Relevant bibliographies by topics / Gene regulatory networks – Research / Dissertations / Theses
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Dissertations / Theses on the topic 'Gene regulatory networks – Research'

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Published: 4 June 2021
Last updated: 3 February 2022

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1

Nguyen, Lan K. "Dynamical modelling of feedback gene regulatory networks." Diss., Lincoln University, 2009. http://hdl.handle.net/10182/1340.

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Living cells are made up of networks of interacting genes, proteins and other bio-molecules. Simple interactions between network components in forms of feedback regulations can lead to complex collective dynamics. A key task in cell biology is to gain a thorough understanding of the dynamics of intracellular systems and processes. In this thesis, a combined approach of mathematical modelling, computational simulation and analytical techniques, has been used to obtain a deeper insight into the dynamical aspects of a variety of feedback systems commonly encountered in cells. These systems range
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2

Arda, H. Efsun. "C. Elegans Metabolic Gene Regulatory Networks: A Dissertation." eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/479.

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In multicellular organisms, determining when and where genes will be expressed is critical for their development and physiology. Transcription factors (TFs) are major specifiers of differential gene expression. By establishing physical contacts with the regulatory elements of their target genes, TFs often determine whether the target genes will be expressed or not. These physical and/or regulatory TF-DNA interactions can be modeled into gene regulatory networks (GRNs), which provide a systems-level view of differential gene expression. Thus far, much of the GRN delineation efforts focused on m
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3

Martinez, Natalia Julia. "Delineating the C. elegans MicroRNA Regulatory Network: A Dissertation." eScholarship@UMMS, 2009. https://escholarship.umassmed.edu/gsbs_diss/411.

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Metazoan genomes contain thousands of protein-coding and non-coding RNA genes, most of which are differentially expressed, i.e., at different locations, at different times during development, or in response to environmental signals. Differential gene expression is achieved through complex regulatory networks that are controlled in part by two types of trans-regulators: transcription factors (TFs) and microRNAs (miRNAs). TFs bind to cis-regulatory DNA elements that are often located in or near their target genes, while microRNAs hybridize to cis-regulatory RNA elements mostly located in the 3’
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4

Ingram, Piers J. "Modelling gene regulatory networks." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/1375.

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This thesis presents the results of mathematical modeling of both individual genes and small networks of genes. The regulation of gene activity is essential for the proper functioning of cells, which employ a variety of molecular mechanisms to control gene expression. Despite this, there is considerable variation in the precise number and timing of protein molecules that are produced. This is because gene expression is fundamentally a noisy process, subject to a number of sources of randomness, including uctuations in metabolite levels, the environment and ampli ed by the very low number of mo
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5

Choudhary, Ashish. "Intervention in gene regulatory networks." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4284.

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In recent years Boolean Networks (BN) and Probabilistic Boolean Networks (PBN) have become popular paradigms for modeling gene regulation. A PBN is a collection of BNs in which the gene state vector transitions according to the rules of one of the constituent BNs, and the network choice is governed by a selection distribution. Intervention in the context of PBNs was first proposed with an objective of avoid- ing undesirable states, such as those associated with a disease. The early methods of intervention were ad hoc, using concepts like mean first passage time and alteration of rule based str
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6

Dejori, Mathäus. "Inference modeling of gene regulatory networks." [S.l.] : [s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=978883071.

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7

Hache, Hendrik. "Computational analysis of gene regulatory networks." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2009. http://dx.doi.org/10.18452/16043.

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Genregulation bezeichnet die geregelte Steuerung der Genexpression durch das Zusammenspiel einer Vielzahl von Transkriptionsfaktoren die in ihrer Gesamtheit hoch komplexe und zell-spezifische genregulatorische Netzwerke bilden. Im Rahmen meiner Arbeit beschäftigte ich mich mit zwei Ansätzen der computergestützten Analyse solcher Netzwerke, Modellierung und Reverse Engineering. Der erste Teil meiner Arbeit beschreibt die Entwicklung der Web-Anwendung GEne Network GEnerator (GeNGe). Hierbei handelt es sich um ein System für die automatische Erzeugung von genregulatorischen Netzwerken. Hierfür en
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8

Santos, Bruno Acácio de Castro Moreira dos. "Small RNAs in gene regulatory networks." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708543.

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9

Schlitt, Thomas. "Towards reverse engineering gene regulatory networks." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615835.

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10

Cooper, Max B. "Evolution of small gene regulatory networks." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495599.

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11

Kadelka, Claus Thomas. "Robustness Analysis of Gene Regulatory Networks." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/73302.

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Cells generally manage to maintain stable phenotypes in the face of widely varying environmental conditions. This fact is particularly surprising since the key step of gene expression is fundamentally a stochastic process. Many hypotheses have been suggested to explain this robustness. First, the special topology of gene regulatory networks (GRNs) seems to be an important factor as they possess feedforward loops and certain other topological features much more frequently than expected. Second, genes often regulate each other in a canalizing fashion: there exists a dominance order amidst the re
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12

Wang, Jian. "Recovering Bayesian networks with applications to gene regulatory networks." Connect to online resource, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3273725.

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13

Ozbudak, Ertugrul M. (Ertugrul Mustafa) 1976. "Noise and multistability in gene regulatory networks." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/16644.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2004.<br>Includes bibliographical references (leaves 103-112).<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Proteins are the functional machinery in living cells. Proteins interact with each other and bind to DNA to form so-called gene regulatory networks and in this way regulate the level, location and timing of expression of other proteins. Cells implement feedback loops to create a memory of their gene expressio
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14

Polynikis, Athanasios. "Modeling and Analysis of Gene Regulatory Networks." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.520250.

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15

Mitchell, Elaine Irwin. "Spatio-temporal modelling of gene regulatory networks." Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/259d76f6-76cf-474d-a26a-2802808b126e.

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16

Murrugarra, Tomairo David M. "Algebraic Methods for Modeling Gene Regulatory Networks." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/28388.

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So called discrete models have been successfully used in engineering and computational systems biology. This thesis discusses algebraic methods for modeling and analysis of gene regulatory networks within the discrete modeling context. The first chapter gives a background for discrete models and put in context some of the main research problems that have been pursued in this field for the last fifty years. It also outlines the content of each subsequent chapter. The second chapter focuses on the problem of inferring dynamics from the structure (topology) of the network. It also discusses the c
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17

Ouma, Zachary Wilberforce. "Topological Properties of Eukaryotic Gene Regulatory Networks." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512041623395438.

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18

Touchard, Samuel. "Bayesian inference of gene-miRNA regulatory networks." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/8726/.

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Nowadays, in the post-genomics era, one of the major tasks and challenges is to decipher how genes are regulated. The miRNAs play an essential regulatory role in both plants and animals. It has been estimated that about 30% of the genes in the human genome are down-regulated by microRNAs (miRNAs), short RNA molecules which repress the translation of proteins of mRNAs in animals and plants. Genes which are regulated by a miRNA are called targets of this given miRNA. Hence, the task is to try to determine which miRNAs regulate which genes, in order then to build a network of these DNA components
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19

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 networ
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20

Mairesse, Benjamin. "Robustness of Bistable Switches in Gene Regulatory Networks." Thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-106248.

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Gene expression often reacts to intra- and extra-cellular signals in an ”on-off” switchlike manner. These switches, which display hysteresis, correspond to multiple steady-state solutions of the gene regulatory network and are created through saddle-node bifurcations at which a single real eigenvalue of the local Jacobian crosses the imaginary axis for changes in some system parameter. From control theory, it is well known that such an instability can be induced by a single positive feedback loop, possibly involving only a single gene and its corresponding mRNA. However, experimental results h
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21

Kentzoglanakis, Kyriakos. "Reconstructing gene regulatory networks : a swarm intelligence framework." Thesis, University of Portsmouth, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.523619.

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22

Werhli, Adriano Velasque. "Reconstruction of gene regulatory networks from postgenomic data." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/3198.

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An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. The recent substantial increase in the availability of such data has stimulated the interest in inferring the networks and pathways from the data themselves. The main interests of this thesis are the application, evaluation and the improvement of machine learning methods applied to the reverse engineering of biochemical pathways and networks. The thesis starts with the application of an established method to newly available gene expression data related to the interfer
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23

Yu, Le. "Interference of gene regulatory networks using Boolean models." Thesis, University of Strathclyde, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.501829.

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24

Pakka, Vijayanarasimha Hindupur. "Dynamics of molecular fluctuations in gene regulatory networks." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/71823/.

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The components that are central to cellular processing are proteins, whose production is regulated by other proteins known as transcription factors. Proteins are products of genes that regulate the expression of one another, thereby forming large gene regulatory networks that perform specific cellular functions. The complex connectivity between genes of a network could result in various behaviours that are interesting. The assumption then is that tracking subnetwork behaviour helps understand the characteristics of the larger networks they are embedded in. For example, the structure of a subne
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25

Rodrigo, Tarrega Guillermo. "Computational design and designability of gene regulatory networks." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/14179.

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Nuestro conocimiento de las interacciones moleculares nos ha conducido hoy hacia una perspectiva ingenieril, donde diseños e implementaciones de sistemas artificiales de regulación intentan proporcionar instrucciones fundamentales para la reprogramación celular. Nosotros aquí abordamos el diseño de redes de genes como una forma de profundizar en la comprensión de las regulaciones naturales. También abordamos el problema de la diseñabilidad dada una genoteca de elementos compatibles. Con este fin, aplicamos métodos heuríticos de optimización que implementan rutinas para resolver problemas inver
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26

Takane, Marina. "Inference of gene regulatory networks from large scale gene expression data." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80883.

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With the advent of the age of genomics, an increasing number of genes have been identified and their functions documented. However, not as much is known of specific regulatory relations among genes (e.g. gene A up-regulates gene B). At the same time, there is an increasing number of large-scale gene expression datasets, in which the mRNA transcript levels of tens of thousands of genes are measured at a number of time points, or under a number of different conditions. A number of studies have proposed to find gene regulatory networks from such datasets. Our method is a modification of th
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27

Bellot, Pujalte Pau. "Study of gene regulatory networks inference methods from gene expression data." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/406095.

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A cell is a the basic structural and functional unit of every living thing, it is protein-based an that regulates itself. The cell eats to stay alive, it grows and develops; reacting to the environment, while subjected to evolution. It also makes copies of itself. These processes are governed by chain of chemical reactions, creating a complex system. The scientific community has proposed to model the whole process with Gene Regulatory Networks (GRN). The understanding of these networks allows gaining a systems-level acknowledgment of biological organisms and also to genetically related disease
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28

Abedi, Vida. "System-identification of gene regulatory networks by systematic gene perturbation analysis." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28254.

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Systems biology is an interdisciplinary field that combines engineering and molecular biology to better understand the 'design principles' of biological systems. One of the main goals in systems biology is to understand and map complex biological networks. In order to achieve this goal, tools able to process the non-linearity and high dimensionality of biological systems are urgently needed. To develop, test and benchmark tools for investigation of biological processes, it is important to utilize a model system that is able to provide a glimpse of complexity found in higher organisms, and be s
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29

Miller, Andrew Keith. "Models of gene regulatory networks and other biological systems." Thesis, University of Auckland, 2011. http://hdl.handle.net/2292/8587.

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Mathematical modelling of biological processes is important in systems biology, because it facilitates understanding of the nature and behaviour of biological systems. This thesis is in two parts, the first on constructing and validating Gene Regulatory Network (GRN) models, and the second on mathematical model representation. A method, BaSeTraM, for identifying transcription factor (TF) binding sites from position weight matrices was developed. The sites identi_ed were used to build a GRN by identifying genes near each site. BaSeTraM performed comparably to a widely used method when v
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30

Tan, Mehmet. "Induction And Control Of Large-scale Gene Regulatory Networks." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610619/index.pdf.

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Gene regulatory networks model the interactions within the cell and thus it is essential to understand their structure and to develop some control mechanisms that could effectively deal with them. This dissertation tackles these two aspects. To handle the first problem, a new constraint-based modeling algorithm is proposed that can both increase the quality of the output and decrease the computational requirements for learning the structure of gene regulatory networks by integrating multiple biological data types and applying a special method for dense nodes in the network. Constraint-based st
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31

Jedrysiak, Daniel K. "Construction and Characterization of Gene Regulatory Networks in Yeast." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24000.

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Two major roadblocks in synthetic biology are the difficulties associated with the physical assembly of gene regulatory networks (GRNs) and the lack of characterized biological parts. In this work we aimed to address both of these issues. We developed a novel method for the assembly of GRNs called Brick- Mason assembly. We have shown that the method can assemble a 6 part network in a single day and provides significant advancements over traditional cloning methods. We used BrickMason to assemble GRNs that would allow us to compare natural yeast mechanisms of repression to the steric hindrance
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32

Chen, Xiaohui. "Comparisons of statistical modeling for constructing gene regulatory networks." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/4068.

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Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription f
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33

Bilal, R. "Dynamics of gene regulatory networks in the immune system." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1386220/.

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The aim of this project is to study the dynamics of Gene Regulatory Networks (GRN) in the immune system. For this purpose two principles of regulation were discussed. First principle is to study the effect of the speed with which the system crosses the critical region on choosing the final attractor in bistable system and in the immune system genetic switch that governs the differentiation of progenitor cell in the presence of small asymmetries and fluctuations. Second is to study the constructive role played by high-frequency force driving vibrational resonance (VR), and the effect of interac
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34

Petratou, Kleio. "Investigating gene regulatory networks underlying zebrafish pigment cell development." Thesis, University of Bath, 2017. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723314.

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The multipotent cells comprising the vertebrate neural crest (NC) generate an astonishing array of derivatives, including neuronal, skeletal and adrenal components and pigment cells. Zebrafish possess three chromatophores lineages, melanophores, iridophores and xanthophores, which lend themselves to investigating the complex gene regulatory networks (GRNs) underlying fate segregation of NC progenitors. Although the core GRN governing melanophore specification has been previously established, those guiding iridophore and xanthophore development remain elusive. This study explores the iridophore
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35

Dutan, Polit Lucia Margarita. "Identifying gene regulatory networks during neuronal differentiation of iPSCs." Thesis, King's College London (University of London), 2018. https://kclpure.kcl.ac.uk/portal/en/theses/identifying-gene-regulatory-networks-during-neuronal-differentiation-of-ipscs(624a488f-ff78-4da0-982c-169943681ddd).html.

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Neural induction is the earliest step in the formation of the human nervous system. However, the regulatory signals underlying neural induction are largely unknown due to ethical, technical and legal restrictions that limit access to live human cells during this stage. Dual SMAD inhibition induces ESC/iPSCs cells to acquire neural fate, providing an exceptional in vitro system to analyze neural induction. Transcriptome analyses enabled identification of sets of genes down-regulated during the initial 2 days of neural differentiation including pluripotency markers OCT4, NANOG and MYC. Subsequen
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Åkesson, Julia. "Robust Community Predictions of Hubs in Gene Regulatory Networks." Thesis, Linköpings universitet, Bioinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153200.

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Many diseases, such as cardiovascular diseases, cancer and diabetes, originate from several malfunctions in biological systems. The human body is regulated by a wide range of biological systems, composed of biological entities interacting in complex networks, responsible for carrying out specific functions. Some parts of the networks, such as hubs serving as master regulators, are more important for maintaining a function. To find the cause of diseases, where hubs are possible disease regulators, it is critical to know the structure of these biological systems. Such structures can be reverse e
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Camellato, Brendan. "Gene Regulatory Networks are a Mechanism for Drug Resistance." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37099.

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Multidrug resistance has become a major issue in the treatment of both microbial infections and cancers. While genetically encoded drug resistance is fairly well understood, it cannot explain all observed cases of resistance, namely the ability of a subset of disease cells to persist in an otherwise susceptible population. This non-genetic resistance requires the heterogeneous expression of a drug resistance phenotype, which can be produced by certain gene regulatory network architectures. Two particular network motifs, the coherent feedforward loop (CFFL) and the positive feedback loop (PFL),
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Lapedriza, Alberto. "Gene regulatory network of melanocyte development." Thesis, University of Bath, 2016. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.690724.

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Greenhill et al. (2011) developed a gene regulatory network of the main genes and interactions known to play a role in melanocyte biology, and generated a mathematical model to describe the behaviour of this complex network using semi quantitative data (ISH expression data). In this project we sought to collect expression data from four genes of the melanocyte GRN (sox10, kit, mitfa and dct) to develop a quantitative model that is able to describe the data more accurately. Moreover, we intended to identify more genes that are part of the melanocyte development process to be incorporated to the
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Deng, Wenping. "Algorithms for Reconstruction of Gene Regulatory Networks from High-Throughput Gene Expression Data." Thesis, Michigan Technological University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13420080.

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<p> Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with i
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Woodhouse, Steven. "Synthesising executable gene regulatory networks in haematopoiesis from single-cell gene expression data." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/269317.

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A fundamental challenge in biology is to understand the complex gene regulatory networks which control tissue development in the mammalian embryo, and maintain homoeostasis in the adult. The cell fate decisions underlying these processes are ultimately made at the level of individual cells. Recent experimental advances in biology allow researchers to obtain gene expression profiles at single-cell resolution over thousands of cells at once. These single-cell measurements provide snapshots of the states of the cells that make up a tissue, instead of the population-level averages provided by conv
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Fichtenholtz, Alexander Michael. "In silico bacterial gene regulatory network reconstruction from sequence." Thesis, Boston University, 2012. https://hdl.handle.net/2144/32880.

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Thesis (Ph.D.)--Boston University<br>PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you.<br>DNA sequencing techniques have evolved to the point where one can sequence millions of bases per minute, while our capacity to use this information has been left behind. One particularly notorious example is
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Naeem, Haroon. "Activity of microRNAs and transcription factors in Gene Regulatory Networks." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-141531.

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43

Zhu, Cong. "GENE REGULATORY NETWORKS OF AGL15 A PLANT MADS TRANSCRIPTION FACTOR." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_diss/446.

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Plant embryogenesis is an intriguing developmental process that is controlled by many genes. AGAMOUS Like 15 (AGL15) is a MADS-domain transcriptional regulator that accumulates preferentially during this stage. However, at the onset of this work it was unknown which genes are regulated by AGL15 or how AGL15 is regulated. This dissertation is part of the ongoing effort to understand the biological roles of AGL15. To decipher how AGL15 functions during plant development, a chromatin immunoprecipitation (ChIP) approach was adapted to obtain DNA fragments that are directly bound by AGL15 in vivo.
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44

Rhodes, Johanna. "Identifying gene regulatory networks common to multiple plant stress responses." Thesis, University of Warwick, 2012. http://wrap.warwick.ac.uk/56238/.

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Stress responses in plants can be defined as a change that affects the homeostasis of pathways, resulting in a phenotype that may or may not be visible to the human eye, affecting the fitness of the plant. Crosstalk is believed to be the shared components of pathways of networks, and is widespread in plants, as shown by examples of crosstalk between transcriptional regulation pathways, and hormone signalling. Crosstalk between stress responses is believed to exist, particularly crosstalk within the responses to biotic stress, and within the responses to abiotic stress. Certain hormone pathways
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Gerber, Georg Kurt 1970. "Computational discovery of gene modules, regulatory networks and expression programs." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42201.

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Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.<br>Includes bibliographical references (p. 163-181).<br>High-throughput molecular data are revolutionizing biology by providing massive amounts of information about gene expression and regulation. Such information is applicable both to furthering our understanding of fundamental biology and to developing new diagnostic and treatment approaches for diseases. However, novel mathematical methods are needed for extracting biological knowledge from high-dimensional, complex and noisy data sources. In this thesis, I devel
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MILES, RICHARD BRENT. "STRONGLY CONNECTED COMPONENTS AND STEADY STATES IN GENE REGULATORY NETWORKS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195439442.

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47

Slawek, Janusz. "Inferring Gene Regulatory Networks from Expression Data using Ensemble Methods." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3396.

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High-throughput technologies for measuring gene expression made inferring of the genome-wide Gene Regulatory Networks an active field of research. Reverse-engineering of systems of transcriptional regulations became an important challenge in molecular and computational biology. Because such systems model dependencies between genes, they are important in understanding of cell behavior, and can potentially turn observed expression data into the new biological knowledge and practical applications. In this dissertation we introduce a set of algorithms, which infer networks of transcriptional regul
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48

Kim, Haseong. "Stochastic modeling and inference of large-scale gene regulatory networks." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9966.

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Gene regulatory networks (GRNs) consist of thousands of genes and proteins which are dynamically interacting with each other. Researchers have investigated how to uncover these unknown interactions by observing expressions of biological molecules with various statistical/mathematical methods. Once these regulatory structures are revealed, it is necessary to understand their dynamical behaviors since pathway activities could be changed by their given conditions. Therefore, both the regulatory structure estimation and dynamics modeling of GRNs are essential for biological research. Generally, GR
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Naeem, Haroon [Verfasser], and Ralf [Akademischer Betreuer] Zimmer. "Activity of microRNAs and transcription factors in Gene Regulatory Networks : Activity of microRNAs and transcription factors in Gene Regulatory Networks / Haroon Naeem. Betreuer: Ralf Zimmer." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2012. http://d-nb.info/1021307653/34.

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50

Abul, Osman. "Controlling Discrete Genetic Regulatory Networks." Phd thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12605739/index.pdf.

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Genetic regulatory networks can model dynamics of cells. They also allow for studying the effect of internal or external interventions. Selectively applying interventions towards a certain objective is known as controlling network dynamics. In this thesis work, the issue of how the external interventions af fect the network is studied. The effects are determined using differential gene expression analysis. The differential gene expression problem is further studied to improve the power of the given method. Control problem for dynamic discrete regulatory networks is formulated. This also addres
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