• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 31
  • 3
  • 2
  • Tagged with
  • 45
  • 45
  • 14
  • 13
  • 10
  • 9
  • 8
  • 7
  • 6
  • 6
  • 6
  • 5
  • 5
  • 5
  • 5
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
11

RelA as a Potential Regulator of Inflammation and Tissue Damage in Streptozotocin-Induced Diabetic STAT5 Knockout Mice

Holtzapple, Emilee R. 13 May 2016 (has links)
No description available.
12

Novel Mechanisms Underlying Homocysteine-Suppressed Endothelial Cell Growth

Jan, Michael January 2014 (has links)
Cardiovascular disease (CVD) is the leading cause of death worldwide, and is projected to remain so for at least the next decade. Ever since its discovery in the urine and blood of children with inborn errors of metabolism, homocysteine (Hcy) at elevated plasma concentrations has been associated with CVD clinically and epidemiologically. Observational studies and meta-analyses have noted that changes in plasma Hcy by 5μM increase the odds ratio of developing coronary artery disease by 1.6-1.8 among other CVD. Clinical trials aimed at reducing plasma Hcy for benefit against development of subsequent cardiovascular events have had unconvincing results, but have moreover failed to address the mechanisms by which Hcy contributes to CVD. Recommendations from national agencies like the American Heart Association and the United States Preventive Services Task Force emphasize primordial prevention as a way to combat CVD. Reducing plasma Hcy as secondary and primary interventions does not fulfill this recommendation. In order to best understand the role of Hcy in CVD, an investigation into its mechanisms of action must be undertaken before measures of primordial prevention can be devised. Numerous experimental studies in the literature identify vascular endothelium as a target for the pathological effects of Hcy. Endothelial injury and impairment are contributory processes to atherosclerosis, and Hcy has been demonstrated to inhibit endothelial cell (EC) growth and proliferation through mechanisms involving cell cycle arrest, oxidative stress, and programmed cell death in vitro. Animal models have also confirmed that high levels of Hcy accelerate atherosclerotic plaque development and lead to impairment of vascular reendothelialization following injury. Hcy has been shown to have the opposite effect in vascular smooth muscle cells (SMC), causing their proliferation and again contributing to atherosclerosis. The cell-type specificity of Hcy remains to be understood, and among the aims of this research was to further characterize the effects of Hcy in EC. The overarching goal was discovery in order to direct future investigations of Hcy-mediated pathology. To begin, the first investigation considered the transcriptional and regulatory milieu in EC following exposure to Hcy. High-throughput screening using microarrays determined the effect of Hcy on 26,890 mRNA and 1,801 miRNA. Two different in vitro models of hyperhomocysteinemia (HHcy) were considered in this analysis. The first used a high dose of 500µ Hcy to mimic plasma concentrations of patients wherein the transsulfuration pathway of Hcy metabolism is impaired as in inborn cystathionine-ß-synthase deficiency. The other set of conditions used 50µ Hcy in the presence of adenosine to approximate impairment of the remethylation pathway of Hcy metabolism wherein s-adenosylhomocysteine accumulates, thus inhibiting s-adenosylmethionine formation and methylation reactions. These distinctions are important because most clinical trials do not distinguish between causes of HHcy, thereby ignoring the specific derangements underlying HHcy. mRNA and miRNA expression changes for both sets of treatment conditions identified CVD as a common network of Hcy-mediated pathology in EC. Moreover, methylation-specific conditions identified cell cycle modulation as a major contributory mechanism for this pathology, which agrees with recent findings in the literature. Analysis of significant mRNA changes and significant miRNA changes independently identified roles for Hcy in CVD and cell cycle regulation, thereby suggesting that miRNA may mediate the effects of Hcy in addition to gene expression changes alone. To investigate the role of Hcy in the cell cycle further, the next set of investigations considered the effect of Hcy under conditions approximating impaired remethylation in early cell cycle events. Previous studies have demonstrated that Hcy inhibits cyclin A transcription in EC via demethylation of its promoter. Conversely, Hcy induces cyclin A expression in SMC, again making the case for a cell type-specific mechanism in EC. Preceding cyclin A transcription and activation, canonical events in the early cell cycle include D-type cyclin activation, retinoblastoma protein (pRB) phosphorylation, and transcription factor E2F1 activation. In a series of in vitro experiments on EC, it was seen that Hcy inhibits expression of cyclin D2 and cyclin D3, but not cyclin D1. Next, pRB phosphorylation was seen to be decreased following treatment with Hcy. This also led to decreased E2F1 expression. However, this series of events could be reversed with E2F1 supplementation, allowing the cell cycle to proceed. As Hcy exerts a number of its effects via regulation of gene transcription, a final series of investigations aimed to predict potential targets of Hcy by examining patterns of transcription factor binding among known targets of Hcy regulation. Gene promoters of Hcy-modulated genes were analyzed in order to determine common transcription factors that potentially control their regulation. The locations of CpG-rich regions in promoters were identified to determine which regions would be most susceptible to regulation by DNA methylation. Next, high-throughput next-generation sequencing (NGS) and bisulfite NGS was performed for DNA from EC treated with Hcy in order to determine methylation changes after Hcy treatment. A number of potential transcription factors and their binding sites were identified as potential mediators of Hcy-mediated gene regulation. Taken together, these investigations represent an exploration of Hcy-mediated pathology in CVD, by focusing upon novel regulatory mechanisms in EC. Objective high-throughput arrays identified roles for Hcy in CVD and cell cycle pathways regulated by miRNA and gene expression, which were confirmed experimentally in vitro. These observations led to an investigation and identification of common transcription factors that potentially regulate Hcy-altered gene expression. This framework may be used to guide future investigations into the complex pathological network mediating the effects of Hcy in CVD. First, identification of a role for miRNA in mediating the effects of Hcy represents a novel regulatory mechanism, heretofore largely unexplored. Next, expanding the role of Hcy in EC cell cycle regulation to identify upstream mediators greatly adds to the published literature. Finally, noting that these changes center upon transcriptional and post-transcriptional regulation gives import to developing methods to characterize promoter and transcription factor regulation. The investigations presented herein and their results provide evidence that the future of Hcy research is vibrant, relevant, and not nearly surfeit. / Pharmacology
13

Integrative and Network-Based Approaches for Functional Interpretation of MetabolomicData

Patt, Andrew Christopher January 2021 (has links)
No description available.
14

Machine learning enabled bioinformatics tools for  analysis of biologically diverse samples

Lu, Yingzhou 25 August 2023 (has links)
Advanced molecular profiling technologies, utilizing the entire human genome, have opened new avenues to study biological systems. In recent decades, the generation of vast volumes of multi-omics data, spanning a broad range of phenotypes. Development of advanced bioinformatics tools to identify informative biomarkers from these data becomes increasingly important. These tools are crucial to extract meaningful biomarkers from this data, especially for understanding the biological pathways responsible for disease development. The identification of signature genes and the analysis of differentially networked genes are two fundamental and critically important tasks. However, many current methodologies employ test statistics that don't align perfectly with the signature definition, potentially leading to the identification of imprecise signatures. It may be challenging because the test statistics employed by many prevailing methods fall short of fulfilling the exact definition of a marker genes, inherently leaving them susceptible to deriving inaccurate features. The problem is further compounded when attempting to identify marker genes across biologically diverse samples, especially when comparing more than two biological conditions. Additionally, traditional differential group analysis or co-expression analysis under singular conditions often falls short in certain scenarios. For instance, the subtle expression levels of transcription factors (TFs) make their detection daunting, despite their pivotal role in guiding gene expression. Pinpointing the intricate network landscape of complex ailments and isolating core genes for subsequent analysis are challenging tasks. Yet, these marker genes are instrumental in identifing potential pivotal pathways. Multi-omics data, with its inherent complexity and diversity, presents unique challenges that traditional methods might struggle to address effectively. Recognizing this, our team sought to introduce new and innovative techniques specifically designed to handle this intricate dataset. To overcome these challenges, it is vital to develop and adopt innovative methods tailored to handle the complexity and diversity inherent in multi-omics data. In response to these challenges, we have pioneered the Cosine-based One-sample Test (COT), a method meticulously crafted for the analysis of biologically diverse samples. Tailored to discern marker genes across a spectrum of subtypes using their expression profiles, COT employs a one-sample test framework. The test statistic within COT utilizes cosine similarity, comparing a molecule's expression profile across various subtypes with the precise mathematical representation of ideal marker genes. To ensure ease of application and accessibility, we've encapsulated the COT workflow within a Python package. To assess its effectiveness, we undertook an exhaustive evaluation, juxtaposing the marker genes detection capabilities of COT against its contemporaries. This evaluation employed realistic simulation data. Our findings indicated that COT was not only adept at handling gene expression data but was also proficient with proteomics data. This data, sourced from enriched tissue or cell subtype samples, further accentuated COT's superior performance. We demonstrated the heightened effectiveness of COT when applied to gene expression and proteomics data originating from distinct tissue or cell subtypes. This led to innovative findings and hypotheses in several biomedical case studies. Additionally, we have enhanced the Differential Dependency Network (DDN) framework to detect network rewiring between different conditions where significantly rewired network modes serve as informative biomarkers. Using cross-condition data and a block-wise Lasso network model, DDN detects significant network rewiring together with a subnetwork of hub molecular entities. In DDN 3.0, we took the imbalanced sample size into the consideration, integrated several acceleration strategies to enable it to handle large datasets, and enhanced the network presentation for more informative network displays including color-coded differential dependency network and gradient heatmap. We applied it to the simulated data and real data to detect critical changes in molecular network topology. The current tool stands as a valuable blueprint for the development and validation of mechanistic disease models. This foundation aids in offering a coherent interpretation of data, deepening our understanding of disease biology, and sparking new hypotheses ripe for subsequent validation and exploration. As we chart our future course, our vision is to expand the scope of tools like COT and DDN 3.0, explore the vast realm of multi-omics data, including those from longitudinal studies or clinical trials. We're looking at incorporating datasets from longitudinal studies and clinical trials – domains where data complexity scales to new heights. We believe that these tools can facilitate more nuanced and comprehensive understanding of disease development and progression. Furthermore, by integrating these methods with other advanced bioinformatics and machine learning tools, we aim to create a holistic pipeline that will allow for seamless extraction of significant biomarkers and actionable insights from multi-omics data. This is a promising step towards precision medicine, where individual genomic information can guide personalized treatment strategies. / Doctor of Philosophy / Recent advances in technology have allowed us to study human biology on a much larger scale than ever before. These technologies have produced a lot of data on many different types of traits. As a result, it's becoming increasingly important to develop tools that can sift through this data and find meaningful biomarkers – essentially, indicators that can help us understand what causes diseases. Two key parts of this process are identifying 'signature genes' and analyzing groups of genes that work together differently depending on the circumstances. But, current methods have their drawbacks – they don't always pick out the right genes and can struggle when comparing more than two groups at once. There are also other challenges when it comes to identifying groups of genes that express differently or work together under one set of conditions. For instance, some important genes – known as transcription factors (TFs) – control the activity of other genes. But because TFs are often expressed at low levels, they're hard to detect, even though they play a key role in controlling gene activity. And, it can be tough to identify 'hub' genes, which are central to gene networks and can help us understand the potential key pathways in diseases. To address these challenges, we introduced the Cosine based One-sample Test (COT), a novel approach to identify pivotal genes across diverse samples. COT gauges the alignment of a gene's expression profile with the quintessential marker genes' definition. Our evaluations underscore COT's robust performance, paving the way for deeper disease understanding. Further enhancing our toolkit, we've refined the Differential Dependency Network (DDN), a method to unravel the dynamic interplay of genes under diverse conditions. DDN 3.0 is a more robust iteration, adept at accommodating varied sample sizes, efficiently processing vast datasets, and offering richer visualizations of gene networks. Its prowess in pinpointing crucial alterations in gene networks is noteworthy. The Cosine based One-sample Test (COT) and the Differential Dependency Network (DDN) are revolutionary tools, poised to significantly elevate genomics research. COT, with its precision in gauging the alignment of a gene's expression pattern with predefined ideal gene markers, emerges as an invaluable asset in the hunt for marker genes. It acts as a fine-tuned sieve, meticulously screening vast datasets to unveil these crucial genetic signposts. On the other hand, DDN offers a comprehensive framework to decipher the intricate web of gene interactions under diverse conditions. It meticulously analyzes the interplay between genes, spotlighting potential 'hub' genes and highlighting shifts in their dynamic relationships. Together, COT and DDN not only pave the way for the identification of pivotal marker genes but also furnish a richer, more nuanced understanding of the genomic landscape. By leveraging these tools, researchers are empowered to unravel the intricate tapestry of genes, laying the foundation for groundbreaking discoveries in genomics. Looking to the future, we plan to apply COT and DDN 3.0 to more complex datasets. We believe these tools will give us a better understanding of how diseases develop and progress. By integrating these methods with other advanced tools, we're aiming to create a complete system for extracting important biomarkers and insights from this complex data. This is a big step towards precision medicine, where a person's unique genetic information could guide their treatment strategy.
15

Wheat blast: quantitative pathway analyses for the Triticum pathotype of Magnaporthe oryzae and phenotypic reaction of U.S. wheat cultivars

Cruz, Christian D. January 1900 (has links)
Doctor of Philosophy / Department of Plant Pathology / William W. Bockus / James P. Stack / Wheat blast, caused by the Triticum pathotype of Magnaporthe oryzae (MoT), is a serious disease of wheat causing yield failures and significant economic losses during epidemic years in Brazil, Paraguay, and Bolivia. Although outbreaks occur only sporadically, wheat blast is considered a major disease affecting wheat production in South America and may be a threat to the wheat crop in the United States. Wheat is a major crop in the U.S. and wheat exports from the U.S. are important to food security of several countries around the World. Thus, it is important to understand the potential for MoT entry and establishment into the U.S. and to test U.S. wheat cultivars for susceptibility to MoT. The hypotheses of this research project were a) importing wheat grain from Brazil does not pose a risk for MoT establishment in the U.S., and b) resistance to MoT head infection does not exist in U.S. hard red winter wheat elite cultivars. Quantitative pathway analysis models were used to estimate the risk of MoT entry and establishment, in the coterminous U.S. and in a more targeted area within southeast North Carolina, via the importation of wheat grain from Brazil. The pathway model predicted that significant risk for MoT entry and establishment exists in some areas of the U.S. However, in approximately 60% of the coterminous U.S. winter wheat production areas the risk of MoT establishment was estimated to be zero. With respect to winter wheat growing areas in the U.S., conditions for MoT establishment and wheat blast outbreak occur only in small, restricted geographic areas. A higher resolution pathway analysis based on a ground transportation corridor in North Carolina indicated that conditions for MoT establishment exist seven out of ten years. Among U.S. cultivars tested, a continuum in severity to head blast was observed; cultivars Everest and Karl 92 were highly susceptible with more than 90% disease severity, while cultivars PostRock, Jackpot, Overley, Jagalene, Jagger, and Santa Fe showed less than 3% infection.
16

Statistical identification of metabolic reactions catalyzed by gene products of unknown function

Zheng, Lianqing January 1900 (has links)
Doctor of Philosophy / Department of Statistics / Gary L. Gadbury / High-throughput metabolite analysis is an approach used by biologists seeking to identify the functions of genes. A mutation in a gene encoding an enzyme is expected to alter the level of the metabolites which serve as the enzyme’s reactant(s) (also known as substrate) and product(s). To find the function of a mutated gene, metabolite data from a wild-type organism and a mutant are compared and candidate reactants and products are identified. The screening principle is that the concentration of reactants will be higher and the concentration of products will be lower in the mutant than in wild type. This is because the mutation reduces the reaction between the reactant and the product in the mutant organism. Based upon this principle, we suggest a method to screen the possible lipid reactant and product pairs related to a mutation affecting an unknown reaction. Some numerical facts are given for the treatment means for the lipid pairs in each treatment group, and relations between the means are found for the paired lipids. A set of statistics from the relations between the means of the lipid pairs is derived. Reactant and product lipid pairs associated with specific mutations are used to assess the results. We have explored four methods using the test statistics to obtain a list of potential reactant-product pairs affected by the mutation. The first method uses the parametric bootstrap to obtain an empirical null distribution of the test statistic and a technique to identify a family of distributions and corresponding parameter estimates for modeling the null distribution. The second method uses a mixture of normal distributions to model the empirical bootstrap null. The third method uses a normal mixture model with multiple components to model the entire distribution of test statistics from all pairs of lipids. The argument is made that, for some cases, one of the model components is that for lipid pairs affected by the mutation while the other components model the null distribution. The fourth method uses a two-way ANOVA model with an interaction term to find the relations between the mean concentrations and the role of a lipid as a reactant or product in a specific lipid pair. The goal of all methods is to identify a list of findings by false discovery techniques. Finally a simulation technique is proposed to evaluate properties of statistical methods for identifying candidate reactant-product pairs.
17

Dynamic flux estimation - a novel framework for metabolic pathway analysis

Goel, Gautam 20 August 2009 (has links)
High-throughput time series data characterizing magnitudes of gene expression, levels of protein activity, and the accumulation of select metabolites in vivo are being generated with increased frequency. These time profiles contain valuable information about the structure, dynamics and underlying regulatory mechanisms that govern the behavior of cellular systems. However, extraction and integration of this information into fully functional, computational and explanatory models has been a daunting task. Three types of issues have prevented successful outcomes in this inverse task of system identification. The first type pertains to the algorithmic and computational difficulties encountered in parameter estimation, be it using a genetic algorithm, nonlinear regression, or any other technique. The second type of issues stems from implicit assumptions that are made about the system topology and/or the functional model representing the biological system. These include the choice of intermediate pathway steps to be accounted for in the model, decisions on the irreversibility of a step, and the inclusion of ill-characterized regulatory signals. The third type of issue arises from the fact that there is often no unique set of parameter values, which when fitted to a model, reproduces the observed dynamics under one or several different sets of experimental conditions. This latter issue raises intriguing questions about the validity of the parameter values and the model itself. The central focus of my research has been to design a workflow for parameter estimation and system identification from biological time series data that resolves the issues outlined above. In this thesis I present the theory and application of a novel framework, called Dynamic Flux Estimation (DFE), for system identification from biological time-series data.
18

Application of bioinformatics in studies of sphingolipid biosynthesis

Momin, Amin Altaf 17 May 2010 (has links)
The studies in this dissertation demonstrate that the gene expression pathway maps are useful tools to notice alteration in different branches of sphingolipid biosynthesis pathway based on microarray and other transcriptomic analysis. To facilitate the integrative analysis of gene expression and sphingolipid amounts, updated pathway maps were prepared using an open access visualization tool, Pathvisio v1.1. The datasets were formatted using Perl scripts and visualized with the aid of color coded pathway diagrams. Comparative analysis of transcriptomics and sphingolipid alterations from experimental studies and published literature revealed 72.8 % correlation between mRNA and sphingolipid differences (p-value < 0.0001 by the Fisher's exact test).The high correlation between gene expression differences and sphingolipid alterations highlights the application of this tool to evaluate molecular changes associate with sphingolipid alterations as well as predict differences in specific metabolites that can be experimentally verified using sensitive approaches such as mass spectrometry. In addition, bioinformatics sequence analysis was used to identify transcripts for sphingolipid biosynthesis enzyme 3-ketosphinganine reductase, and homology modeling studies helped in the evaluation of a cell line defective in sphingolipid metabolism due to mutation in the enzyme serine palmitoyltransferase, the first enzyme of de novo biosynthesis pathway. Hence, the combination of different bioinformatics approaches, including protein and DNA sequence analysis, structure modeling and pathway diagrams can provide valuable inputs for biochemical and molecular studies of sphingolipid metabolism.
19

Outcomes of Myosin 1C Gene Expression Depletion on Cancer-related Pathways, in Vitro and in Clinical Samples

Pfister, Anna January 2016 (has links)
The unconventional myosin IC has previously been suggested to be a haploinsufficient tumour suppressor. The mechanism for this action has hitherto been unknown, however, and hence we decided to attempt to elucidate the genes involved. The first study involved knock-down of MYO1C using siRNA technology followed by whole transcriptiome microarray analysis performed on samples taken at different time points post transfection. This revealed a cornucopia of differential expressions compared to the negative control, among them we found an early up-regulation of the PI3K/AKT pathway and the pathway for prostate cancer. Among the down regulated pathways we found endometrial-, colorectal cancer and small cell lung cancer as well as the cell cycle pathway which was a little counter intuitive to the hypothesis that MYO1C suppresses cancer. For the next study six different genes (CCND1, CCND2, CDKN2B, CDKN2C, MYC, RBL1) important for the transitions into S-phase of the cell cycle were therefore chosen for validation using qPCR. These six genes and MYO1C were analysed on both the original time series and a new biological replicate as well as a well stratified set of endometrial carcinoma samples. We were able to verify the significant down-regulation of CCND2 in both time series indicating that this is caused by the depletion of MYO1C. In the tumour samples we saw a negative correlation between the expression of MYO1C and FIGO grade corroborating results previously found by our group when looking at protein expression.
20

Modeling Functional Modules Using Statistical and Machine Learning Methods

Cubuk, Cankut 30 November 2020 (has links)
[ES] La comprensión de los aspectos de la funcionalidad de las células que cuentan para los mecanismos de las enfermedades es el mayor reto de la medicina personalizada. A pesar de la disponibilidad creciente de los datos de genómica y transcriptómica, sigue existiendo una notable brecha entre la detección de las perturbaciones en la expresión de genes y la comprensión de su contribución en los mecanismos moleculares que últimamente tienen relación importante con el fenotipo estudiado. A lo largo de la última década, distintos modelos computacionales y matemáticos se han propuesto para el análisis de las rutas. Sin embargo, estos modelos no toman en cuenta los mecanismos dinámicos de las rutas como la estructura y las interacciones entre genes y proteínas. En esta tesis doctoral, presento dos modelos matemáticos ligeramente distintos, para integrar los datos transcriptómicos masivos de humano con un conocimiento previo de de las rutas de señalización y metabólicas para estimar las actividades mecánicas que están detrás de esas rutas (MPAs). Las MPAs son variables continuas con valores de nivel individual que pueden ser usadas con los modelos de aprendizaje de máquinas y métodos estadísticos para determinar los biomarcadores que podemos usar para los diagnósticos tempranos y la clasificación de subtipos de enfermedades, además de poder sugerir las dianas terapéuticas potenciales para las intervenciones individualizadas. El objetivo global es desarrollar nuevos y avanzados enfoques de la biología de sistemas para proponer unas hipótesis funcionales que nos ayuden a entender e interpretar los mecanismos complejos de las enfermedades. Estos mecanismos son cruciales para mejorar los tratamientos personalizados y predecir los resultados clínicos. En primer lugar, contribuí al desarrollo de un método que está diseñado para extraer las subrutas elementales desde la ruta de señalización con sus actividades estimadas. Posteriormente, este algoritmo se ha adaptado a los módulos metabólicos y se ha implementado como una herramienta web. Finalmente , el método ha revelado un panorama metabólico para una lista completa de diferentes tipos de cánceres. En este estudio, analicé el perfil metabólico de 25 tipos de cáncer distintos y se validó el método usando varios enfoques computacionales y experimentales. Cada método desarrollado en esta tesis ha sido enfrentado a otros métodos similares existentes, evaluados por sus sensibilidades y especificidades, experimentalmente validados cuando fue posible y usados para predecir resultados clínicos de varios tipos de cánceres. La investigación descrita en esta tesis y los resultados obtenidos fueron publicados en distintas revistas arbitradas que están relacionadas con el cáncer y biología de sistemas, y también en los periódicos nacionales. / [CA] La comprensió dels aspectes de la funcionalitat de les cèl·lules que compten per als mecanismes de les malalties és el major repte de la medicina personalitzada. Malgrat la disponibilitat creixent de les dades de genòmica i transcriptómica, continua existint una notable bretxa entre la detecció de les pertorbacions en l'expressió de gens i la comprensió de la seua contribució en els mecanismes moleculars que últimament tenen relació important amb el fenotip estudiat. Al llarg de l'última dècada, diferents models computacionals i matemàtics s'han proposat per a l'anàlisi de les rutes. No obstant això, aquests models no tenen en compte els mecanismes dinàmics de les rutes com l'estructura i les interaccions entre gens i proteïnes. En aquesta tesi doctoral, presente dos models matemàtics lleugerament diferents, per a integrar les dades transcriptómicos massius d'humà amb un coneixement previ de de les rutes de senyalització i metabòliques per a estimar les activitats mecàniques que estan darrere d'aqueixes rutes (MPAs). Les MPAs són variables contínues amb valors de nivell individual que poden ser usades amb els models d'aprenentatge de màquines i mètodes estadístics per a determinar els biomarcadores que podem usar per als diagnòstics primerencs i la classificació de subtipus de malalties, a més de poder suggerir les dianes terapèutiques potencials per a les intervencions individualitzades. L'objectiu global és desenvolupar nous i avançats enfocaments de la biologia de sistemes per a proposar unes hipòtesis funcionals que ens ajuden a entendre i interpretar els mecanismes complexos de les malalties. Aquests mecanismes són crucials per a millorar els tractaments personalitzats i predir els resultats clínics. En primer lloc, vaig contribuir al desenvolupament d'un mètode que està dissenyat per a extraure les subrutas elementals des de la ruta de senyalització amb les seues activitats estimades. Posteriorment, aquest algorisme s'ha adaptat als mòduls metabòlics i s'ha implementat com una eina web. Finalment, el mètode ha revelat un panorama metabòlic per a una llista completa de diferents tipus de càncers. En aquest estudi, vaig analitzar el perfil metabòlic de 25 tipus de càncer diferents i es va validar el mètode usant diversos enfocaments computacionals i experimentals. Cada mètode desenvolupat en aquesta tesi ha sigut enfrontat a altres mètodes similars existents, avaluats per les seues sensibilitats i especificitats, experimentalment validats quan va ser possible i usats per a predir resultats clínics de diversos tipus de càncers. La investigació descrita en aquesta tesi i els resultats obtinguts van ser publicats en diferents revistes arbitrades que estan relacionades amb el càncer i biologia de sistemes, i també en els periòdics nacionals. / [EN] Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is the main challenge for precision medicine. In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Over the last decade, different computational and mathematical models have been proposed for pathway analysis. However, they are not taking into account the dynamic mechanisms contained by pathways as represented in their layout and the interactions between genes and proteins. In this thesis, I present two slightly different mathematical models to integrate human transcriptomic data with prior knowledge of signalling and metabolic pathways to estimate the Mechanistic Pathway Activities (MPAs). MPAs are continuous and individual level values that can be used with machine learning and statistical methods to determine biomarkers for the early diagnosis and subtype classification of the diseases, and also to suggest potential therapeutic targets for individualized therapeutic interventions. The overall objective is, developing new and advanced systems biology approaches to propose functional hypotheses that help us to understand and interpret the complex mechanism of the diseases. These mechanisms are crucial for robust personalized drug treatments and predict clinical outcomes. First, I contributed to the development of a method which is designed to extract elementary sub-pathways from a signalling pathway and to estimate their activity. Second, this algorithm adapted to metabolic modules and it is implemented as a webtool. Third, the method used to reveal a pan-cancer metabolic landscape. In this study, I analyzed the metabolic module profile of 25 different cancer types and the method is also validated using different computational and experimental approaches. Each method developed in this thesis was benchmarked against the existing similar methods, evaluated for their sensitivity and specificity, experimentally validated when it is possible and used to predict clinical outcomes of different cancer types. The research described in this thesis and the results obtained were published in different systems biology and cancer-related peer-reviewed journals and also in national newspapers. / Cubuk, C. (2020). Modeling Functional Modules Using Statistical and Machine Learning Methods [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156175 / TESIS

Page generated in 0.4877 seconds