Dissertations / Theses on the topic 'Protein and gene networks'
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Gunnarsson, Ida. "Deriving Protein Networks by Combining Gene Expression and Protein Chip Analysis." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-706.
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In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.
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Lehtinen, S. K. "Gene and protein networks in understanding cellular function." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1470874/.
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Over the past decades, networks have emerged as a useful way of representing complex large-scale systems in a variety of fields. In cellular and molecular biology, gene and protein networks have attracted considerable interest as tools for making sense of increasingly large volumes of data. Despite this interest, there is still substantial debate over how to best exploit network models in cellular biology. This thesis explores the use of gene and protein networks in various biological contexts. The first part of the thesis (Chapter 2) examines protein function prediction using network-based ‘guilt-by-association’ approaches. Given the falling costs of genome sequencing and the availability of large volumes of biological data, automated annotation of gene and protein function is becoming increasingly useful. Chapter 2 describes the development of a new network-based protein function prediction method and compares it to a leading algorithm on a number of benchmarks. Biases in benchmarking methods are also explicitly explored. The second part (Chapters 3 and 4) explores network approaches in understanding loss of function variation in the human genome. For a number of genes, homozygous loss of function appears to have no detrimental effect. A possible explanation is that these genes are only necessary in specific genetic backgrounds. Chapter 3 develops methods for identifying these types of relationships between apparently loss of function tolerant genes. Chapter 4 describes the use of networks in predicting the functional effects of loss of function mutations. The third part of the thesis (Chapters 5 and 6) uses network representations to model the effects of cellular stress on yeast cells. Chapter 5 examines stress induced changes in co-expression and protein interaction networks, finding evidence of increased modularisation in both types of network. Chapter 6 explores the effect of stress on resilience to node removal in the co-expression networks.
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Agarwal, Sumeet. "Networks in nature : dynamics, evolution, and modularity." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564283.
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In this thesis we propose some new approaches to the study of complex networks, and apply them to multiple domains, focusing in particular on protein-protein interaction networks. We begin by examining the roles of individual proteins; specifically, the influential idea of 'date' and 'party' hubs. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. We show that the observations underlying this proposal appear to have been largely illusory, and that topological properties of hubs do not in general correlate with interactor co-expression, thus undermining the primary basis for the categorisation. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins, indicating that it might be useful to conceive of roles for protein-protein interactions, as opposed to individual proteins. The observation that examining just one or a few network properties can be misleading motivates us to attempt to develop a more holistic methodology for network investigation. A wide variety of diagnostics of network structure exist, but studies typically employ only small, largely arbitrarily selected subsets of these. Here we simultaneously investigate many networks using many diagnostics in a data-driven fashion, and demonstrate how this approach serves to organise both networks and diagnostics, as well as to relate network structure to functionally relevant characteristics in a variety of settings. These include finding fast estimators for the solution of hard graph problems, discovering evolutionarily significant aspects of metabolic networks, detecting structural constraints on particular network types, and constructing summary statistics for efficient model-fitting to networks. We use the last mentioned to suggest that duplication-divergence is a feasible mechanism for protein-protein interaction evolution, and that interactions may rewire faster in yeast than in larger genomes like human and fruit fly. Our results help to illuminate protein-protein interaction networks in multiple ways, as well as providing some insight into structure-function relationships in other types of networks. We believe the methodology outlined here can serve as a general-purpose, data-driven approach to aid in the understanding of networked systems.
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Yalamanchili, Hari Krishna. "Computational approaches for protein functions and gene association networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206477.
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Entire molecular biology revolves primarily around proteins and genes (DNA and RNA). They collaborate with each other facilitating various biomolecular systems. Thus, to comprehend any biological phenomenon from very basic cell division to most complex cancer, it is fundamental to decode the functional dynamics of proteins and genes. Recently, computational approaches are being widely used to supplement traditional experimental approaches. However, each automated approach has its own advantages and limitations. In this thesis, major shortcomings of existing computational approaches are identified and alternative fast yet precise methods are proposed.
First, a strong need for reliable automated protein function prediction is identified. Almost half of protein functional interpretations are enigmatic. Lack of universal functional vocabulary further elevates the problem. NRProF, a novel neural response based method is proposed for protein functional annotation. Neural response algorithm simulates human brain in classifying images; the same is applied here for classifying proteins. Considering Gene Ontology (GO) hierarchical structure as background, NRProF classifies a protein of interest to a specific GO category and thus assigns the corresponding function.
Having established reliable protein functional annotations, protein and gene collaborations are studied next. Interactions amongst transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental for gene regulation and are highly specific, even in evolution background. To explain this binding specificity a Co-Evo (co-evolutionary) relationship is hypothesized. Pearson correlation and Mutual Information (MI) metrics are used to validate the hypothesis. Residue level MI is used to infer specific binding residues of TFs and corresponding TFBSs, assisting a thorough understanding of gene regulatory mechanism and aid targeted gene therapies.
After comprehending TF and TFBS associations, interplay between genes is abstracted as Gene Regulatory Networks. Several methods using expression correlations are proposed to infer gene networks. However, most of them ignore the embedded dynamic delay induced by complex molecular interactions and other riotous cellular mechanisms, involved in gene regulation. The delay is rather obvious in high frequency time series expression data. DDGni, a novel network inference strategy is proposed by adopting gapped smith-waterman algorithm. Gaps attune expression delays and local alignment unveils short regulatory windows, which traditional methods overlook.
In addition to gene level expression data, recent studies demonstrated the merits of exon-level RNA-Seq data in profiling splice variants and constructing gene networks. However, the large number of exons versus small sample size limits their practical application. SpliceNet, a novel method based on Large Dimensional Trace is proposed to infer isoform specific co-expression networks from exon-level RNA-Seq data. It provides a more comprehensive picture to our understanding of complex diseases by inferring network rewiring between normal and diseased samples at isoform resolution. It can be applied to any exon level RNA-Seq data and exon array data.
In summary, this thesis first identifies major shortcomings of existing computational approaches to functional association of proteins and genes, and develops several tools viz. NRProF, Co-Evo, DDGni and SpliceNet. Collectively, they offer a comprehensive picture of the biomolecular system under study.published_or_final_version
Biochemistry
Doctoral
Doctor of Philosophy
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Shelton, Rebecca Kay. "Parameter Identifiability and Estimation in Gene and Protein Interaction Networks." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32702.
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The collection of biological data has been limited by instrumentation, the complexity of the systems themselves, and even the ability of graduate students to stay awake and record the data. However, increasing measurement capabilities and decreasing costs may soon enable the collection of reasonably sampled time course data characterizing biological systems, though in general only a subset of the systemâ s species would be measured. This increase in data volume requires a corresponding increase in the use and interpretation of such data, specifically in the development of system identification techniques to identify parameter sets in proposed models.
In this paper, we present the results of identifiability analysis on a small test system, including the identifiability of parameters with respect to different measurements (proteins and mRNA), and propose a working definition for â biologically meaningful estimationâ . We also analyze the correlations between parameters, and use this analysis to consider effective approaches to determining parameters with biological meaning. In addition, we look at other methods for determining relationships between parameters and their possible significance. Finally, we present potential biologically meaningful parameter groupings from the test system and present the results of our attempt to estimate the value of select groupings.Master of Science
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King, James Lowell. "Gene Ontology-Guided Force-Directed Visualization of Protein Interaction Networks." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1066.
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Protein interaction data is being generated at unprecedented rates thanks to advancements made in high throughput techniques such as mass spectrometry and DNA microarrays. Biomedical researchers, operating under budgetary constraints, have found it difficult to scale their efforts to keep up with the ever-increasing amount of available data. They often lack the resources and manpower required to analyze the data using existing methodologies. These research deficiencies impede our ability to understand diseases, delay the advancement of clinical therapeutics, and ultimately costs lives.
One of the most commonly used techniques to analyze protein interaction data is the construction and visualization of protein interaction networks. This research investigated the effectiveness and efficiency of novel domain-specific algorithms for visualizing protein interaction networks. The existing domain-agnostic algorithms were compared to the novel algorithms using several performance, aesthetic, and biological relevance metrics. The graph drawing algorithms proposed here introduced novel domain-specific forces to the existing force-directed graph drawing algorithms. The innovations include an attractive force and graph coarsening policy based on semantic similarity, and a novel graph refinement algorithm.
These experiments have demonstrated that the novel graph drawing algorithms consistently produce more biologically meaningful layouts than the existing methods. Aggregated over the 480 tests performed, and quantified using the Biological Evaluation Percentage metric defined in the Methodology chapter, the novel graph drawing algorithms created layouts that are 237 percent more biologically meaningful than the next best algorithm. This improvement came at the cost of additional edge crossings and smaller minimum angles between adjacent edges, both of which are undesirable aesthetics. The aesthetic and performance tradeoffs are experimentally quantified in this study, and dozens of algorithmically generated graph drawings are presented to visually illustrate the benefits of the novel algorithms. The graph drawing algorithms proposed in this study will help biomedical researchers to more efficiently produce high quality interactive protein interaction network drawings for improved discovery and communication.
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Yasar, Sevgi. "Multi-resolution Visualization Of Large Scale Protein Networks Enriched With Gene Ontology Annotations." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611132/index.pdf.
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Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other.
Recent increase in protein interaction prediction techniques have made great amount of protein-protein interaction data available for genomes. As a consequence, a systematic visualization and analysis technique has become crucial.
To the best of our knowledge, no PPI visualization tool consider multi-resolution viewing of PPI network. In this thesis, we implemented a new approach for PPI network visualization
which supports multi-resolution viewing of compound graphs. We construct compound nodes and label them by using gene set enrichment methods based on Gene Ontology annotations.
This thesis further suggests new methods for PPI network visualization.
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Zhu, Shaoming. "Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41693/1/Shaoming_Zhu_Thesis.pdf.
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Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.
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Brettner, Leandra M., and Joanna Masel. "Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast." BioMed Central, 2012. http://hdl.handle.net/10150/610103.
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BACKGROUND:A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.RESULTS:We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.CONCLUSIONS:Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.
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Aragüés, Peleato Ramón. "Protein Interaction networks and their applications to protein characterization and cancer genes prediction." Doctoral thesis, Universitat Pompeu Fabra, 2007. http://hdl.handle.net/10803/7148.
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La importancia de comprender los procesos biológicos ha estimulado el desarrollo de métodos para la detección de interacciones proteína-proteína. Esta tesis presenta PIANA (Protein Interactions And Network Analysis), un programa informático para la integración y el análisis de redes de interacción proteicas. Además, describimos un método que identifica motivos de interacción basándose en que las proteínas con parejas de interacción comunes tienden a interaccionar con esas parejas a través del mismo motivo de interacción. Encontramos que las proteínas altamente conectadas (i.e., hubs) con múltiples motivos tienen mayor probabilidad de ser esenciales para la viabilidad de la célula que los hubs con uno o dos motivos. Finalmente, presentamos un método que predice genes relacionados con cáncer mediante la integración de redes de interacción proteicas, datos de expresión diferenciada y propiedades estructurales, funcionales y evolutivas. El valor de predicción positiva es 71% con sensitividad del 1%, superando a otros métodos usados independientemente.The importance of understanding cellular processes prompted the development of experimental approaches that detect protein-protein interactions. Here, we describe a software platform called PIANA (Protein Interactions And Network Analysis) that integrates interaction data from multiple sources and automates the analysis of protein interaction networks. Moreover, we describe a method that delineates interacting motifs by relying on the observation that proteins with common interaction partners tend to interact with these partners through the same interacting motif. We find that highly connected proteins (i.e., hubs) with multiple interacting motifs are more likely to be essential for cellular viability than hubs with one or two interacting motifs. Furthermore, we present a method that predicts cancer genes by integrating protein interaction networks, differential expression studies and structural, functional and evolutionary properties. For a sensitivity of 1%, the positive predictive value is 71%, which outperforms the use of any of the methods independently.
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Jaeger, Samira. "Network-based inference of protein function and disease-gene association." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16623.
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Proteininteraktionen sind entscheidend für zelluläre Funktion. Interaktionen reflektieren direkte funktionale Beziehungen zwischen Proteinen. Veränderungen in spezifischen Interaktionsmustern tragen zur Entstehung von Krankheiten bei. In dieser Arbeit werden funktionale und pathologische Aspekte von Proteininteraktionen analysiert, um Funktionen für bisher nicht charakterisierte Proteine vorherzusagen und Proteine mit Krankheitsphänotypen zu assoziieren. Verschiedene Methoden wurden in den letzten Jahren entwickelt, die die funktionalen Eigenschaften von Proteinen untersuchen. Dennoch bleibt ein wesentlicher Teil der Proteine, insbesondere menschliche, uncharakterisiert. Wir haben eine Methode zur Vorhersage von Proteinfunktionen entwickelt, die auf Proteininteraktionsnetzwerken verschiedener Spezies beruht. Dieser Ansatz analysiert funktionale Module, die über evolutionär konservierte Prozesse definiert werden. In diesen Modulen werden Proteinfunktionen gemeinsam über Orthologiebeziehungen und Interaktionspartner vorhergesagt. Die Integration verschiedener funktionaler Ähnlichkeiten ermöglicht die Vorhersage neuer Proteinfunktionen mit hoher Genauigkeit und Abdeckung. Die Aufklärung von Krankheitsmechanismen ist wichtig, um ihre Entstehung zu verstehen und diagnostische und therapeutische Ansätze zu entwickeln. Wir stellen einen Ansatz für die Identifizierung krankheitsrelevanter Genprodukte vor, der auf der Kombination von Proteininteraktionen, Proteinfunktionen und Netzwerkzentralitätsanalyse basiert. Gegeben einer Krankheit, werden krankheitsspezifische Netzwerke durch die Integration von direkt und indirekt interagierender Genprodukte und funktionalen Informationen generiert. Proteine in diesen Netzwerken werden anhand ihrer Zentralität sortiert. Das Einbeziehen indirekter Interaktionen verbessert die Identifizierung von Krankheitsgenen deutlich. Die Verwendung von vorhergesagten Proteinfunktionen verbessert das Ranking von krankheitsrelevanten Proteinen.Protein interactions are essential to many aspects of cellular function. On the one hand, they reflect direct functional relationships. On the other hand, alterations in protein interactions perturb natural cellular processes and contribute to diseases. In this thesis we analyze both the functional and the pathological aspect of protein interactions to infer novel protein function for uncharacterized proteins and to associate yet uncharacterized proteins with disease phenotypes, respectively. Different experimental and computational approaches have been developed in the past to investigate the basic characteristics of proteins systematically. Yet, a substantial fraction of proteins remains uncharacterized, particularly in human. We present a novel approach to predict protein function from protein interaction networks of multiple species. The key to our method is to study proteins within modules defined by evolutionary conserved processes, combining comparative cross-species genomics with functional linkage in interaction networks. We show that integrating different evidence of functional similarity allows to infer novel functions with high precision and a very good coverage. Elucidating the pathological mechanisms is important for understanding the onset of diseases and for developing diagnostic and therapeutic approaches. We introduce a network-based framework for identifying disease-related gene products by combining protein interaction data and protein function with network centrality analysis. Given a disease, we compile a disease-specific network by integrating directly and indirectly linked gene products using protein interaction and functional information. Proteins in this network are ranked based on their network centrality. We demonstrate that using indirect interactions significantly improves disease gene identification. Predicted functions, in turn, enhance the ranking of disease-relevant proteins.
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Toufighi, Kiana 1980. "Integrative study of gene expression and protein complexes." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/380907.
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Over the last several decades, the emerging ‘integrated’ view of the cell has triumphed over
the ‘one gene/one protein/one function’ paradigm. This is illustrated by the biologically
opposite effects of key regulatory proteins in different cell types, in established versus
primary cells, and in vitro versus in vivo situations. The persistent theme throughout this
dissertation is the integration of a wide range of data sources for the purpose of
understanding distinct cellular contexts. We first use circadian expression data from human
epidermal stem cells to discover waves of transcripts expressed in tune with known clock
genes and show that time-of-day dependent responses to proliferation/differentiation cues is
important for skin homeostasis. We then combine this expression data with information on
protein structures and complexes to describe how protein-complex assembly is temporally
regulated during differentiation. Lastly, we show that human protein complexes are
composed of a stable ‘core’ and a plastic ‘periphery’ whose tissue-specific expression
allows protein complexes to function in a context-dependent manner.En las últimas décadas, la emergente vista integrativa de la célula ha triunfado sobre el paradigma histórico: ‘un gene/una proteína/una función’. Esto es ilustrado por los efectos biológicos opuestos de proteínas regulatorias clave en cultivos celulares inmortalizados frente a primarios e in vitro frente a in vivo. El tema persistente en este disertación es la integración de un amplio set de datos para estudiar los distintos contextos celulares. En primer lugar, utilizamos los datos de expresión génica obtenidos de células madre epidérmicas para descubrir las ondas de transcripción expresadas en sintonía con los genes conocidos de los ritmos circadianos. En este estudio demostramos que las respuestas de las células madres a las señales de proliferación/diferenciación dependen de hora del día y el tiempo circadiano es importante para la homeostasis de la piel. Posteriormente, combinamos estos datos de expresión con la información estructural de proteínas y complejos proteicos para describir la regulación temporal de complejos durante el proceso de diferenciación. Por último, mostramos que los complejos de proteínas humanos están compuestos de un ‘núcleo’ estable y una 'periferia' plástica cuya expresión específica de tejido celular permite que los complejos de proteínas funcionen de una manera dependiente del contexto.
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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 metazoan development, whereas the organization of GRNs that pertain to systems physiology remains mostly unexplored.
My work has focused on delineating the first gene regulatory network of the nematode Caenorhabditis elegans metabolic genes, and investigating how this network relates to the energy homeostasis of the nematode. The resulting metabolic GRN consists of ~70 metabolic genes, 100 TFs and more than 500 protein–DNA interactions. It also includes novel protein-protein interactions involving the metabolic transcriptional cofactor MDT-15 and several TFs that occur in the metabolic GRN. On a global level, we found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs). NHRs form a special class of TFs that can interact with diffusible biomolecules and are well-known regulators of lipid metabolism in other organisms, including humans. Interestingly, NHRs comprise the largest family of TFs in nematodes; the C. elegans genome encodes 284 NHRs, most of which are uncharacterized. In our study, we show that the C. elegans NHRs that we retrieved in the metabolic GRN organize into network modules, and that most of these NHRs function to maintain lipid homeostasis in the nematode. Network modularity has been proposed to facilitate rapid and robust changes in gene expression. Our results suggest that the C. elegans metabolic GRN may have evolved by combining NHR family expansion with the specific modular wiring of NHRs to enable the rapid adaptation of the animal to different environmental cues.
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Li, Ai. "Generalizations of the topological overlap measure for neighborhood analysis and module detection in gene and protein networks." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1481673641&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
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Hlatshwayo, Nkosikhona Rejoyce. "Comparison of protein binding microarray derived and ChIP-seq derived transcription factor binding DNA motifs." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1017907.
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Transcription factors (TFs) are biologically important proteins that interact with transcription machinery and bind DNA regulatory sequences to regulate gene expression by modulating the synthesis of the messenger RNA. The regulatory sequences comprise of short conserved regions of a specific length called motifs . TFs have very diverse roles in different cells and play a very significant role in development. TFs have been associated with carcinogenesis in various tissue types, as well as developmental and hormone response disorders. They may be responsible for the regulation of oncogenes and can be oncogenic. Consequently, understanding TF binding and knowing the motifs to which they bind is worthy of attention and research focus. Various projects have made the study of TF binding their main focus; nevertheless, much about TF binding remains confounding. Chromatin immunoprecipitation in conjunction with deep sequencing (ChIP-seq) techniques are a popular method used to investigate DNA-TF interactions in vivo. This procedure is followed by motif discovery and motif enrichment analysis using relevant tools. Protein Binding Microarrays (PBMs) are an in vitro method for investigating DNA-TF interactions. We use a motif enrichment analysis tools (CentriMo and AME) and an empirical quality assessment tool (Area under the ROC curve) to investigate which method yields motifs that are a true representation of in vivo binding. Motif enrichment analysis: On average, ChIP-seq derived motifs from the JASPAR Core database outperformed PBM derived ones from the UniPROBE mouse database. However, the performance of motifs derived using these two methods is not much different from each other when using CentriMo and AME. The E-values from Motif enrichment analysis were not too different from each other or 0. CentriMo showed that in 35 cases JASPAR Core ChIP-seq derived motifs outperformed UniPROBE mouse PBM derived motifs, while it was only in 11 cases that PBM derived motifs outperformed ChIP-seq derived motifs. AME showed that in 18 cases JASPAR Core ChIP-seq derived motifs did better, while only it was only in 3 cases that UniPROBE motifs outperformed ChIP-seq derived motifs. We could not distinguish the performance in 25 cases. Empirical quality assessment: Area under the ROC curve values computations followed by a two-sided t-test showed that there is no significant difference in the average performances of the motifs from the two databases (with 95% confidence, mean of differences=0.0088125 p-value= 0.4874, DF=47) .
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Kataka, Evans [Verfasser], Dmitrij [Akademischer Betreuer] Frishman, Dmitrij [Gutachter] Frishman, and Jürgen [Gutachter] Cox. "Tissue-specific gene (and protein) expression and its effects on protein-protein interaction networks in cancer and other complex diseases. / Evans Kataka ; Gutachter: Dmitrij Frishman, Jürgen Cox ; Betreuer: Dmitrij Frishman." München : Universitätsbibliothek der TU München, 2020. http://d-nb.info/1222161702/34.
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Ogris, Christoph. "Global functional association network inference and crosstalk analysis for pathway annotation." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-146703.
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Cell functions are steered by complex interactions of gene products, like forming a temporary or stable complex, altering gene expression or catalyzing a reaction. Mapping these interactions is the key in understanding biological processes and therefore is the focus of numerous experiments and studies. Small-scale experiments deliver high quality data but lack coverage whereas high-throughput techniques cover thousands of interactions but can be error-prone. Unfortunately all of these approaches can only focus on one type of interaction at the time. This makes experimental mapping of the genome-wide network a cost and time intensive procedure. However, to overcome these problems, different computational approaches have been suggested that integrate multiple data sets and/or different evidence types. This widens the stringent definition of an interaction and introduces a more general term - functional association. FunCoup is a database for genome-wide functional association networks of Homo sapiens and 16 model organisms. FunCoup distinguishes between five different functional associations: co-membership in a protein complex, physical interaction, participation in the same signaling cascade, participation in the same metabolic process and for prokaryotic species, co-occurrence in the same operon. For each class, FunCoup applies naive Bayesian integration of ten different evidence types of data, to predict novel interactions. It further uses orthologs to transfer interaction evidence between species. This considerably increases coverage, and allows inference of comprehensive networks even for not well studied organisms. BinoX is a novel method for pathway analysis and determining the relation between gene sets, using functional association networks. Traditionally, pathway annotation has been done using gene overlap only, but these methods only get a small part of the whole picture. Placing the gene sets in context of a network provides additional evidence for pathway analysis, revealing a global picture based on the whole genome. PathwAX is a web server based on the BinoX algorithm. A user can input a gene set and get online network crosstalk based pathway annotation. PathwAX uses the FunCoup networks and 280 pre-defined pathways. Most runs take just a few seconds and the results are summarized in an interactive chart the user can manipulate to gain further insights of the gene set's pathway associations.At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.
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Daw, Elbait Gihan Elsir Ahmed. "From cancer gene expression to protein interaction: Interaction prediction, network reasoning and applications in pancreatic cancer." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-19908.
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Microarray technologies enable scientists to identify co-expressed genes at large scale. However, the gene expression analysis does not show functional relationships between co-expressed genes. There is a demand for effective approaches to analyse gene expression data to enable biological discoveries that can lead to identification of markers or therapeutic targets of many diseases.
In cancer research, a number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue such as Pancreatic Ductal Adenocarcinoma (PDAC). PDAC carries very poor prognosis, it eludes early detection and is characterised by its aggressiveness and resistance to currently available therapies. To identify molecular markers and suitable targets, there exist a research effort that maps differentially expressed genes to protein interactions to gain an understanding at systems level. Such interaction networks have a complex interconnected structure, whose the understanding of which is not a trivial task.
Several formal approaches use simulation to support the investigation of such networks. These approaches suffer from the missing knowledge concerning biological systems. Reasoning in the other hand has the advantage of dealing with incomplete and partial information of the network knowledge.
The initial approach adopted was to provide an algorithm that utilises a network-centric approach to pancreatic cancer, by re-constructing networks from known interactions and predicting novel protein interactions from structural templates. This method was applied to a data set of co-expressed PDAC genes. To this end, structural domains for the gene products are identified by using threading which is a 3D structure prediction technique. Next, the Protein Structure Interaction Database (SCOPPI), a database that classifies and annotates domain interactions derived from all known protein structures, is used to find templates of structurally interacting domains. Moreover, a network of related biological pathways for the PDAC data was constructed.
In order to reason over molecular networks that are affected by dysregulation of gene expression, BioRevise was implemented. It is a belief revision system where the inhibition behaviour of reactions is modelled using extended logic programming. The system computes a minimal set of enzymes whose malfunction explains the abnormal expression levels of observed metabolites or enzymes.
As a result of this research, two complementary approaches for the analysis of pancreatic cancer gene expression data are presented. Using the first approach, the pathways found to be largely affected in pancreatic cancer are signal transduction, actin cytoskeleton regulation, cell growth and cell communication. The analysis indicates that the alteration of the calcium pathway plays an important role in pancreas specific tumorigenesis. Furthermore, the structural prediction method reveals ~ 700 potential protein-protein interactions from the PDAC microarray data, among them, 81 novel interactions such as: serine/threonine kinase CDC2L1 interacting with cyclin-dependent kinase inhibitor CDKN3 and the tissue factor pathway inhibitor
2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). These resulting genes were further investigated and some were found to be potential therapeutic markers for PDAC. Since TMPRSS4 is involved in metastasis formation, it is hypothesised that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. The predicted protein-protein network inspired the analysis of the data from two other perspectives. The resulting protein-protein interaction network highlighted the importance of the co-expression of KLK6 and KLK10 as prognostic factors for survival in PDAC
as well as the construction of a PDAC specific apoptosis pathway to study different effects of multiple gene silencing in order to reactivate apoptosis in PDAC.
Using the second approach, the behaviour of biological interaction networks using computational logic formalism was modelled, reasoning over the networks is enabled and the abnormal behaviour of its components is explained. The usability of the BioRevise system is demonstrated through two examples, a metabolic disorder disease and a deficiency in a pancreatic
cancer associated pathway. The system successfully identified the inhibition of the enzyme glucose-6-phosphatase as responsible for the Glycogen storage disease type I, which according to literature is known to be the main reason for this disease. Furthermore, BioRevise was used to model reaction inhibition in the Glycolysis pathway which is known to be affected by Pancreatic cancer.
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Banduseela, Varuna Chaminda. "Molecular And Cellular Networks in Critical Illness Associated Muscle Weakness : Skeletal Muscle Proteostasis in the Intensive Care Unit." Doctoral thesis, Uppsala universitet, Institutionen för neurovetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-183959.
Full textAbstract:
Critical illness associated muscle weakness and muscle dysfunction in intensive care unit (ICU) patients lead to severe morbidity and mortality as well as significant adverse effect on quality of life. Immobilization, mechanical ventilation, neuromuscular blocking agents, corticosteroids, and sepsis have been implicated as important risk factors, but the underlying molecular and cellular mechanisms remain unclear. A unique porcine ICU model was employed to investigate the effect of these risk factors on the expression profiles, gene expression and contractile properties of limb and diaphragm muscle, in the early phase of ICU stay. This project has focused on unraveling the underlying molecular and cellular pathways or networks in response to ICU and critical illness interventions. Upregulation of heat shock proteins indicated to play a protective role despite number of differentially transcribed gene groups that would otherwise have a negative effect on muscle fiber structure and function in response to immobilization and mechanical ventilation. Mechanical ventilation appears to play a critical role in development of diaphragmatic dysfunction. Impaired autophagy, chaperone expression and protein synthesis are indicated to play a pivotal role in exacerbating muscle weakness in response to the combined effect of risk factors in ICU. These results may be of therapeutic importance in alleviating critical illness associated muscle weakness.
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Kumar, Vivek. "Computational Prediction of Protein-Protein Interactions on the Proteomic Scale Using Bayesian Ensemble of Multiple Feature Databases." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1322489637.
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Lintner, Robert E. "Comparative Functional Analysis and Identification of Regulatory Control in Gene Networks Using the Leucine-Responsive Regulatory protein and its Regulon as a Model System." University of Toledo Health Science Campus / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=mco1178738358.
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Zagore, Leah Louise. "The Molecular Function of the RNA Binding Protein DAZL in Male Germ Cell Survival." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1575647143675768.
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Andrews, Tallulah. "Clustering genes by function to understand disease phenotypes." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:06bfce1f-4ae0-4715-9ee3-290c43ae9b18.
Full textAbstract:
Developmental disorders including: autism, intellectual disability, and congenital abnormalities are present in 3-8% of live births and display a huge amount of phenotypic and genetic heterogeneity. Traditionally, geneticists have identified individual monogenic diseases among these patients but a majority of patients fail to receive a clinical diagnosis. However, the genomes of these patients frequently harbour large copynumber variants (CNVs) but their interpretation remains challenging. Using pathway analysis I found significant functional associations for 329 individual phenotypes and show that 39% of these could explain the patientsâ multiple co-morbid phenotypes; and multiple associated genes clustered within individual CNVs. I showed there was significantly more such clustering than expected by chance. In addition, the presence of a multiple functionally-related genes is a significant predictor of CNV pathogenicity beyond the presence of known disease genes and size of the CNV. This clustering of functionally-related genes was part of a broader pattern of functional clusters across the human genome. These genome-wide functional clusters showed tissuespecific expression and some evidence of chromatin-domain level regulation. Furthermore, many genome-wide functional clusters were enriched in segmental duplications making them prone to CNV-causing mutations and were frequently seen disrupted in healthy individuals. However, the majority of the time a pathogenic CNV affected the entire functional cluster, where as benign CNVs tended to affect only one or two genes. I also showed that patients with CNVs affecting the same functional cluster are significantly more phenotypically similar to each other than expected even if their CNVs do not affect any of the same genes. Lastly, I considered one of the major limitations in pathway analysis, namely ascertainment biases in functional information due to the prioritization of genes linked to human disease, and show how the modular nature of gene-networks can be used to identify and prioritize understudied genes.
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Zhou, Yadi. "The Subcellular Localization and Protein-protein Interactions of Barley Mixed-Linkage-(1->3),(1->4)-ß-D-Glucan Synthase CSLF6 and CSLH1." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1541445059683651.
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duVerle, David Alexander. "Building a Machine-Learning Framework for Protein Interactions: Calpain Cleavage Prediction and Gene Regulatory Network Inference." 京都大学 (Kyoto University), 2012. http://hdl.handle.net/2433/157921.
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Wang, Danling. "Multifractal characterisation and analysis of complex networks." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48176/1/Danling_Wang_Thesis.pdf.
Full textAbstract:
Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.
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Wang, Yanfei. "Fuzzy methods for analysis of microarrays and networks." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48175/1/Yanfei_Wang_Thesis.pdf.
Full textAbstract:
Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.
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Chen, Jing. "Computational Selection and Prioritization of Disease Candidate Genes." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1211228557.
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Brown, Serena Jean Silver. "Dual functions of the retinal determination gene network member EYES ABSENT as a transcription factor and protein phosphatase." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33454.
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Scofield, Michael D. "Elucidating the Transcriptional Network Underlying Expression of a Neuronal Nicotinic Receptor Gene: A Dissertation." eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/497.
Full textAbstract:
Neuronal nicotinic acetylcholine receptors (nAChRs) are involved in a plethora of fundamental biological processes ranging from muscle contraction to the formation of memories. The studies described in this work focus on the transcriptional regulation of the CHRNB4 gene, which encodes the ß4 subunit of neuronal nAChRs. We previously identified a regulatory sequence (5´– CCACCCCT –3´), or “CA box”, critical for CHRNB4 promoter activity in vitro. Here I report transcription factor interaction at the CA box along with an in vivo analysis of CA box transcriptional activity. My data indicate that Sp1, Sp3, Sox10 and c-Jun interact with the CHRNB4 CA box in the context of native chromatin. Using an in vivo transgenic approach in mice, I demonstrated that a 2.3-kb fragment of the CHRNB4 promoter region, containing the CA box, is capable of directing cell-type specific expression of a reporter gene to many of the brain regions that endogenously express the CHRNB4 gene. Site-directed mutagenesis was used to test the hypothesis that the CA box is critical for CHRNB4 promoter activity in vivo. Transgenic animals were generated in which LacZ expression is driven by a mutant form of the CA box. Reporter gene expression was not detected in any tissue or cell type at ED18.5. Similarly, I observed dramatically reduced reporter gene expression at PD30 when compared to wild type transgenic animals, indicating that the CA box is an important regulatory feature of the CHRNB4 promoter. ChIP analysis of brain tissue from mutant transgenic animals demonstrated that CA box mutation results in decreased interaction of the transcription factor Sp1 with the CHRNB4 promoter. I have also investigated transcription factor interaction at the CHRNB4 promoter CT box, (5´– ACCCTCCCCTCCCCTGTAA –3´) and demonstrated that hnRNP K interacts with the CHRNB4 promoter in an olfactory bulb derived cell line. Surprisingly, siRNA experiments demonstrated that hnRNP K knockdown has no impact on CHRNA5, CHRNA3 or CHRNB4 gene expression. Interestingly, knockdown of the transcription factor Purα results in significant decreases in CHRNA5, CHRNA3 and CHRNB4 mRNA levels. These data indicate that Purα can act to enhance expression of the clustered CHRNA5, CHRNA3 and CHRNB4 genes. Together, these results contribute to a more thorough understanding of the transcriptional regulatory mechanisms underlying expression of the CHRNB4 as well as the CHRNA5 and CHRNA3 genes, critical components of cholinergic signal transduction pathways in the nervous system.
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Kim, Wooyoung. "Innovative Algorithms and Evaluation Methods for Biological Motif Finding." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/63.
Full textAbstract:
Biological motifs are defined as overly recurring sub-patterns in biological systems. Sequence motifs and network motifs are the examples of biological motifs. Due to the wide range of applications, many algorithms and computational tools have been developed for efficient search for biological motifs. Therefore, there are more computationally derived motifs than experimentally validated motifs, and how to validate the biological significance of the ‘candidate motifs’ becomes an important question. Some of sequence motifs are verified by their structural similarities or their functional roles in DNA or protein sequences, and stored in databases. However, biological role of
network motifs is still invalidated and currently no databases exist for this purpose.
In this thesis, we focus not only on the computational efficiency but also on the biological meanings of the motifs. We provide an efficient way to incorporate biological information with clustering analysis methods: For example, a sparse nonnegative matrix factorization (SNMF) method is used with Chou-Fasman parameters for the protein motif finding. Biological network motifs are searched by various clustering algorithms with Gene ontology (GO) information. Experimental results show that the algorithms perform better than existing algorithms by producing a larger number of high-quality of biological motifs.
In addition, we apply biological network motifs for the discovery of essential proteins. Essential proteins are defined as a minimum set of proteins which are vital for development to a fertile adult and in a cellular life in an organism. We design a new centrality algorithm with biological network motifs, named MCGO, and score proteins in a protein-protein interaction (PPI) network to find essential proteins. MCGO is also combined with other centrality measures to predict essential proteins using machine learning techniques.
We have three contributions to the study of biological motifs through this thesis; 1) Clustering analysis is efficiently used in this work and biological information is easily integrated with the analysis; 2) We focus more on the biological meanings of motifs by adding biological knowledge in the algorithms and by suggesting biologically related evaluation methods. 3) Biological network motifs are successfully applied to a practical application of prediction of essential proteins.
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Jaeger, Samira [Verfasser], Ulf [Akademischer Betreuer] Leser, Miguel [Akademischer Betreuer] Andrade-Navarro, and Oliver [Akademischer Betreuer] Kohlbacher. "Network-based inference of protein function and disease-gene association / Samira Jaeger. Gutachter: Ulf Leser ; Miguel Andrade-Navarro ; Oliver Kohlbacher." Berlin : Humboldt Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://d-nb.info/1028566875/34.
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Scofield, Michael D. "Elucidating the Transcriptional Network Underlying Expression of a Neuronal Nicotinic Receptor Gene: A Dissertation." eScholarship@UMMS, 2009. http://escholarship.umassmed.edu/gsbs_diss/497.
Full textAbstract:
Neuronal nicotinic acetylcholine receptors (nAChRs) are involved in a plethora of fundamental biological processes ranging from muscle contraction to the formation of memories. The studies described in this work focus on the transcriptional regulation of the CHRNB4 gene, which encodes the ß4 subunit of neuronal nAChRs. We previously identified a regulatory sequence (5´– CCACCCCT –3´), or “CA box”, critical for CHRNB4 promoter activity in vitro. Here I report transcription factor interaction at the CA box along with an in vivo analysis of CA box transcriptional activity. My data indicate that Sp1, Sp3, Sox10 and c-Jun interact with the CHRNB4 CA box in the context of native chromatin. Using an in vivo transgenic approach in mice, I demonstrated that a 2.3-kb fragment of the CHRNB4 promoter region, containing the CA box, is capable of directing cell-type specific expression of a reporter gene to many of the brain regions that endogenously express the CHRNB4 gene. Site-directed mutagenesis was used to test the hypothesis that the CA box is critical for CHRNB4 promoter activity in vivo. Transgenic animals were generated in which LacZ expression is driven by a mutant form of the CA box. Reporter gene expression was not detected in any tissue or cell type at ED18.5. Similarly, I observed dramatically reduced reporter gene expression at PD30 when compared to wild type transgenic animals, indicating that the CA box is an important regulatory feature of the CHRNB4 promoter. ChIP analysis of brain tissue from mutant transgenic animals demonstrated that CA box mutation results in decreased interaction of the transcription factor Sp1 with the CHRNB4 promoter. I have also investigated transcription factor interaction at the CHRNB4 promoter CT box, (5´– ACCCTCCCCTCCCCTGTAA –3´) and demonstrated that hnRNP K interacts with the CHRNB4 promoter in an olfactory bulb derived cell line. Surprisingly, siRNA experiments demonstrated that hnRNP K knockdown has no impact on CHRNA5, CHRNA3 or CHRNB4 gene expression. Interestingly, knockdown of the transcription factor Purα results in significant decreases in CHRNA5, CHRNA3 and CHRNB4 mRNA levels. These data indicate that Purα can act to enhance expression of the clustered CHRNA5, CHRNA3 and CHRNB4 genes. Together, these results contribute to a more thorough understanding of the transcriptional regulatory mechanisms underlying expression of the CHRNB4 as well as the CHRNA5 and CHRNA3 genes, critical components of cholinergic signal transduction pathways in the nervous system.
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Schmid, Ramona [Verfasser], and Roland [Akademischer Betreuer] Eils. "Analyzing Compounds’ Mode of Action - A Use Case for New Approaches Utilizing Protein Interaction Networks and Prior Knowledge to Complement State-of-the-Art Gene Expression Analyses / Ramona Schmid ; Betreuer: Roland Eils." Heidelberg : Universitätsbibliothek Heidelberg, 2012. http://d-nb.info/1179786343/34.
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Simões, Sérgio Nery. "Uma abordagem de integração de dados de redes PPI e expressão gênica para priorizar genes relacionados a doenças complexas." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-17112015-172846/.
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Doenças complexas são caracterizadas por serem poligênicas e multifatoriais, o que representa um desafio em relação à busca de genes relacionados a elas. Com o advento das tecnologias de sequenciamento em larga escala do genoma e das medições de expressão gênica (transcritoma), bem como o conhecimento de interações proteína-proteína, doenças complexas têm sido sistematicamente investigadas. Particularmente, baseando-se no paradigma Network Medicine, as redes de interação proteína-proteína (PPI -- Protein-Protein Interaction) têm sido utilizadas para priorizar genes relacionados às doenças complexas segundo suas características topológicas. Entretanto, as redes PPI são afetadas pelo viés da literatura, em que as proteínas mais estudadas tendem a ter mais conexões, degradando a qualidade dos resultados. Adicionalmente, métodos que utilizam somente redes PPI fornecem apenas resultados estáticos e não-específicos, uma vez que as topologias destas redes não são específicas de uma determinada doença. Neste trabalho, desenvolvemos uma metodologia para priorizar genes e vias biológicas relacionados à uma dada doença complexa, através de uma abordagem integrativa de dados de redes PPI, transcritômica e genômica, visando aumentar a replicabilidade dos diferentes estudos e a descoberta de novos genes associados à doença. Após a integração das redes PPI com dados de expressão gênica, aplicamos as hipóteses da Network Medicine à rede resultante para conectar genes sementes (relacionados à doença, definidos a partir de estudos de associação) através de caminhos mínimos que possuam maior co-expressão entre seus genes. Dados de expressão em duas condições (controle e doença) são usados separadamente para obter duas redes, em que cada nó (gene) dessas redes é pontuado segundo fatores topológicos e de co-expressão. Baseado nesta pontuação, desenvolvemos dois escores de ranqueamento: um que prioriza genes com maior alteração entre suas pontuações em cada condição, e outro que privilegia genes com a maior soma destas pontuações. A aplicação do método a três estudos envolvendo dados de expressão de esquizofrenia recuperou com sucesso genes diferencialmente co-expressos em duas condições, e ao mesmo tempo evitou o viés da literatura. Além disso, houve uma melhoria substancial na replicação dos resultados pelo método aplicado aos três estudos, que por métodos convencionais não alcançavam replicabilidade satisfatória.Complex diseases are characterized as being poligenic and multifactorial, so this poses a challenge regarding the search for genes related to them. With the advent of high-throughput technologies for genome sequencing and gene expression measurements (transcriptome), as well as the knowledge of protein-protein interactions, complex diseases have been sistematically investigated. Particularly, Protein-Protein Interaction (PPI) networks have been used to prioritize genes related to complex diseases according to its topological features. However, PPI networks are affected by ascertainment bias, in which the most studied proteins tend to have more connections, degrading the quality of the results. Additionally, methods using only PPI networks can provide just static and non-specific results, since the topologies of these networks are not specific of a given disease. In this work, we developed a methodology to prioritize genes and biological pathways related to a given complex disease, through an approach that integrates data from PPI networks, transcriptomics and genomics, aiming to increase replicability of different studies and to discover new genes associated to the disease. The methodology integrates PPI network and gene expression data, and then applies the Network Medicine Hypotheses to the resulting network in order to connect seed genes (obtained from association studies) through shortest paths possessing larger coexpression among their genes. Gene expression data in two conditions (control and disease) are used to obtain two networks, where each node (gene) in these networks is rated according to topological and coexpression aspects. Based on this rating, we developed two ranking scores: one that prioritizes genes with the largest alteration between their ratings in each condition, and another that favors genes with the greatest sum of these scores. The application of this method to three studies involving schizophrenia expression data successfully recovered differentially co-expressed gene in two conditions, while avoiding the ascertainment bias. Furthermore, when applied to the three studies, the method achieved a substantial improvement in replication of results, while other conventional methods did not reach a satisfactory replicability.
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Liu, Yuanlong. "Development of network-based analysis methods with application to the genetic component of asthma." Thesis, Sorbonne Paris Cité, 2017. https://theses.md.univ-paris-diderot.fr/LIU_Yuanlong_2_va_20171113.pdf.
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Les études d'association pan-génomiques (GWAS) ont permis d'identifier de nouveaux locus associés à l'asthme, mais ces loci n'expliquent qu'une partie de la composante génétique de cette maladie. Une limite de ces études est qu'elles sont basées sur des analyses simple-marqueurs qui manquent de puissance pour détecter des variants génétiques à effet marginal faible et influençant conjointement le risque de maladie. Des stratégies, qui intègrent des connaissances biologiques, comme les interactions protéine-protéine (PPI) ou des réseaux de gènes avec des résultats de « GWAS », ont été proposées pour identifier des modules de gènes associés aux maladies. Les objectifs de cette thèse étaient de développer des méthodes d'analyse de réseaux de gènes, et de les appliquer à des données pan-génomiques de l'asthme pour identifier de nouveaux gènes candidats et des processus biologiques potentiellement impliqués dans l'asthme.Le premier travail de thèse a consisté à étendre une méthode de recherche de réseau de gènes à partir de données de « GWAS » (dmGWAS) pour identifier de nouveaux gènes associés à l'asthme. Nous avons utilisé deux jeux de données, chacun correspondant aux résultats d'une méta-analyse de neuf études d'association pan-génomiques de l'asthme de l'enfant (5,924 et 6,043 sujets, et appelés META1 et META2). Nous avons développé une nouvelle méthode pour calculer les p-valeurs de chaque gène à partir des p-valeurs des SNPs et proposé une stratégie de recherche bidirectionnelle à partir des deux jeux de données pan-génomiques pour identifier un module de gènes. Nous avons détecté un module de 91 gènes associé à l'asthme (p < 1e-5). Ce module est composé d'un réseau central et de cinq réseaux périphériques. Parmi les 91 gènes, 19 gènes étaient nominalement significatifs dans les deux jeux de données et incluaient 13 gènes à 4 loci trouvés précédemment associés à l'asthme (2q12, 5q31, 9p24.1, 17q12-q21), et six gènes à six nouveaux loci: CRMP1 (4p16.1), ZNF192 (6p22.1), RAET1E (6q24.3), CTSL1 (9p21.33), C12orf43 (12q24.31) et JAK3 (19p13-p12). L'analyse fonctionnelle du module identifié a révélé quatre clusters de gènes impliqués dans l'immunité innée et adaptative, la chimiotaxie, l'adhésion cellulaire et la régulation de la transcription, qui sont des processus biologiquement pertinents pour l'asthme.Le deuxième travail de thèse a consisté à développer une nouvelle méthode de réseau de gènes appelée SigMod. .SigMod permet de sélectionner un module de gènes enrichis en signaux d'association avec la maladie et montrant de fortes inter-connexions. Par rapport aux méthodes précédentes SigMod offre plusieurs avantages, notamment la robustesse au bruit de fond, la capacité de prendre en compte une pondération sur les liens entre gènes, et de rendre les résultats facilement interprétables. Nous avons proposé un algorithme basé sur la théorie des découpages de graphes pour résoudre le problème d'optimisation de manière exacte et efficace. Des simulations ont montré une meilleure performance de SigMod par rapport aux méthodes existantes. L'application de SigMod aux données de l'asthme a permis d'identifier un module de 190 gènes qui présentent des relations fonctionnelles et sont biologiquement pertinents pour l'asthmeGenome-wide association studies (GWAS) of asthma have been successful in identifying novel asthma-associated loci, but the genes at these loci account only for a part of the whole genetic component. One limitation of GWAS is that they rest on single-marker analyses which are underpowered to detect variants with small marginal effects but rather influence jointly disease risk. To complement the single-marker approaches, more sophisticated strategies, which integrate biological knowledge, such as protein-protein interactions (PPI) or gene networks with GWAS outcomes to identify disease-associated gene modules, have become prominent. The objectives of this thesis were to develop network-based analysis methods, and apply them to asthma GWAS data to identify biological processes and prioritize new candidate genes related to asthma.This thesis consists of two main studies. The first study was to extend an existing network-based method (dmGWAS) to identify novel genes associated with asthma. We used two GWAS datasets, each consisting of the results of a meta-analysis of nine childhood-onset asthma GWAS (5,924 and 6,043 subjects, called META1 and META2, respectively). We developed a novel method to compute gene-level p-values from SNP p-values (fastCGP), and proposed a bi-directional module search method to identify asthma-associated gene modules. Application of these methods to the asthma data detected a gene module of 91 genes significantly associated with asthma (p < 1e-5). This module consisted of a core network and five peripheral subnetworks including high-confidence candidates for asthma. Out of the 91 genes, 19 genes were nominally significant in both META1 and META2 datasets. They included 13 genes at 4 loci previously found associated with asthma (2q12, 5q31, 9p24.1, 17q12-q21), and six genes at six novel loci: CRMP1 (4p16.1), ZNF192 (6p22.1), RAET1E (6q24.3), CTSL1 (9p21.33), C12orf43 (12q24.31) and JAK3 (19p13-p12). Functional analysis of the module revealed four functionally related gene clusters involved in innate and adaptive immunity, chemotaxis, cell-adhesion and transcription regulation, which are biologically meaningful processes underlying asthma risk.The second study of this thesis was to develop a novel network-based method, named SigMod, to search disease-associated gene modules. SigMod takes a list of gene p-values and a gene network as input. It identifies a set of genes that are enriched in high association signals and tend to have strong interconnection via the formulation of a binary quadratic optimization problem. We proposed an algorithm based on graph-cut theory to solve the optimization problem exactly and efficiently. SigMod has several advantages compared to existing methods, including the ability to find the module enriched in highest association signals, the capacity to incorporate edge weights in the network, and the robustness to background noise. Also, the emphasis of selecting strongly interconnected genes can lead to the identification of genes with close functional relevance. We applied SigMod to both simulated and real datasets. This new method outperformed existing approaches. When SigMod was applied to childhood-onset asthma data, it successfully identified a module made of 190 functionally related genes that are biologically relevant for asthma
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Briones, Moreno Asier. "Evolution of DELLA proteins as transcriptional hubs in plants." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/159378.
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[ES] Las proteínas DELLA son elementos centrales de la ruta de señalización por giberelinas (GAs), donde actúan como represores de las respuestas a GAs. En angiospermas, se ha observado que las DELLAs interaccionan con cientos de factores de transcripción y otros reguladores transcripcionales, modulando de este modo la expresión génica. Por lo tanto, la participación generalizada de las GAs a lo largo del ciclo vital de las plantas es una consecuencia directa de la promiscuidad de las proteínas DELLA y de su rol como reguladores transcripcionales clave. Aunque las DELLAs se encuentran en todas las plantas terrestres, solo son reguladas por GAs en traqueofitas, en las cuales se han centrado la mayoría de los estudios previos. El trabajo aquí presentado pretende descifrar en qué punto de la evolución las DELLAs adquirieron las características moleculares que las convierten en "hubs", y qué ventajas biológicas podrían estar relacionadas con la evolución de las DELLAs. En el primer capítulo, describimos análisis comparativos de redes de co-expresión génicas asociadas a DELLA en especies vasculares y no vasculares, y proponemos que las DELLAs tienen un papel crítico en la conformación de panoramas transcripcionales. Desde su aparición en el ancestro de las plantas terrestres, conectaron múltiples programas transcripcionales que serían independientes sin ellas, mejoraron la eficiencia de la transmisión de información y aumentaron el nivel de complejidad en la regulación transcripcional. También observamos que este efecto se incrementó tras su integración en la señalización por GAs. En el segundo capítulo, proporcionamos pruebas experimentales más sólidas que extienden esta conclusión. Usando una combinación de rastreos de doble híbrido en levadura dirigidos, con DELLAs de diferentes posiciones en el linaje vegetal, y complementación heteróloga en plantas de Arabidopsis y Marchantia, mostramos que la promiscuidad es una característica conservada en todas las proteínas DELLA examinadas; lo cual sugiere que esta propiedad puede haber estado codificada en la DELLA ancestral, y después se mantuvo a lo largo de la evolución, con episodios de co-evolución entre las DELLAs y sus interactores. Finalmente, la comparación de dianas transcripcionales de las DELLAs en diferentes especies muestra la llamativa conservación de un pequeño conjunto de funciones reguladas por DELLAs en plantas vasculares y no vasculares -incluyendo la respuesta a factores de estrés-, mientras que análisis comparativos de promotores indican que las dianas específicas de cada especie aparecen mediante al menos dos mecanismos: el establecimiento de nuevas interacciones de la DELLA, y el acceso a nuevos promotores diana a través de interactores conservados.
En resumen, proponemos que las DELLAs son proteínas intrínsecamente promiscuas, con propiedades de "hub" en virtualmente todas las plantas, y la conservación de sus dianas transcripcionales depende en gran medida de la evolución de sus interactores. La conservación de las propiedades de "hub" de las proteínas DELLA las convierte en dianas biotecnológicas ideales, ya que la mayoría del conocimiento generado en una especie podría ser fácilmente adaptado a otras especies relativamente lejanas.[CA] Les proteïnes DELLA són elements centrals de la ruta de senyalització per gibberel·lines (GAs), on actuen com a repressors de les respostes a GAs. En angiospermes, s'ha observat que les DELLAs interaccionen amb centenars de factors de transcripció i altres reguladors transcripcionals, modulant d'aquesta manera l'expressió gènica. Per tant, la participació generalitzada de les GAs al llarg del cicle vital de les plantes és una conseqüència directa de la promiscuïtat de les proteïnes DELLA i del seu rol com a reguladors transcripcionals clau. Tot i que les DELLAs es troben en totes les plantes terrestres, només són regulades per GAs en traqueofites, en les quals s'han centrat la majoria dels estudis anteriors. El treball ací presentat pretén desxifrar en quin punt de l'evolució les DELLAs van adquirir les característiques moleculars que les converteixen en "hubs", i quins avantatges biològics podrien estar relacionats amb l'evolució de les DELLAs. En el primer capítol, descrivim anàlisis comparatius de xarxes de co-expressió gèniques associades a DELLA en espècies vasculars i no vasculars, i proposem que les DELLAs tenen un paper crític en la conformació de panorames transcripcionals. Des de la seua aparició en l'ancestre de les plantes terrestres, van connectar múltiples programes transcripcionals que serien independents sense elles, van millorar l'eficiència de la transmissió d'informació i augmentar el nivell de complexitat en la regulació transcripcional. També observem que aquest efecte es va incrementar després de la seua integració en la senyalització per GAs. En el segon capítol, proporcionem proves experimentals més sòlides que estenen aquesta conclusió. Usant una combinació de rastrejos de doble híbrid en rent dirigits, amb DELLAs de diferents posicions en el llinatge vegetal, i complementació heteròloga en plantes d'Arabidopsis i Marchantia, vam mostrar que la promiscuïtat és una característica conservada en totes les proteïnes DELLA examinades; la qual cosa suggereix que aquesta propietat pot haver estat codificada en la DELLA ancestral, i després es va mantenir al llarg de l'evolució, amb episodis de co-evolució entre les DELLAs i els seus interactors. Finalment, la comparació de dianes transcripcionals de les DELLAs en diferents espècies mostra la cridanera conservació d'un petit conjunt de funcions regulades per DELLAs en plantes vasculars i no vasculars -incloent la resposta a factors de estrès-, mentre que anàlisis comparatius de promotors indiquen que les dianes específiques de cada espècie apareixen mitjançant al menys dos mecanismes: l'establiment de noves interaccions de la DELLA, i l'accés a nous promotors diana a través d'interactors conservats. En resum, proposem que les DELLAs són proteïnes intrínsecament promíscues, amb propietats de "hub" en virtualment totes les plantes, i la conservació de les seues dianes transcripcionals depèn en gran mesura de l'evolució dels seus interactors. La conservació de les propietats de "hub" de les proteïnes DELLA les converteix en dianes biotecnològiques ideals, ja que la majoria del coneixement generat en una espècie podria ser fàcilment adaptat a altres espècies relativament llunyanes.
[EN] DELLA proteins are central elements of the gibberellin (GA) signaling pathway, where they act as repressors of GA responses. In angiosperms, DELLAs have been shown to interact with hundreds of transcription factors and other transcriptional regulators, thereby modulating gene expression. Hence, the widespread involvement of GAs along the plant life cycle is a direct consequence of the promiscuity of DELLA proteins and their role as key transcriptional regulators. Although DELLAs can be found in all land plants, they are only regulated by GAs in tracheophytes, where most of the previous studies have been focused. The work presented here aims to decipher at which point in evolution did DELLAs acquired the molecular features that render them as 'hubs', and what biological advantages could be related with DELLA evolution. In the first chapter, we describe comparative analyses of DELLA-associated gene co-expression networks in vascular and non-vascular species and propose that DELLAs have a critical role in the conformation of transcriptional landscapes. Upon their emergence in the ancestor of land plants, they connected multiple transcriptional programs that would be independent without them, improved the efficiency of information transmission and increased the level of complexity in transcriptional regulation. We also observed that this effect was enhanced after their integration in GA signaling. In the second chapter, we provide stronger experimental evidence that extends this conclusion. Using a combination of targeted yeast two-hybrid screenings with DELLAs from different positions in the plant lineage, and heterologous complementation in Arabidopsis and Marchantia plants, we show that promiscuity is a conserved feature in all the examined DELLA proteins, which suggests that this property might have been encoded in the ancestral DELLA, and then maintained along evolution, with episodes of co-evolution between DELLAs and their partners. Finally, comparison of DELLA transcriptional targets in different species shows a striking conservation of a small set of functions regulated by DELLAs in vascular and non-vascular plants -including the response to stress factors-, while comparative promoter analysis indicates that species-specific DELLA targets emerge through at least two mechanisms: establishment of novel DELLA interactions, and the access by conserved partners to new target promoters. In summary, we propose that DELLAs are intrinsically promiscuous proteins, with hub properties in virtually all land plants, and the conservation of their transcriptional targets largely depends on the evolution of their interactors. The conservation of the hub properties of DELLA proteins makes them ideal biotechnological targets, as most of the knowledge generated in one species could be readily adapted to other relatively distant species.
Esta tesis doctoral ha sido posible gracias a un contrato predoctoral FPU del Ministerio de Educación (FPU2014-01941).
Briones Moreno, A. (2020). Evolution of DELLA proteins as transcriptional hubs in plants [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/159378
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Lim, Ji-Hyun. "A computational approach to discovering p53 binding sites in the human genome." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/3388.
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The tumour suppressor p53 protein plays a central role in the DNA damage response/checkpoint pathways leading to DNA repair, cell cycle arrest, apoptosis and senescence. The activation of p53-mediated pathways is primarily facilitated by the binding of tetrameric p53 to two 'half-sites', each consisting of a decameric p53 response element (RE). Functional REs are directly adjacent or separated by a small number of 1-13 'spacer' base pairs (bp). The p53 RE is detected by exact or inexact matches to the palindromic sequence represented by the regular expression [AG][AG][AG]C[AT][TA]G[TC][TC][TC] or a position weight matrix (PWM). The use of matrix-based and regular expression pattern-matching techniques, however, leads to an overwhelming number of false positives. A more specific model, which combines multiple factors known to influence p53-dependent transcription, is required for accurate detection of the binding sites. In this thesis, we present a logistic regression based model which integrates sequence information and epigenetic information to predict human p53 binding sites. Sequence information includes the PWM score and the spacer length between the two half-sites of the observed binding site. To integrate epigenetic information, we analyzed the surrounding region of the binding site for the presence of mono- and trimethylation patterns of histone H3 lysine 4 (H3K4). Our model showed a high level of performance on both a high-resolution data set of functional p53 binding sites from the experimental literature (ChIP data) and the whole human genome. Comparing our model with a simpler sequence-only model, we demonstrated that the prediction accuracy of the sequence-only model could be improved by incorporating epigenetic information, such as the two histone modification marks H3K4me1 and H3K4me3.
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Carvunis, Anne-Ruxandra. "Des protéines et de leurs interactions aux principes évolutifs des systèmes biologiques." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS001/document.
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Darwin a révélé au monde que les espèces vivantes ne cessent jamais d’évoluer, mais les mécanismes moléculaires de cette évolution restent le sujet de recherches intenses. La biologie systémique propose que les relations entre génotype, environnement et phénotype soient sous-tendues par un ensemble de réseaux moléculaires dynamiques au sein de la cellule, mais l’organisation de ces réseaux demeure mystérieuse. En combinant des concepts établis en biologie évolutive et systémique avec la cartographie d’interactions protéiques et l’étude des méthodologies d’annotation de génomes, j’ai développé de nouvelles approches bioinformatiques qui ont en partie dévoilé la composition et l’organisation des systèmes cellulaires de trois organismes eucaryotes : la levure de boulanger, le nématode Caenorhabditis elegans et la plante Arabidopsis thaliana. L’analyse de ces systèmes m’a conduit à proposer des hypothèses sur les principes évolutifs des systèmes biologiques. En premier lieu, je propose une théorie selon laquelle la traduction fortuite de régions intergéniques produirait des peptides sur lesquels la sélection naturelle agirait pour aboutir occasionnellement à la création de protéines de novo. De plus, je montre que l’évolution de protéines apparues par duplication de gènes est corrélée avec celle de leurs profils d’interactions. Enfin, j’ai mis en évidence des signatures de la co-évolution ancestrale hôte-pathogène dans l’organisation topologique du réseau d‘interactions entre protéines de l’hôte. Mes travaux confortent l’hypothèse que les systèmes moléculaires évoluent, eux aussi, de manière darwinienneDarwin exposed to the world that living species continuously evolve. Yet the molecular mechanisms of evolution remain under intense research. Systems biology proposes that dynamic molecular networks underlie relationships between genotype, environment and phenotype, but the organization of these networks is mysterious. Combining established concepts from evolutionary and systems biology with protein interaction mapping and the study of genome annotation methodologies, I have developed new bioinformatics approaches that partially unveiled the composition and organization of cellular systems for three eukaryotic organisms: the baker’s yeast, the nematode Caenorhabditis elegans and the plant Arabidopsis thaliana. My analyses led to insights into the evolution of biological systems. First, I propose that the translation of peptides from intergenic regions could lead to de novo birth of new protein-coding genes. Second, I show that the evolution of proteins originating from gene duplications and of their physical interaction repertoires are tightly interrelated. Lastly, I uncover signatures of the ancestral host-pathogen co-evolution in the topology of a host protein interaction network. My PhD work supports the thesis that molecular systems also evolve in a Darwinian fashion
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Chen, Yen-Shan. "MAMMALIAN TESTIS-DETERMINING FACTOR SRY HAS EVOLVED TO THE EDGE OF AMBIGUITY." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1372888881.
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CAO, BAOQIANG. "ON APPLICATIONS OF STATISTICAL LEARNING TO BIOPHYSICS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1168577852.
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Ospina, Forero Luis Eduardo. "Modelling protein-protein interaction networks." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:c5150074-e160-4c59-8c23-f8804ae3dd2e.
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Proteins, the main motors of the cell, are in charge of performing a diverse array of biological functions. They rarely perform those functions alone, but generally work as groups of proteins that through a complex array of interactions perform a single biological function. These complex interactions between different proteins are often analysed via network theory, where a protein-protein interaction (PPI) network is created considering each protein as a node and each of their interactions as edges. Different approaches from the perspective of network analysis have been proposed to describe, analyse, and predict PPI networks. Some methods focus on the use of network summary statistics, community detection, random graph models, and machine learning procedures. However, despite the large effort invested in PPI network research, current models fail to describe well the structure of PPI networks. Small overrepresented subgraphs, which have been thought as the building blocks of networks, have been shown to be important patterns in gene regulatory networks, and there is evidence that suggests they may be evolutionarily conserved across the PPI networks of different organisms. Hence, a first step to better understand the structure of protein-protein interaction networks, is to describe how the local structure of these networks, accounted by the occurrence of small connected subgraphs, is created. We approach this problem in two stages. In the first stage, we provide a framework to statistically assess if a random graph model can describe the occurrence of different small connected subgraphs observed in PPI networks. Then, by applying this framework we find that state-of-the-art network comparison methods based on subgraph counts struggle at finding similarities between networks that have different numbers of nodes or edges. Hence, in joint work with Dr. Anatol Wegner, Dr. Robert Gaunt, Professor Gesine Reinert, and Professor Charlotte M. Deane, we propose a novel network comparison method, NetEmd, that tackles this problem indirectly by proposing a method that is invariant to translations and rescalings of subgraph count distributions, and which is better able to detect similarities across networks with different number of nodes or edges. In the second stage, we use NetEmd, along with three other state-of-the-art network comparison methods, to test the ability of several random graph models to describe the occurrence of subgraphs counts in the PPI networks of six organisms, and in multiple smaller sections of these networks. We find that the overall occurrence of small connected subgraphs could potentially be described by two network generation mechanisms operating in complementary sections of the PPI networks. In addition we find that cellular compartment-specific PPI networks can be potentially described by a single model that captures, with only two parameters, both, the common properties between the different cellular compartment networks, and their individual structural features.
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Popov, Nikita. "Expression and activity of Myc network proteins during cell cycle progression and differentiation /." Sundbyberg, 2004. http://diss.kib.ki.se/2004/91-7349-856-4/.
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Webber, Aaron. "Transcriptional co-regulation of microRNAs and protein-coding genes." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/transcriptional-coregulation-of-micrornas-and-proteincoding-genes(f5b601b2-33f3-4608-9ae8-b7d5a0c6beaf).html.
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This thesis was presented by Aaron Webber on the 4th December 2013 for the degree of Doctor of Philosophy from the University of Manchester. The title of this thesis is ‘Transcriptional co-regulation of microRNAs and protein-coding genes’. The thesis relates to gene expression regulation within humans and closely related primate species. We have investigated the binding site distributions from publically available ChIP-seq data of 117 transcription regulatory factors (TRFs) within the human genome. These were mapped to cis-regulatory regions of two major classes of genes, 20,000 genes encoding proteins and 1500 genes encoding microRNAs. MicroRNAs are short 20 - 24 nt noncoding RNAs which bind complementary regions within target mRNAs to repress translation. The complete collection of ChIP-seq binding site data is related to genomic associations between protein-coding and microRNA genes, and to the expression patterns and functions of both gene types across human tissues. We show that microRNA genes are associated with highly regulated protein-coding gene regions, and show rigorously that transcriptional regulation is greater than expected, given properties of these protein-coding genes. We find enrichment in developmental proteins among protein-coding genes hosting microRNA sequences. Novel subclasses of microRNAs are identified that lie outside of protein-coding genes yet may still be expressed from a shared promoter region with their protein-coding neighbours. We show that such microRNAs are more likely to form regulatory feedback loops with the transcriptional regulators lying in the upstream protein-coding promoter region. We show that when a microRNA and a TRF regulate one another, the TRF is more likely to sometimes function as a repressor. As in many studies, the data show that microRNAs lying downstream of particular TRFs target significantly many genes in common with these TRFs. We then demonstrate that the prevalence of such TRF/microRNA regulatory partnerships relates directly to the variation in mRNA expression across human tissues, with the least variable mRNAs having the most significant enrichment in such partnerships. This result is connected to theory describing the buffering of gene expression variation by microRNAs. Taken together, our study has demonstrated significant novel linkages between the transcriptional TRF and post-transcriptional microRNA-mediated regulatory layers. We finally consider transcriptional regulators alone, by mapping these to genes clustered on the basis of their expression patterns through time, within the context of CD4+ T cells from African green monkeys and Rhesus macaques infected with Simian immunodeficiency virus (SIV). African green monkeys maintain a functioning immune system despite never clearing the virus, while in rhesus macaques, the immune system becomes chronically stimulated leading to pathogenesis. Gene expression clusters were identified characterizing the natural and pathogenic host systems. We map transcriptional regulators to these expression clusters and demonstrate significant yet unexpected co-binding by two heterodimers (STAT1:STAT2 and BATF:IRF4) over key viral response genes. From 34 structural families of TRFs, we demonstrate that bZIPs, STATs and IRFs are the most frequently perturbed upon SIV infection. Our work therefore contributes to the characterization of both natural and pathogenic SIV infections, with longer term implications for HIV therapeutics.
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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 molecules involved. I have developed a probabilistic model of the burst size distribution (the number of proteins produced by the binding of one promoter) of a single gene. Recent experimental data provides excellent agreement with the model, but also reveals limitations of currently available data in determining the origin of variations in expression. A second strand of my work has addressed the dynamics of networks of genes. A network motif is a sub-graph that occurs more often in the network than would be expected by chance. The recurrent presence of certain motifs has been linked to systematic di erences in the functional properties of networks. I have developed models of the possible dynamical behaviour, in particular for the bi-fan motif, a small sub-network with four genes. This motif has been identi ed as the most prevalent in the regulatory networks of both the bacterium Escherichia coli and Saccharaomyces cerevisiae. The results of this work show that the microscopic details of the interactions are of paramount importance, with few inherent constraints on the network dynamics from consideration of network structure alone. This result is relevant to all attempts to model gene networks without su ciently detailed knowledge of the mechanisms of interaction.
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Braute, Petter, and Jorg Eliassen Rødsjø. "Protein function prediction using annotated protein-protein interaction networks." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9177.
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Jonsson, Pall Freyr. "Computational analysis of protein-protein interaction networks." Thesis, University College London (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439848.
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Sjöberg, Paul. "Numerical Methods for Stochastic Modeling of Genes and Proteins." Doctoral thesis, Uppsala universitet, Avdelningen för teknisk databehandling, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8293.
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Stochastic models of biochemical reaction networks are used for understanding the properties of molecular regulatory circuits in living cells. The state of the cell is defined by the number of copies of each molecular species in the model. The chemical master equation (CME) governs the time evolution of the the probability density function of the often high-dimensional state space. The CME is approximated by a partial differential equation (PDE), the Fokker-Planck equation and solved numerically. Direct solution of the CME rapidly becomes computationally expensive for increasingly complex biological models, since the state space grows exponentially with the number of dimensions. Adaptive numerical methods can be applied in time and space in the PDE framework, and error estimates of the approximate solutions are derived. A method for splitting the CME operator in order to apply the PDE approximation in a subspace of the state space is also developed. The performance is compared to the most widely spread alternative computational method.
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Kulis, Michael D. "Islet neogenesis associated protein-related protein from gene to folded protein /." Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-01112006-195113/.
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Thesis (Ph. D.)--Chemistry and Biochemistry, Georgia Institute of Technology, 2006.Shuker, Suzanne, Committee Chair ; Doyle, Donald, Committee Member ; Orville, Allen, Committee Member ; Barry, Bridgette, Committee Member ; McCarty, Nael, Committee Member.
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Kulis, Michael D. Jr. "Islet Neogenesis Associated Protein-Related Protein: From Gene to Folded Protein." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10436.
Full textAbstract:
Type 1 diabetes is the direct result of an autoimmune attack on the pancreatic islet cells. The islets contain b cells, which are the only type of cell capable of supplying insulin in the human body. The destruction of these cells leaves the diabetic to rely on exogenous insulin to maintain a normal blood sugar level. Insulin therapy allows the diabetic to deal with the symptoms of the disease, but does nothing for the underlying condition. In order to truly cure the disease, the strategy is to replenish the b cells in the diabetic. Islet neogenesis associated protein (INGAP) has been shown to regenerate islet cells and reverse experimentally-induced diabetes in animal models. The INGAP pentadecapeptide is a 15 amino acid peptide from INGAP with comparable activity to the full-length protein. This 15-mer is undergoing clinical trials for treating diabetes.
The overall goal of the project described in this work is to determine the structure of the INGAP pentadecapeptide for use in structure-based drug design of non-peptide mimics of the 15-mer. The first set of experiments in the present work directly examined the 15-mer in solution using NMR. No stable structure of the small peptide was found. The second set of experiments involved a homolog of INGAP, called INGAP-related protein, or INGAPrP. INGAPrP was recombinantly produced in E. coli and subsequently purified and refolded. Refolding of INGAPrP was verified by a 1H-15N HSQC experiment. CD experiments supported the NMR study, indicating helical content in INGAPrP. The folded nature of the protein will allow for the three-dimensional structure of INGAPrP to be determined. The protein structure will show the fold of the 15-mer within the full-length protein. This information will be valuable for the ultimate goal of producing structural mimics of the INGAP pentadecapeptide. Non-peptide mimics should have better oral bioavailability and longer half-lives in vivo.