Дисертації з теми "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.
Повний текст джерелаIn 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.
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/.
Повний текст джерелаAgarwal, Sumeet. "Networks in nature : dynamics, evolution, and modularity." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564283.
Повний текст джерелаYalamanchili, 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.
Повний текст джерелаpublished_or_final_version
Biochemistry
Doctoral
Doctor of Philosophy
Shelton, Rebecca Kay. "Parameter Identifiability and Estimation in Gene and Protein Interaction Networks." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32702.
Повний текст джерелаMaster of Science
King, James Lowell. "Gene Ontology-Guided Force-Directed Visualization of Protein Interaction Networks." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1066.
Повний текст джерелаYasar, 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.
Повний текст джерелаZhu, 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.
Повний текст джерелаBrettner, 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.
Повний текст джерелаAragüés, 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.
Повний текст джерелаThe 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.
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.
Повний текст джерелаProtein 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.
Toufighi, Kiana 1980. "Integrative study of gene expression and protein complexes." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/380907.
Повний текст джерелаEn 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.
Arda, H. Efsun. "C. Elegans Metabolic Gene Regulatory Networks: A Dissertation." eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/479.
Повний текст джерелаLi, 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.
Повний текст джерелаHlatshwayo, 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.
Повний текст джерелаKataka, 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.
Повний текст джерелаOgris, 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.
Повний текст джерелаAt the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 2: Manuscript.
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.
Повний текст джерелаBanduseela, 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.
Повний текст джерелаKumar, 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.
Повний текст джерелаLintner, 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.
Повний текст джерелаZagore, 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.
Повний текст джерелаAndrews, 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.
Повний текст джерелаZhou, 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.
Повний текст джерелаduVerle, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаChen, Jing. "Computational Selection and Prioritization of Disease Candidate Genes." University of Cincinnati / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1211228557.
Повний текст джерелаBrown, 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.
Повний текст джерелаScofield, 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.
Повний текст джерелаKim, Wooyoung. "Innovative Algorithms and Evaluation Methods for Biological Motif Finding." Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/cs_diss/63.
Повний текст джерелаJaeger, 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.
Повний текст джерелаScofield, 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.
Повний текст джерелаSchmid, 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.
Повний текст джерелаSimões, 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/.
Повний текст джерелаComplex 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.
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.
Повний текст джерелаGenome-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
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.
Повний текст джерела[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
TESIS
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.
Повний текст джерелаCarvunis, Anne-Ruxandra. "Des protéines et de leurs interactions aux principes évolutifs des systèmes biologiques." Thesis, Grenoble, 2011. http://www.theses.fr/2011GRENS001/document.
Повний текст джерелаDarwin 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
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.
Повний текст джерелаCAO, BAOQIANG. "ON APPLICATIONS OF STATISTICAL LEARNING TO BIOPHYSICS." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1168577852.
Повний текст джерелаOspina, 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.
Повний текст джерелаPopov, 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/.
Повний текст джерелаWebber, 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.
Повний текст джерелаIngram, Piers J. "Modelling gene regulatory networks." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/1375.
Повний текст джерелаBraute, 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.
Повний текст джерелаJonsson, 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.
Повний текст джерелаSjöberg, 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.
Повний текст джерелаKulis, 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/.
Повний текст джерелаShuker, Suzanne, Committee Chair ; Doyle, Donald, Committee Member ; Orville, Allen, Committee Member ; Barry, Bridgette, Committee Member ; McCarty, Nael, Committee Member.
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.
Повний текст джерела