobilization in Ae. aegypti. Toward new tools for genome engineering, the site-specific integrase from the phage φC31 was successfully used to promote exchange between a transgene cassette inserted into the genome of Ae. aegypti and a cassette in a plasmid vector, in the first demonstration of recombinase mediated cassette exchange technology in a pest insect species. The integrases from phages φRV1 and Bxb1 were not found to be active in the germline of the mosquito. Finally, development of a gene drive system in Ae. aegypti using an RNAi-mediated killer-rescue mechanism was attempted. Tissue-specific expression of tTAV-regulated-toxic effectors genes, using the promoter regions of the blood meal induced genes Carboxypeptidase A-1, 30Kb and Vitellogenin A, was possible, but sex-specificity was not achieved. A blood meal inducible lethal phenotype was not possible using the chosen promoters, with expression of the effectors either leading to death in early development or to a sublethal phenotype. RNAi against tTAV fused to the Mnp fragment of the dengue virus’ genome was tissue specific, but was found to be highly effective in the fat body suggesting that the Vitellogenin A was the best candidate for the engineering of killer-rescue systems in the mosquito.

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Davies, Emma L. "Somatic cell gene therapy for diabetes mellitus : engineering a surrogate B-cell." Thesis, Aston University, 1996. http://publications.aston.ac.uk/10940/.

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Improved methods of insulin delivery are required for the treatment of insulin-dependent diabetes mellitus (IDDM) to achieve a more physiological profile of glucose homeostasis. Somatic cell gene therapy offers the prospect that insulin could be delivered by an autologous cell implant, engineered to secrete insulin in response to glucose. This study explores the feasibility of manipulating somatic cells to behave as a surrogate insulin-secreting β-cells. Initial studies were conducted using mouse pituitary AtT20 cells as a model, since these cells possess an endogenous complement of enzymes capable of processing proinsulin to mature insulin. Glucose sensitive insulin secretion was conferred to these cells by transfection with plasmids containing the human preproinsulin gene (hppI-1) and the GLUT2 gene for the glucose transporter isoform 2. Insulin secretion was responsive to changes in the glucose concentration up to about 50μM. Further studies to up-rate this glucose sensitivity into the mM range will require manipulation of the hexokinase and glucokinase enzymes. Intraperitoneal implantation of the manipulated AtT20 cells into athymic nude mice with streptozotocin-induced diabetes resulted in decreased plasma glucose concentrations. The cells formed vascularised tumours in vivo which were shown to contain insulin-secreting cells. To achieve proinsulin processing in non-endocrine cells, co-transfection with a suitable enzyme, or mutagenesis of the proinsulin itself are necessary. The mutation of the human preproinsulin gene to the consensus sequence for cleavage by the subtilisin-like serine protease, furin, was carried out. Co-transfection of human fibroblasts with wild-type proinsulin and furin resulted in 58% conversion to mature insulin by these cells. Intraperitoneal implantation of the mature-insulin secreting human fibroblasts into the diabetic nude mouse animal model gave less encouraging results than the AtT20 cells, apparently due to poor vascularisation. Cell aggregations removed from the mice at autopsy were shown to contain insulin secreting cells only at the periphery. This thesis provides evidence that it is possible to construct, by cellular engineering, a glucose-sensitive insulin-secreting surrogate β-cell. Therefore, somatic cell gene therapy offers a feasible alternative for insulin delivery in IDDM patients.

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Zhao, Jia. "Engineering serine integrase-based synthetic gene circuits for cellular memory and counting." Thesis, University of Glasgow, 2015. http://theses.gla.ac.uk/6911/.

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A cellular counting system based on synthetic gene circuits would enable complex biological programming and be used in many biotechnology applications. Although a variety of synthetic memory circuits have been constructed, basic modules that can be assembled into a counting system are lacking. This thesis focuses on engineering a binary counting module, which can alternate between two states in response to a single repeating input signal. The highly directional large serine bacteriophage integrases were utilised as the basis for the synthetic circuits constructed in this study. Integrases and their protein co-factors, the recombination directionality factor (RDF) can change the orientation of a specific DNA segment flanked by two recombination sites. Integrase alone switches the orientation in one direction, and this directionality is reversed by the addition of its corresponding RDF. The two orientations can be used to turn gene expression on and off, leading to distinct output states which can be thought of as representing a single binary digit (0 and 1) heritably stored in the DNA. In this study, three different serine integrase-based synthetic gene circuits for cellular memory and counting were engineered and characterised. A set-reset latch was first constructed. By expressing ϕC31 integrase and co-expressing integrase with RDF Gp3 from two independent inducible systems, the orientation of the invertible DNA in the set-reset latch was inverted and restored respectively. This device demonstrated that ϕC31 integrase can successfully encode information into plasmid DNA. Next, a state-based latch was constructed, in which the gp3 gene was placed inside the invertible DNA segment to couple its transcriptional regulation to the circuit state. Integrase expression triggered by one input signal resulted in inversion of the invertible DNA, placing the gp3 gene in the correct orientation for transcription. Gp3 expression can then be triggered by another input signal to reverse the directionality of integrase, restoring the DNA back to its original configuration. By optimising the stoichiometry and kinetics of integrase and Gp3 expression, efficient switching of both multi-copy plasmid and single copy chromosomal DNA was achieved. Finally, the state-based latch was developed into a binary counting module by introducing a delay mechanism, in which gp3 transcription was inhibited by a state-based repressor during recombination requiring the absence of Gp3. Placing expression of gp3 under the control of the invertible DNA, allowed a single input signal controlling only integrase expression to switch the module between OFF (0) and ON (1). This is the first integrase-based module that generates different outputs in response to the same input signal and a fundamental step towards building a genetic binary counter with large counting capacity.

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Legault-Coutu, Daniel. "Studies on mesenchymal stem cells: In vivo identity, cellular biochemistry and use in gene therapy and tissue engineering." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103509.

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In 1868, the French experimental physiologist Goujon demonstrated the transplantability and osteogenic potential of bone marrow. More than a hundred years later, Friedenstein first isolated the cells responsible for this osteogenic capacity and demonstrated the existence of a non-hematopoietic stem cell within bone marrow. Since then, the field of mesenchymal stem cell (MSC) research has generated an incredible amount of data aimed at characterizing these cells and indentifying their true in vivo identity and localization, but also exploring their therapeutic potential in animal models. These studies served as a basis for more than 100 human clinical trials using MSCs in the last decade. However, many questions remain unanswered about MSCs. Current research thus aims at improving our understanding of these intriguing cells in order to better use them therapeutically. Four main themes can be identified in MSC research: 1) Characterization and identification of MSCs subsets, 2) Elucidation of the development origin and in vivo identity of MSCs, 3) Identification of the cellular and molecular mechanisms underlying the therapeutic effects of MSCs, and 4) pre-clinical (translational) and clinical use of MSCs. In the manuscript-based thesis presented here, I will present three original research articles covering all four of these themes. In chapter 2, I describe the use of novel cell surface markers to identify MSCs in vivo and in vitro. I initially show that FGF-receptors (FGFRs) are developmentally-regulated in both bone tissues and MSCs. Using these markers, I identified different MSCs subsets in bone tissues including perichondrium, periosteum and trabecular bone. Some of these cells appeared as pericytes in periosteum and trabecular marrow, supporting the model of a perichondrial MSC upstream of a pericytic osteo-stromal progenitor. Finally, I found that FGFRs activation leads to self-renewing proliferation of MSCs in vitro by reversibly inhibiting cellular senescence, providing a biological relevance for the expression of these markers. In chapter 3, I present the preliminary biochemical characterization of periostin, a poorly characterized matricellular protein abundantly expressed by MSCs in vitro and in vivo. I identify a post-translational modification in periostin that will undoubtedly help uncover its roles in MSCs (fate decision, proliferation, migration), in hematopoietic support, and in tissue repair. Finally, in chapter 4 I present our efforts to use MSCs in a cell-based gene therapy approach for hemophilia B. I show that the successful transplantation, engraftment, survival, differentiation, self-renewal and protein delivery by MSCs requires complex tissue engineering techniques and hierarchical scaffold design. More specifically, three-dimensional biomaterials scaffolds required optimization at the nano-, micro- and macroscale in order to sustain long term engraftment and protein delivery by MSCs in a murine model of hemophilia B. Taken together, the findings presented here provide significant advances in understanding MSCs, both in their fundamental biology and therapeutic potential.
En 1868, le physiologiste expérimental Goujon démontre que la moelle osseuse peut se transplanter et possède des propriétés ostéogéniques. Près de cent ans plus tard, Friedenstein réussi à isoler les cellules de la moelle responsables de ces propriétés et démontre ainsi l'existence d'une cellule souche non-hématopoïétique résidant dans la moelle osseuse. Depuis, la recherche sur les cellules souches mésenchymateuses (CSM) a généré un nombre considérable d'articles évaluant les caractéristiques in vitro de ces cellules, leur expression de marqueurs de surface, essayant de définir leur identité et leur localisation in vivo, mais aussi testant leurs propriétés thérapeutiques chez les animaux. Ces recherches servirent de base à plus de 100 études cliniques chez l'humain enregistrées jusqu'à maintenant, utilisant les CSM pour traiter diverses maladies. Cependant, plusieurs questions restent non résolues concernant ces intrigantes cellules souches. C'est pourquoi la recherche actuelle sur les CSM tente de répondre à ces questions fondamentales, de manière à pouvoir mieux comprendre les CSM et ainsi à mieux les utiliser thérapeutiquement. On note quatre thèmes majeurs dans la recherche sur les CSM actuelle : 1) la caractérisation in vitro des CSM et l'identification de nouveaux marqueurs de surface, 2) la recherche de l'identité in vivo des CSM et de leur niche ou localisation, 3) l'élucidation des mécanismes cellulaires et moléculaires impliqués dans leurs propriétés thérapeutiques, et 4) la recherche préclinique et clinique utilisant les CSM pour traiter des maladies. Dans la thèse par manuscrits présentée ici, je présente trois articles de recherche couvrant l'ensemble de ces quatre thèmes. Au chapitre 2, j'identifie une famille de récepteurs membranaires qui est régulée de façon développementale dans les tissus osseux et dans les CSM : les récepteurs FGF. Je démontre que ces récepteurs peuvent être utilisés pour identifier les CSM dans différents compartiments osseux tels que le perichondrium, le periosteum et l'os trabéculaire, de manière à suggérer l'existence de CSM primitives dans le perichondrium. Nous verrons également comment l'activation des récepteurs FGF sur les CSM permet leur prolifération tout en inhibant leur sénescence, leur permettant ainsi de s'auto-renouveller. Au chapitre 3, je présente la caractérisation biochimique préliminaire de periostin, une protéine matricellulaire peu connue et abondamment produite par les CSM. J'identifie une modification post-traductionnelle sur periostin qui permettra de mieux comprendre ses divers rôles dans la différentiation des CSM, leur capacité de supporter l'hématopoïèse et de participer à la réparation des tissus endommagés. Finalement, au chapitre 4 je présente une étude visant à utiliser les CSM pour la thérapie génique de l'hémophilie B. Je démontre que la survie, la différentiation, l'auto-renouvellement et la production de protéines thérapeutiques par les CSM après transplantation nécessitent l'utilisation de techniques d'ingénierie tissulaire complexes. Plus spécifiquement, j'ai dû optimiser des biomatériaux 3D à l'échelle nano-, micro- et macroscopique pour permettre la survie et la production de protéine à long-terme par les CSM dans des souris hémophiles. En conclusion, les résultats présentés ici représentent plusieurs avancées significatives dans notre compréhension de la biologie fondamentale des CSM mais également de leurs propriétés thérapeutiques.

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Li, Yan 1978 July 15. "Gene expression array simulator." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87263.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, June 2002.
"May 10, 2002.
Includes bibliographical references (leaf 141).
by Yan Li.
M.Eng.

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Kodysh, Yuliya. "Using co-expression to redefine functional gene sets for gene set enrichment analysis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41661.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 89-90).
Manually curated gene sets related to a biological function often contain genes that are not tightly co-regulated transcriptionally. which obscures the evidence of coordinated differential expression of these gene sets in relevant experiments. To address this problem, we explored strategies to refine the manually curated subcollection of the Molecular Signatures Database (MSigDB) for use with Gene Set Enrichment Analysis (GSEA). We examined the manually curated gene sets in context of an atlas of gene expression of many normal human tissues. To refine gene sets, we clustered the genes in each set based on co-expression across the tissues to produce more tightly co-regulated children gene sets that are also likely more accurate representations of the biological process or processes described by the gene set. We evaluated the performance of the clustering algorithms by refining gene sets in the context of several published GSEA analyses and verifying that the children gene sets score higher with GSEA than do the parents. We created and annotated a new, refined version of a large portion of the manually curated component of MSigDB, which we hope will be a resource for the GSEA community.
by Yuliya Kodysh.
M.Eng.

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Gyoergy, Andras. "Functional modularity in gene networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103726.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 141-150).
This thesis addresses two sources of context-dependence in both systems and synthetic biology: retroactivity and competition for shared cellular resources. The contribution is the development of simple-to-use computational tools that aide the analysis and design of multi-module genetic systems. These tools are a result of combining mathematical modeling and theoretical analysis with experiments performed in Escherichia coli. While current approaches most often neglect to account for context-dependence in living systems, experimental evidence demonstrates that such effects have profound influence on system behavior. As a result, modules developed separately are likely to behave differently from predicted, so that they need to be redesigned through a lengthy and ad hoc process every time they are inserted into a different system. To overcome this major limitation, in this thesis I expand the description of gene circuits. First, the description of modules is appended by quantities similar to input and output impedance in electrical networks theory. Second, the description of each protein is appended by a quantity characterizing the amount of resources that are sequestered for its production. As a result, the behavior of modules upon interconnection becomes predictable, facilitating both the rational design of synthetic circuits and furthering our understanding of natural systems. Application examples are considered, which include the design of oscillators and toggle switches, network identification problems, and standard metabolic optimization problems, such as maximizing reaction rates catalyzed by multiple enzymes and maximizing the steady state concentration of heteromultimer complexes.
by Andras Gyoergy.
Ph. D.

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Banks, Eric 1976. "Computational approaches to gene finding." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81523.

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Chen, Pin-Yi. "Resource competition in CRISPR-mediated gene regulation." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127156.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, May, 2020
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 77-79).
CRISPR-mediated gene regulation is known for its ability to control multiple targets simultaneously due to its modular nature: the same dCas9 effector can target different genes simply by changing the associated gRNA. However, multiplexing requires the sharing of limited amounts of dCas9 protein among multiple gRNAs, leading to resource competition. In turn, competition between gRNAs for the same resource may hamper network function. In this thesis, we develop a general model that takes into account the sharing of limited amounts of dCas9 protein for arbitrary CRISPR-mediated gene repression networks. We demonstrate that, as a result of resource competition, hidden interactions appear, which modifies the intended network regulations. As a case study, we analyze the effects of these hidden interactions on repression cascades. In particular, we illustrate that perfect adaptation to resource fluctuations can be achieved for certain network topology. Then, we analyze the stability properties of uncertain systems that are affected by resource competition via contraction analysis. Finally, we perform a combined analytical and experimental study on a two gRNA parallel network to demonstrate the resource competition effect.
by Pin-Yi Chen.
S.M.
S.M.
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Kontos, Kevin. "Gaussian graphical model selection for gene regulatory network reverse engineering and function prediction." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210301.

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One of the most important and challenging ``knowledge extraction' tasks in bioinformatics is the reverse engineering of gene regulatory networks (GRNs) from DNA microarray gene expression data. Indeed, as a result of the development of high-throughput data-collection techniques, biology is experiencing a data flood phenomenon that pushes biologists toward a new view of biology--systems biology--that aims at system-level understanding of biological systems.

Unfortunately, even for small model organisms such as the yeast Saccharomyces cerevisiae, the number p of genes is much larger than the number n of expression data samples. The dimensionality issue induced by this ``small n, large p' data setting renders standard statistical learning methods inadequate. Restricting the complexity of the models enables to deal with this serious impediment. Indeed, by introducing (a priori undesirable) bias in the model selection procedure, one reduces the variance of the selected model thereby increasing its accuracy.

Gaussian graphical models (GGMs) have proven to be a very powerful formalism to infer GRNs from expression data. Standard GGM selection techniques can unfortunately not be used in the ``small n, large p' data setting. One way to overcome this issue is to resort to regularization. In particular, shrinkage estimators of the covariance matrix--required to infer GGMs--have proven to be very effective. Our first contribution consists in a new shrinkage estimator that improves upon existing ones through the use of a Monte Carlo (parametric bootstrap) procedure.

Another approach to GGM selection in the ``small n, large p' data setting consists in reverse engineering limited-order partial correlation graphs (q-partial correlation graphs) to approximate GGMs. Our second contribution consists in an inference algorithm, the q-nested procedure, that builds a sequence of nested q-partial correlation graphs to take advantage of the smaller order graphs' topology to infer higher order graphs. This allows us to significantly speed up the inference of such graphs and to avoid problems related to multiple testing. Consequently, we are able to consider higher order graphs, thereby increasing the accuracy of the inferred graphs.

Another important challenge in bioinformatics is the prediction of gene function. An example of such a prediction task is the identification of genes that are targets of the nitrogen catabolite repression (NCR) selection mechanism in the yeast Saccharomyces cerevisiae. The study of model organisms such as Saccharomyces cerevisiae is indispensable for the understanding of more complex organisms. Our third contribution consists in extending the standard two-class classification approach by enriching the set of variables and comparing several feature selection techniques and classification algorithms.

Finally, our fourth contribution formulates the prediction of NCR target genes as a network inference task. We use GGM selection to infer multivariate dependencies between genes, and, starting from a set of genes known to be sensitive to NCR, we classify the remaining genes. We hence avoid problems related to the choice of a negative training set and take advantage of the robustness of GGM selection techniques in the ``small n, large p' data setting.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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Doherty, Matthew K. "Gene prediction with conditional random fields." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41646.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 75-77).
The accurate annotation of an organism's protein-coding genes is crucial for subsequent genomic analysis. The rapid advance of sequencing technology has created a gap between genomic sequences and their annotations. Automated annotation methods are needed to bridge this gap, but existing solutions based on hidden Markov models cannot easily incorporate diverse evidence to make more accurate predictions. In this thesis, I built upon the semi-Markov conditional random field framework created by DeCaprio et al. to predict protein-coding genes in DNA sequences. Several novel extensions were designed and implemented, including a 29-state model with both semi-Markov and Markov states, an N-best Viterbi inference algorithm, several classes of discriminative feature functions that incorporate diverse evidence, and parallelization of the training and inference algorithms. The extensions were tested on the genomes of Phytophthora infestans, Culex pipiens, and Homo sapiens. The gene predictions were analyzed and the benefits of discriminative methods were explored.
by Matthew K. Doherty.
M.Eng.

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Trudel, Nathalie. "Gene expression profile in human prostate cancer cell lines." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33449.

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Since the beginning of this work in 1998, it is estimated that 1780 men died of prostate cancer in Quebec. Molecular analysis of prostate cancer will eventually lead to the discovery of key genes involved in its onset and progression. The present project was to compare gene expression profiles in human non-tumorigenic versus tumorigenic prostate cell lines generated in our laboratory. A putative tumor suppressor gene present on 12q13 would be responsible for the non-tumorigenic phenotype of one cell line as discovered earlier by our team.
In order to compare gene expression patterns, expression arrays from Clontech, bearing 588 genes known to be involved in human cancers, were hybridized with cDNA derived from two related cell lines available in our laboratory. This one experiment provided interesting hints on differentially expressed genes that could be involved in human prostate cancer. Interesting clones were confirmed by Northern blots. When commercial antibody was available, analysis was extended at the protein level. A combination of these analyses revealed no striking difference in the level of expression for the genes previously identified by the arrays hybridization.
Simultaneously, differential display PCR techniques, allowing the discovery of unknown differentially expressed molecules and thus complementing the previous approach, were applied to compare related cell lines and unique hybrids. Cloning and sequencing of differential fragments brought us to what could be a new cDNA expressed in many human cell lines.
Prostate cancer is not well characterized enough to allow accurate diagnosis or appropriate therapy strategies. Differentially expressed molecules analyzed in this project as well as the putative new cDNA might fulfil part of this lack in the understanding of this disease.

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Liu, Ziying. "Identifying gene regulatory networks using Bayesian networks and domain knowledge." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27269.

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Bayesian network techniques have been used for discovering causal relationships among large number of variables in many applications. This thesis demonstrates how Bayesian techniques are used to build gene regulation networks. The contribution of this thesis is to find a novel way of combining pre-knowledge (biological domain information) into Bayesian network learning process for microarray data analysis. Such pre-knowledge includes biological process, cellular component and molecular function information and cell cycle information. Incorporating preexisting knowledge into the Bayesian network learning process significantly improves the accuracy and performance of learning. Another contribution of this thesis is the inference and validation of learning result based on the biological literature and biological knowledge. The learned network structure is presented graphically to make the results easy to understand. A yeast microarray dataset is used to test the performance of the learning process.

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17

Jones, Thouis Raymond 1971. "Predicting gene function from images of cells." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40515.

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Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 107-118).
This dissertation shows that biologically meaningful predictions can be made by analyzing images of cells. In particular, groups of related genes and their biological functions can be predicted using images from large gene-knockdown experiments. Our analysis methods focus on measuring individual cells in images from large gene-knockdown screens, using these measurements to classify cells according to phenotype, and scoring each gene according to how reduction in its expression affects phenotypes. To enable this approach, we introduce methods for correcting biases in cell images, segmenting individual cells in images, modeling the distribution of cells showing a phenotype of interest within a screen, scoring gene knockdowns according to their effect on a phenotype, and using existing biological knowledge to predict the underlying biological meaning of a phenotype and, by extension, the function of the genes that most strongly affect that phenotype. We repeat this analysis for multiple phenotypes, extracting for each a set of genes related through that phenotype, along with predictions for the biology of each phenotype. We apply our methods to a large gene-knockdown screen in human cells, validating it on known phenotypes as well as identifying and characterizing several new cellular phenotypes that have not been previously studied.
by Thouis Raymond Jones.
Sc.D.

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18

Barnett, John D. (John Derek) 1970. "Convex matrix factorization for gene expression analysis." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/30098.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 68-71).
A method is proposed for gene expression analysis relying upon convex matrix factorization (CMF). In CMF, one of the matrix factors has a convexity constraint, that is, each row is nonnegative and sums to one, and hence can be interpreted as a probability distribution. This is motivated biologically by expression data resulting from a mixture of different cell types. This thesis investigates implementing CMF with various constraints applied to the expression matrix, and applies the technique to a problem in analysis of the cell cycle and two problems in cancer classification.
by John D. Barnett.
S.M.

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19

Sharma, Vishnu Dutt. "INTERFACIAL ENGINEERING OF SYNTHETIC AMPHIPHILES AND ITS IMPACT IN THE DESIGN OF EFFICIENT GENE AND DRUG DELIVERY SYSTEMS." Diss., Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/280244.

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Pharmaceutical Sciences
Ph.D.
Cancer is currently the second most common cause of death in the world. Despite tremendous progress in the treatment of different forms of cancer, the five year survival rates for lung, colorectal, breast, prostate, pancreatic and ovarian cancers remain quite low. New therapies are urgently needed for the better management of these diseases. In this context, both therapeutic gene and drug delivery constitute promising approaches for cancer treatment and are addressed in this thesis. Focusing on gene delivery, we are proposing the use new pyridinium amphiphiles for obtaining gene delivery systems with improved stability and efficiency and low toxicity (Chapters 2 and 3). The main focus was on pyridinium gemini surfactants (GSs), which possess a soft charge, a high charge/mass ratio and a high molecular flexibility - all key parameters that recommend their use in synthetic gene delivery systems with in vitro and in vivo efficiency. In Chapter 2, we optimized a novel DNA delivery systems through interfacial engineering of pyridinium GS at the level of linker, hydrophobic chains and counterions. In Chapter 3, we tested the effects of blending pyridinium cationic GS into pyridinium cationic lipid bilayers and we have evaluated these blends towards plasmid DNA compaction and delivery process. We have also correlated the cationic bilayer composition with the dynamics of the DNA compaction process, and with transfection efficiency, cytotoxicity and internalization mechanism of resulted nucleic acid complexes. Toward improved drug delivery systems, we introduced new amphiphilic block copolymers synthesized from biocompatible and biodegradable segments. Although their capabilites for loading, transport and release of lipophilic substances stored in their hydrophobic cores are widely known, their stability in vivo is limited due to rapid degradation by esterases present in the body. In Chapter 4, we examined the possibility to increase the enzymatic stability of PEG-PCL macromolecular amphiphiles through interfacial engineering, in a process which separates the hydrophilic/hydrophobic interface from the degradable/non-degradable block interface. We evaluated the stability, toxicity, drug loading and release properties of these new polymers using docetaxel as a model chemotherapeutic drug. The results revealed how hydrophilic/ hydrophobic interface tuning can be used to adjust key properties of polymeric drug delivery systems of this type.
Temple University--Theses

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McDermott, Matthew B. A. (Matthew Brian Andrew). "Deep learning benchmarks on L1000 gene expression data." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121738.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-62).
Gene expression data holds the potential to offer deep, physiological insights about the dynamic state of a cell beyond the static coding of the genome alone. I believe that realizing this potential requires specialized machine learning methods capable of using underlying biological structure, but the development of such models is hampered by the lack of an empirical methodological foundation, including published benchmarks and well characterized baselines. In this work, we lay that foundation by profiling a battery of classifiers against newly defined biologically motivated classification tasks on multiple L1000 gene expression datasets. In addition, on our smallest dataset, a privately produced L1000 corpus, we profile per-subject generalizability to provide a novel assessment of performance that is lost in many typical analyses. We compare traditional classifiers, including feed-forward artificial neural networks (FF-ANNs), linear methods, random forests, decision trees, and K nearest neighbor classifiers, as well as graph convolutional neural networks (GCNNs), which augment learning via prior biological domain knowledge. We find GCNNs offer performance improvements given sufficient data, excelling at all tasks on our largest dataset. On smaller datasets, FF-ANNs offer greatest performance. Linear models significantly underperform on all dataset scales, but offer the best per-subject generalizability. Ultimately, these results suggest that structured models such as GCNNs can represent a new direction of focus for the field as our scale of data continues to increase.
by Matthew B. A. McDermott.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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21

Yen, Angela. "Computational epigenomics : gene regulation, comparative methodologies, and epigenetic patterns." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105953.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 203-225).
One of the fundamental aims of biology is to determine what lies at the root of differences across individuals, species, diseases, and cell types. Furthermore, the sequencing of genomes has revolutionized the ways in which scientists can investigate biological processes and disease pathways; new genome-wide, high-throughput experiments require computer scientists with a biological understanding to analyze and interpret the data to improve our understanding about life science. This provides us with a key opportunity to use computational techniques for new biological discoveries. While genetic variation plays an important role in influence phenotype, sequence alone cannot account for all differences: for example, different types of cells in an individual have varying function and attributes, but identical genetic makeup. This highlights the importance of studying epigenetic changes, which are dynamic chemical changes to and around the DNA. While the DNA of every cell in an individual is the same, the epigenetic context for that DNA varies from cell to cell. In this way, these epigenetic differences play a crucial role in gene regulation, with epigenetic changes both causing and recording regulatory mechanisms. In this thesis, we combine the power of computational, statistical, and data science approaches with the new wave of epigenetic data at a genome-wide level in a number of ways. First, in chapter 2, we demonstrate the importance of computational analysis at an epigenomic level by identifying an epigenomic signature of the olfactory receptor gene family that gives insight into the mechanism behind monogenic gene regulation. Next, in chapter 3, we explain our development of ChromDiff, a novel statistical and information theoretic computational methodology to identify chromatin state differences in groups of samples. In our methodology, we use correction for external covariates to isolate the relevant signal, and as a result, we find that our method outperforms existing computational methods, with further validation through randomized simulations. In chapter 4, we apply our methodology to characteristics including sex, developmental age, and tissue type, we unveil relevant chromatin states and genes that distinguish the groups of epigenomes, with further validation of our results through differential expression analysis and gene set enrichment. In chapter 5, we show the power of integrative analysis through the combination of DNA methylation data with chromatin state profiles, cell types, sample groups, experimental technologies, and histone mark data to reveal insightful epigenetic patterns and relationships. Finally, in chapter 6, we identify "hidden" or "unknown" covariates in epigenomic data by using agnostic principal component analysis on our samples to discover similarities between our known covariates and the identified components. In summation, our research highlights the importance of both algorithm development and method application for epigenomic questions, reaffirming the importance of interdisciplinary research that brings together cutting-edge techniques in computer science with appropriate biological hypotheses and data. While questions and analysis must be carefully paired in an informed manner to produce meaningful, interpretable, and believable results in computational biology, our work here provides a sampling of the vast potential for scientific discovery at the intersection of the fields of computer science and biology.
by Angela Yen.
Ph. D.

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22

Kemper, Corey Ann 1979. "Exploiting biological pathways to infer temporal gene interaction models." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38563.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (p. 156-166).
An important goal in genomic research is the reconstruction of the complete picture of temporal interactions among all genes, but this inference problem is not tractable because of the large number of genes, the small number of experimental observations for each gene, and the complexity of biological networks. We focus instead on the B cell receptor (BCR) signaling pathway, which narrows the inference problem and provides a clinical application, as B cell chronic lymphocytic leukemia (B-CLL) is believed to be related to BCR response. In this work, we infer population-dependent gene networks of temporal interaction within the BCR signaling pathway. We develop simple statistical models that capture the temporal behavior of differentially expressed genes and then estimate the parameters in an Expectation-Maximization framework, resulting in clusters with a biological interpretation for each subject population. Using the cluster labels to define a small number of modes of interaction and imposing sparsity constraints to effectively limit the number of genes influencing each target gene makes the ill-posed problem of network inference tractable.
(cont.) For both the clustering and the inference of the predictive models, we have statistical results that show that we successfully capture the temporal structure of and the interactions between the genes relevant to the BCR. signaling pathway. We have confirmatory results from a biological standpoint, in which genes that we have identified as playing key roles in the networks have already been shown in previous work to be relevant to BCR. stimulation, but we also have results that guide future experiments in the study of other related genes, in order to further the long term goal of a full understanding of how and why B-CLL cells behave abnormally.
by Corey Ann Kemper.
Ph.D.

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23

Yen, Angela. "Leveraging high-throughput datasets for studies of gene regulation." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66821.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 95-102).
In this thesis, I leveraged computational methods on biological data to better understand gene regulation and development of the human body, as well as of the model organisms mouse and yeast. Firstly, I tackled biological questions with machine learning techniques by studying pre-transcriptional gene regulation through nucleosome positioning, which resulted in the identification of function-specific factors and improved predictive performance. Next, computational analysis enabled the discovery of genome-wide epigenetic modifications that play a foundational role in silencing for the monoallelic and monogenic expression of olfactory receptor genes in mice. Lastly, signatures of functional, bound RNA regions provide insight into a potential protocol-specific bias and produce a new avenue for de novo discovery of functional regions.
by Angela Yen.
M.Eng.

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Deoras, Ameya Nitin. "Gene identification using phylogenetic metrics with conditional random fields." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40533.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 69-72).
While the complete sequence of the human genome contains all the information necessary for encoding a complete human being, its interpretation remains a major challenge of modern biology. The first step to any genomic analysis is a comprehensive and accurate annotation of all genes encoded in the genome, providing the basis for understanding human variation, gene regulation, health and disease. Traditionally, the problem of computational gene prediction has been addressed using graphical probabilistic models of genomic sequence. While such models have been successful for small genomes with relatively simple gene structure, new methods are necessary for scaling these to the complete human genome, and for leveraging information across multiple mammalian species currently being sequenced. While generative models like hidden Markov models (HMMs) face the difficulty of modeling both coding and non-coding regions across a complete genome, discriminative models such as Conditional Random Fields (CRFs) have recently emerged, which focus specifically on the discrimination problem of gene identification, and can therefore be more powerful. One of the most attractive characteristics of these models is that their general framework also allows the incorporation of any number of independently derived feature functions (metrics), which can increase discriminatory power. While most of the work on CRFs for gene finding has been on model construction and training, there has not been much focus on the metrics used in such discriminatory frameworks. This is particularly important with the availability of rich comparative genome data, enabling the development of phylogenetic gene identification metrics which can maximally use alignments of a large number of genomes.
(cont.) In this work I address the question of gene identification using multiple related genomes. I first present novel comparative metrics for gene classification that show considerable improvement over existing work, and also scale well with an increase in the number of aligned genomes. Second, I describe a general methodology of extending pair-wise metrics to alignments of multiple genomes that incorporates the evolutionary phylogenetic relationship between informant species. Third, I evaluate various methods of combining metrics that exploit metric independence and result in superior classification. Finally, I incorporate the metrics into a Conditional Random Field gene model, to perform unrestricted de novo gene prediction on 12-species alignments of the D. melanogaster genome, and demonstrate accuracy rivaling that of state-of-the-art gene prediction systems.
by Ameya Nitin Deoras.
S.M.

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Jackson, Jonathan Lee. "Extensible neural network software : applications in gene expression analysis." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33285.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 75-78).
Artificial Neural Networks have been increasingly utilized in the life sciences for analysis of large data sets. High-throughput technologies, such as gene expression microarrays, have challenged traditional statistical learning algorithms given their high dimensionality. This thesis describes GAINN, a neural network software package I created. GAINN was designed to be an extensible tool for both researches and students to use in neural network explorations. Several algorithms and features were implemented and tested on classification of various gene expression array data sets. The code design and user interface were implemented in such a manner that new algorithms and features would be trivial to incorporate into GAINN.
by Jonathan Lee Jackson.
M.Eng.

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26

Palmer, Nathan Patrick. "Data mining techniques for large-scale gene expression analysis." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68493.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 238-256).
Modern computational biology is awash in large-scale data mining problems. Several high-throughput technologies have been developed that enable us, with relative ease and little expense, to evaluate the coordinated expression levels of tens of thousands of genes, evaluate hundreds of thousands of single-nucleotide polymorphisms, and sequence individual genomes. The data produced by these assays has provided the research and commercial communities with the opportunity to derive improved clinical prognostic indicators, as well as develop an understanding, at the molecular level, of the systemic underpinnings of a variety of diseases. Aside from the statistical methods used to evaluate these assays, another, more subtle challenge is emerging. Despite the explosive growth in the amount of data being generated and submitted to the various publicly available data repositories, very little attention has been paid to managing the phenotypic characterization of their samples (i.e., managing class labels in a controlled fashion). If sense is to be made of the underlying assay data, the samples' descriptive metadata must first be standardized in a machine-readable format. In this thesis, we explore these issues, specifically within the context of curating and analyzing a large DNA microarray database. We address three main challenges. First, we acquire a large subset of a publicly available microarray repository and develop a principled method for extracting phenotype information from freetext sample labels, then use that information to generate an index of the sample's medically-relevant annotation. The indexing method we develop, Concordia, incorporates pre-existing expert knowledge relating to the hierarchical relationships between medical terms, allowing queries of arbitrary specificity to be efficiently answered. Second, we describe a highly flexible approach to answering the question: "Given a previously unseen gene expression sample, how can we compute its similarity to all of the labeled samples in our database, and how can we utilize those similarity scores to predict the phenotype of the new sample?" Third, we describe a method for identifying phenotype-specific transcriptional profiles within the context of this database, and explore a method for measuring the relative strength of those signatures across the rest of the database, allowing us to identify molecular signatures that are shared across various tissues ad diseases. These shared fingerprints may form a quantitative basis for optimal therapy selection and drug repositioning for a variety of diseases.
by Nathan Patrick Palmer.
Ph.D.

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27

Lin, Michael F. (Michael Fong-Jay). "Comparative gene identification in mammalian, fly, and fungal genomes." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36807.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references (leaves 55-56).
An important step in genome interpretation is the accurate identification of protein-coding genes. One approach to gene identification is comparative analysis of the genomes of several related species, to find genes that have been conserved by natural selection over millions of years of evolution. I develop general computational methods that combine statistical analysis of genome sequence alignments with classification algorithms in order to detect the distinctive signatures of protein-coding DNA sequence evolution. I implement these methods as a software system, which I then use to identify previously unknown genes, and cast doubt on some existing gene annotations, in the genomes of the fungi Saccharomyces cerevisiae and Candida albicans, the fruit fly Drosophila melanogaster, and the human. These methods perform competitively with the best existing de novo gene identification systems, and are practically applicable to the goal of improving existing gene annotations through comparative genomics.
by Michael F. Lin.
M.Eng.

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28

Steiner, Paul Jamesen. "Oligonucleotide design and codon optimization for PCR-based gene synthesis." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/61292.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-80).
If synthetic biologists are to engineer novel biological functionality, they must be able to fabricate the DNA encoding it. A number of companies synthesize DNA for a fee, but their service is opaque. Researchers can alternatively perform their own syntheses, but the process is time-consuming and error-prone. This thesis introduces a software tool designed to make it simpler and more reliable. DNA is synthesized from overlapping oligonucleotides by ligation or PCR; this thesis focuses on PCR-based methods. Many sets of oligonucleotides can be used to synthesize a given sequence; choosing the optimal set is a computational problem. A number of software tools for oligonucleotide design exist, but none are adequate. Some employ poorly-designed algorithms, while others place unnecessary restrictions on oligonucleotide length or overlap size. An optimal set of oligonucleotides for PCR-based synthesis has no potential for mis priming and has maximally uniform overlap melting temperatures. We present an algorithm that finds such a set. Unlike similar algorithms, it places no restrictions on oligo length or overlap size except those given by the user. Mason, a tool employing this algorithm, has been implemented in Common Lisp. The space of potential sets of oligos is much larger when the DNA to be synthesized contains protein-coding regions; because the genetic code is degenerate, a combinatorial number of different sequences can encode the same protein. If the primary concern is a protein sequence, codons can be changed to synonymous codons with little consequence, making it possible to remove problematic repetitive elements. We show that our algorithm can theoretically be extended and used with constraint optimization algorithms to solve the more difficult problem of simultaneously optimizing codon usage and designing oligonucleotides for synthesis.
by Paul Jamesen Steiner.
M.Eng.

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29

Herr, Taylor(Taylor J. ). "Dissecting the gene-regulatory circuitry of disease-associated genetic variants." Thesis, Massachusetts Institute of Technology, 2017. https://hdl.handle.net/1721.1/124573.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis. "June 2019."
Includes bibliographical references (pages 89-91).
Disease-associated nucleotides lie primarily in non-coding regions, increasing the urgency of understanding how gene-regulatory circuitry impacts human disease. Here, we use the increasing availability of functional genomics datasets and models elucidating how regulatory proteins control genes, to evaluate the impact of genetic variants on the activity of diverse regulators. First, we generate a comprehensive compendium of predicted binding intensities across the entire genome for over 500 transcription factors. Second, we create a novel dataset to connect how these binding intensities change in the context of disease datasets. Third, we develop a statistical framework to integrate these two datasets using dimensionality reduction, latent cluster discovery, and topic modeling. We use these techniques to show that regulatory proteins with analogous biological functions share similar global changes in binding due to genome-wide genetic variation. We also use our framework to discover a latent set of topics behind all genomic locations in chromosome 1, to link the locations in each of the topic clusters with a class of related diseases, and to show that relevant biological processes are statistically enriched in the genomic locations most related to each cluster.
by Taylor Herr.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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30

Hwang, Samuel James. "DNA as a programmable material : de novo gene synthesis and error correction." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44423.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2008.
Includes bibliographical references (leaves 42-43).
Deoxyribonucleic acid (DNA), the polymeric molecule that carries the genetic code of all living organisms, is arguably one of the most programmable assembly materials available to chemists, biologists, and materials scientists. Scientists have used DNA to build many different structures for various applications in disparate areas of research from traditional biological applications to more recent non-biological applications. Although DNA isn't typically thought of as an assembly material by people not doing research in the area, the availability of decreasing cost synthetic oligonucleotides has led to advances in gene fabrication technology which in turn has enabled synthetic biology to flourish. Using DNA as a building material for small and large constructs of DNA is reliant on having effective gene synthesis techniques. Construction of synthetic DNA is limited by errors that pervade the final product. To address this problem, effective error correction methods are pivotal. Having extremely robust gene synthesis and error correction techniques will allow researchers to generate very large scale constructs potentially necessary in applications such as genome re-engineering.
by Samuel James Hwang.
S.M.

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31

Liu, Brendan F. "Developing a gene model for simulations that incorporates multi-species conservation." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91838.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-71).
The genetic architecture, the number, frequency, and effect size of disease causing alleles for many common diseases including Type 2 Diabetes is not fully understood. Genetic simulations can be used to make predictions under specified genetic architecture models. Models whose predictions are inconsistent with empirical data can be rejected. We extended a gene simulation model previously published by our lab. The distribution of number and length of coding and intron regions of each simulated gene was consistent with the distribution in the human genome. Selection pressure against mutations was modeled by utilizing the cross-species conservation of each region. The combined distribution of variants by their frequency over 500 genes was compared between the simulated genes and the corresponding empirical data. This distribution of variants between the simulated and empirical data was found to be consistent.
by Brendan F. Liu.
M. Eng.

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Jammalamadaka, Arvind K. (Arvind Kumar) 1981. "Some methods and models for analyzing time-series gene expression data." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53278.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 199-203).
Experiments in a variety of fields generate data in the form of a time-series. Such time-series profiles, collected sometimes for tens of thousands of experiments, are a challenge to analyze and explore. In this work, motivated by gene expression data, we provide several methods and models for such analysis. The methods developed include new clustering techniques based on nonparametric Bayesian procedures, and a confirmatory methodology to validate that the clusters produced by any of these methods have statistically different mean paths.
by Arvind K. Jammalamadaka.
Ph.D.

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33

Kitzman, Jacob O. "Computational prediction of RNA-based gene regulatory mechanisms in human and Tetrahymena." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37206.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.
Includes bibliographical references.
The diversity and profound impact of gene regulation mediated by small RNAs (sRNAs) is just beginning to come into focus. RNA interference (RNAi) pathways have been shown to mediate processes such as genomic rearrangement in ciliates and developmental timing and tissue differentiation in plants and animals. Here we present a computational study into the function of two distinct classes of sRNAs. In the first section, we examine an uncharacterized class of sRNAs isolated from the ciliate Tetrahymena thermophila, present functional comparison to known classes of sRNAs in other organisms, and note a strong and specific relationship to a novel sequence motif. In the second section, we examine the evolutionary impact of microRNAs (miRNAs), which mediate potent post-transcriptional repression on their targets. We observe that miRNAs with tissue-specific expression exert remarkable evolutionary pressure, compelling many preferentially coexpressed genes to avoid accumulating target sites. We present tissue-specific patterns of such target depletion and note strong agreement with experimentally obtained miRNA expression patterns. Conversely, we report enrichment for targeting among genes with expression patterns spatially or temporally complementary to the miRNAs', suggesting a widespread role of tissue identity maintenance for miRNA-mediated regulation.
by Jacob O. Kitzman.
M.Eng.

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34

Rasmussen, Matthew D. (Matthew David). "Methods and analysis of genome-scale gene family evolution across multiple species." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62433.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 123-136).
The fields of genomics and evolution have continually benefited from one another in their common goal of understanding the biological world. This partnership has been accelerated by ever increasing sequencing and high-throughput technologies. Although the future of genomic and evolutionary studies is bright, new models and methods will be needed to address the growing and changing challenges of large-scale datasets. In this work, I explore how evolution generates the diversity of life we see in modern species, specifically the evolution of new genes and functions. By reconstructing the history of the diverse sequences present in modern species, we can improve our understanding of their function and evolutionary importance. Performing such an analysis requires a principled and efficient means of computing the most probable evolutionary scenarios. To address these challenges, I introduce a new model of gene family evolution as well as a new method SPIMAP, an efficient Bayesian method for reconstructing gene trees in the presence of a known species tree. We observe many improvements in reconstruction accuracy, achieved by modeling multiple aspects of evolution, including gene duplication and loss rates, speciation times, and correlated substitution rate variation across both species and loci. I have implemented and applied this method on two clades of fully-sequenced species, 12 Drosophila and 16 fungal genomes as well as simulated phylogenies, and find dramatic improvements in reconstruction accuracy as compared to the most popular existing methods, including those that take the species tree into account. Lastly, I use the SPIMAP method to reconstruct the evolutionary history of all gene families in 16 fungal species including several relatives of the pathogenic species C. albicans. From these reconstructions, we identify several families enriched with duplications and positive selection in pathogenic lineages. Theses reconstructions shed light on the evolution of these species as well as a better understanding of the genes involved in pathogenicity.
by Matthew D. Rasmussen.
Ph.D.

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Chen, Shuodan. "Regulation of lubricin gene expression and synthesis in cartilage by mechanical injury." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/55094.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Articular cartilage is the connective tissue which lines the bony ends of diathrodial joints to provide load distribution and frictionless motion. Lubricin, a glycoprotein which concentrates at the superficial layer of the cartilage, contributes to the low friction coefficient. Mechanical injury to cartilage increases the risk of osteoarthritis (OA), characterized by degradation of articular cartilage starting with the articular surface. The objectives of this study were to quantify the effects of injurious compression on the surface mechanical properties of cartilage, and lubricin gene expression and synthesis using an in vitro organ culture model. Furthermore, the role of TGF-P signaling in the induction of lubricin gene expression and protein secretion from cartilage explants following mechanical injury was analyzed. Cartilage disks with intact superficial zone from the patellofemoral grooves of 1-2 wk old bovine knees were cultured in either free swelling conditions or subjected to injurious compression using a range of applied strains and strain rates. Mechanical injury was found to elevate the friction coefficient of cartilage. Average surface roughness of cartilage superficial zone was increased by the combination of injury and subsequent oscillating shear motion at the surface superimposed on an applied normal strain. RNA extraction and qRT-PCR were conducted sequentially to determine the expression of lubricin and other relevant cartilage genes. Western blotting and ELISA were used to assess protein expression. Lubricin gene expression and secretion increased two days after injury.
(cont.) This finding, plus the fact that injury and TGF-f are each known to increase lubricin expression, suggested that the TGF-3 signaling pathway may be a mechanism through which injury induces lubricin expression. We therefore tested the hypothesis that blocking the TGF-P pathway would suppress the increase in lubricin gene expression and protein secretion caused by injurious compression of cartilage. Indeed, lubricin gene expression and protein secretion were reduced after blocking TGF-f compared to injury alone. Together, these results show that surface damage caused injury and sliding motion can be ameliorated by the presence of lubricin on the cartilage surface. The TGF-3 pathway is an important mechanism in regulating the increased lubricin gene expression and secretion that result from injury.
by Shuodan Chen.
Ph.D.

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36

Gerber, Georg Kurt 1970. "Do the time-wrap: continuous alignment of gene expression time-series data." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87336.

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Krishnamoorthy, Mahentha. "Developing cationic nanoparticles for gene delivery." Thesis, Queen Mary, University of London, 2016. http://qmro.qmul.ac.uk/xmlui/handle/123456789/23193.

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Gene delivery can potentially treat acquired and genetic diseases such as cystic fibrosis, haemophilia and cancer. Non-viral gene delivery vectors are attractive candidates over viral vectors such as recombinant viruses, due to their lower cytotoxicity and immunogenicity, despite significantly lower transfection efficiencies. To improve efficiency of non-viral vectors, the investigation of the various parameters influencing DNA transfection is essential. The present study developed a versatile gene delivery system with tailored physicochemical and biological properties. The system used polymer brushes synthesised via atomic transfer radical polymerisation (ATRP), grafted from silica nanoparticles, whose charge density, grafting density, chemistry, length of brush, the size and shape can be altered. The primary focus of the study was poly(2-dimethylaminoethyl methacrylate) (PDMAEMA), known for its positive charge and DNA condensation. The ability of PDMAEMA to interact with DNA was characterised using dynamic light scattering, electrophoretic light scattering methods, surface plasmon resonance and in situ ellipsometry whilst its interaction with cells was studied via cell viability assays. The brush behaviour in response to pH and ionic strength was also studied. The charge density was altered by copolymerising with poly[oligo(ethylene glycol) methyl ether methacrylate](POEGMA) and the effect of such modification on DNA interaction was studied. PDMAEMA-grafted nanoparticles gave the highest transfection efficiency compared to other synthesised polymer brushes, but still displaying almost 2-fold lower transfection efficiency than the commercially available reagent jetPEI®. Different brush chemistries were also investigated. Poly(glycidyl methacrylate) (PGMA) decorated with oligoamines: allylamine, diethylenetriamine and pentaethylene hexamine, and PDMAEMA quaternized with alkyl halides: methyl iodide, allyl iodide and ethyl iodoacetate did not show any significant transfection, despite their performance reported in the literature. The robust system developed is a promising platform for further investigation of parameters influencing cellular uptake and gene expression, and important milestone to develop non-viral gene delivery systems.

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Wozniak, Amanda Victrix Allen. "A systematic and extensible approach to DNA primer design for whole gene synthesis." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/37059.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (leaves 93-96).
The future of synthetic biology research hinges upon the development of accurate and inexpensive whole gene synthesis technologies. Recent advances in the purification of solid-phase manufactured oligonucleotides make it possible to manufacture whole genes by polymerase chain reaction methods. Yet, despite the improvement in laboratory methods, whole gene synthesis is not rapidly progressing because most gene design software takes an excessively naive approach to the complex problem of designing component oligonucleotides for whole gene synthesis. The synthetic biology community needs a flexible, robust and optimal primer design tool. We present the software design for a tool which designs oligonucleotides that are compatible with a wide variety of oligo purification and whole gene assembly protocols. Our design strategy uses physical sequence feature identification, optimal artificial intelligence search techniques, and sequence optimisation via intelligent codon substitution to produce near-optimal oligonucleotide arrays. We address all aspects of the oligonucleotide design problem, from physical constraints to the computational overhead involved in searching for an optimal solution, and provide an extensive set of data structures and algorithms.
by Amanda Victrix Allen Wozniak.
M.Eng.

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39

Fu, Benson 1978. "Design of a genetics database for gene clips and the Human Genome database." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/86702.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (leaves 34-36).
by Benson Fu.
M.Eng.and S.B.

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40

Tsankov, Alex. "Evolution of nucleosome positioning and gene regulation in yeasts : a genomic and computational approach." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62464.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 107-111).
Chromatin organization plays a major role in gene regulation and can affect the function and evolution of new transcriptional programs. Here, we present the first multi-species comparative genomic analysis of the relationship between chromatin organization and gene expression by measuring mRNA abundance and nucleosome positions genome-wide in 13 Ascomycota yeast species. Our work introduces a host of new computational tools for studying chromatin structure, function, and evolution. We improved on existing methods for detecting nucleosome positions and developed a new approach for identifying nucleosome-free regions (NFRs) and characterizing chromatin organization at gene promoters. We used a general statistical approach for studying the evolution of chromatin and gene regulation at a functional level. We also introduced a new technique for discovering the DNA binding motifs of transacting General Regulatory Factors (GRFs) and developed a new technique for quantifying the relative contribution of intrinsic sequence, GRFs, and transcription to establishing NFRs. And finally, we built a computational framework to quantify the evolutionary interplay between nucleosome positions, transcription factor binding sites, and gene expression. Through our analysis, we found large conservation of global and functional chromatin organization. Chromatin organization has also substantially diverged in both global quantitative features and in functional groups of genes. We find that global usage of intrinsic anti-nucleosomal sequences such as PolyA varies over this phylogeny, and uncover that PolyG tracts also intrinsically repel nucleosomes. The specific sequences bound by GRFs are also highly plastic; we experimentally validate an evolutionary handover from Cbfl in pre-WGD yeasts to Rebi in post-WGD yeast. We also identify five mechanisms that couple chromatin organization to evolution of gene regulation, including (i) compensatory evolution of alternative modifiers associated with conserved chromatin organization; (ii) a gradual transition from constitutive to transregulated NFRs; (iii) a loss of intrinsic anti-nucleosomal sequences accompanying changes in chromatin organization and gene expression, (iv) repositioning of motifs from NFRs to nucleosome-occluded regions; and (v) the expanded use of NFRs by paralogous activator-repressor pairs. Our multi-species dataset and general computational framework provide a foundation for future studies on how chromatin structure changes over time and in evolution.
by Alexander Minchev Tsankov.
Ph.D.

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41

Mitra, Robi David. "Polony sequencing : DNA sequencing technology and a computational analysis reveals chromosomal domains of gene expression." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/8797.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references.
The first part of this thesis describes the development of polony sequencing, a sequencing technology in which DNA is cloned, amplified and sequenced in a polymer matrix. A complex library of one to ten million linear DNA molecules is amplified by performing polymerase chain reaction (PCR) in a thin polyacrylamide film poured on a glass microscope slide. The polyacrylamide matrix retards the diffusion of the DNA molecules so that each amplification product remains localized near its parent molecule. At the end of the reaction, a number of polymerase colonies, or "polonies", have formed, each one grown from a single template molecule. As many as 5 million clones can be amplified in parallel on a single slide. By including an acrydite modification at the 5' end of one of the PCR primers, the amplified DNA will be covalently attached to the polyacrylamide matrix, allowing further enzymatic manipulations to be performed on all clones simultaneously. Also described in this thesis is my progress in development of a protocol to sequence the polonies by repeated cycles of extension with fluorescent deoxynucleotide. Because polony sequencing is inherently parallel, and sub-picoliter volumes are used for each reaction, the technology should be substantially faster and cheaper than existing methods. Applications for polony sequencing such as gene expression analysis, SNP discovery, and SNP screening will also be discussed. The second part of this thesis describes a computational analysis that tests the hypothesis that chromosomal position affects gene expression. It is shown that, throughout the genome, genes lying close together on the same chromosome often show significant coexpression. This coexpression is independent of the orientation of genes to each other, but is dependent on the distance between genes. In several cases where adjacent genes show highly correlated expression, the promoter of only one of the genes contains an upstream activating sequence (UAS) known to be associated with the expression pattern. These results suggest that in certain regions of the genome a single transcription factor binding site may regulate several genes. It is also shown that evolution may take advantage of this phenomenon by keeping genes with similar functions in adjacent positions along the chromosomes. The techniques that are presented provide a computational method to delineate the locations of chromosomal domains and identify the boundary elements that flank them.
Robi David Mitra.
Ph.D.

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42

Yang, Benson P. (Benson Pin-Sheng). "Regulation of Tau gene expression by Aβ and the amyloid precursosr protein in cultured cortical neurons." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/44497.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
Includes bibliographical references (leaves 44-52).
by Benson P. Yang.
M.Eng.

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43

Lazarovici, Allan 1979. "Development of gene-finding algorithms for fungal genomes : dealing with small datasets and leveraging comparative genomics." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29681.

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Thesis (M.Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (leaves 60-62).
A computer program called FUNSCAN was developed which identifies protein coding regions in fungal genomes. Gene structural and compositional properties are modeled using a Hidden Markov Model. Separate training and testing sets for FUNSCAN were obtained by aligning cDNAs from an organism to their genomic loci, generating a 'gold standard' set of annotated genes. The performance of FUNSCAN is competitive with other computer programs design to identify protein coding regions in fungal genomes. A technique called 'Training Set Augmentation' is described which can be used to train FUNSCAN when only a small training set of genes is available. Techniques that combine alignment algorithms with FUNSCAN to identify novel genes are also discussed and explored.
by Allan Lazarovici.
M.Eng.and S.B.

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44

Kim, Samuel Sungil. "Leveraging biological pathways and gene networks to understand the genetic architecture of diseases and complex traits." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122548.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 73-82).
Recent studies have highlighted the role of biological pathways and gene networks in disease biology. In this thesis, we formally assess (1) the contribution of disease-associated gene pathways to disease heritability, (2) the contribution of genes with high network connectivity in known gene networks to disease heritability, and (3) the contribution of genes with high network connectivity to disease-associated gene pathways to disease heritability. We constructed a broad set of pathway, network, and pathway+network annotations and applied stratified LD score regression to 42 independent diseases and complex traits (average N=323K) to identify enriched annotations. We demonstrate gene network connectivity is highly informative for disease architectures, but the information in gene networks may be subsumed by regulatory annotations, such that accounting for known annotations is critical to robust inference of biological mechanisms.
by Samuel Sungil Kim.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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45

Riemann, Reina 1975. "Uncovering transcriptional regulators by combined relevance analysis of genome-wide transcription-factor binding data and gene expression." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87851.

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46

Wang, Yufei. "Ontology engineering the brain gene ontology case study : submitted by Yufei Wang ... in partial fulfillment of the requirements for the degree of Master of Computer and Information Sciences, Auckland University of Technology, March 2007." Click here access this resource online, 2007. http://aut.researchgateway.ac.nz/handle/10292/104.

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Thesis (MCIS - Computer and Information Sciences) --AUT University, 2007.
Includes bibliographical references. Also held in print (ix, 74 leaves : ill. ; 30 cm.) in City Campus Theses Collection (T 006.33 WAN)

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47

Kalluru, Vikram Gajanan. "Identify Condition Specific Gene Co-expression Networks." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338304258.

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48

D'mello, Sheetal Reginald. "Natural polymer based gene activated matrices for bone regeneration." Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1586.

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Gene therapy using non-viral vectors that are safe and efficient at transfecting target cells is an effective approach to overcome the shortcomings of delivery of growth factors in protein form. The objective of this study was to develop and test a non-viral gene delivery system for bone regeneration utilizing a collagen scaffold carrying polyethylenimine (PEI)-plasmid DNA (pDNA) complexes. Two different pDNA were used: pDNA encoding platelet derived growth factor-B (PDGF-B) and pDNA encoding vascular endothelial growth factor (VEGF). The complexes were fabricated at an amine (N) to phosphate (P) ratio of 10 and then characterized for size, surface charge, as well as in vitro cytotoxicity and transfection efficacy in human bone marrow stromal cells (BMSCs). The influence of the PEI-pPDGF-B complex-loaded collagen scaffold on cellular attachment and recruitment was evaluated in vitro using microscopy techniques. The in vivo regenerative capacity of the gene delivery system, using PEI-pPDGF-B and PEI-pVEGF complexes, was assessed in 5 mm diameter critical-sized calvarial defects in Fisher 344 rats. A different biomaterial, chitosan, loaded with copper was also evaluated in vivo. The complexes were ∼100 nm in size with a positive surface charge. Complexes prepared at an N/P ratio of 10 displayed low cytotoxicity as assessed by a cell viability assay. High magnification scanning electron microscopy imaging demonstrated the recruitment and attachment of BMSCs into the collagen scaffold containing PEI-pPDGF-B complexes. Confocal microscopy revealed significant proliferation of BMSCs on PEI-pPDGF-B complex-loaded collagen scaffolds compared to empty scaffolds. In vivo studies showed significantly higher new bone volume/total volume (BV/TV) % in calvarial defects treated with the PEI-pPDGF-B complex-activated collagen scaffolds following 4 weeks of implantation when compared to the other treatment groups. Together these findings suggest that non-viral PDGF-B gene-activated collagen scaffolds effectively promote bone regeneration and are an attractive gene delivery system with significant potential for clinical translation.

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49

Ayuso, Anna Maria E. "Automation of Drosophila gene expression pattern image annotation : development of web-based image annotation tool and application of machine learning methods." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66403.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 91-92).
Large-scale in situ hybridization screens are providing an abundance of spatio-temporal patterns of gene expression data that is valuable for understanding the mechanisms of gene regulation. Drosophila gene expression pattern images have been generated by the Berkeley Drosophila Genome Project (BDGP) for over 7,000 genes in over 90,000 digital images. These images are currently hand curated by field experts with developmental and anatomical terms based on the stained regions. These annotations enable the integration of spatial expression patterns with other genomic data sets that link regulators with their downstream targets. However, the manual curation has become a bottleneck in the process of analyzing the rapidly generated data therefore it is necessary to explore computational methods for the curation of gene expression pattern images. This thesis addresses improving the manual annotation process with a web-based image annotation tool and also enabling automation of the process using machine learning methods. First, a tool called LabelLife was developed to provide a systematic and flexible way of annotating images, groups of images, and shapes within images using terms from a controlled vocabulary. Second, machine learning methods for automatically predicting vocabulary terms for a given image based on image feature data were explored and implemented. The results of the applied machine learning methods are promising in terms of predictive ability, which has the potential to simplify and expedite the curation process hence increasing the rate that biologically significant data can be evaluated and new insights can be gained.
by Anna Maria E. Ayuso.
M.Eng.

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50

Daum, Marilyn. "Geneplanner : a prototype of an expert system to assist with chemical DNA gene synthesis planning /." Online version of thesis, 1989. http://hdl.handle.net/1850/10576.

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Dissertations / Theses on the topic 'GEME] Engineering Sciences'

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