@phdthesis{Breitenbach2019,
  author    = {Breitenbach, Tim},
  title     = {A mathematical optimal control based approach to pharmacological modulation with regulatory networks and external stimuli},
  doi       = {10.25972/OPUS-17436},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-174368},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {In this work models for molecular networks consisting of ordinary differential equations are extended by terms that include the interaction of the corresponding molecular network with the environment that the molecular network is embedded in. These terms model the effects of the external stimuli on the molecular network. The usability of this extension is demonstrated with a model of a circadian clock that is extended with certain terms and reproduces data from several experiments at the same time. Once the model including external stimuli is set up, a framework is developed in order to calculate external stimuli that have a predefined desired effect on the molecular network. For this purpose the task of finding appropriate external stimuli is formulated as a mathematical optimal control problem for which in order to solve it a lot of mathematical methods are available. Several methods are discussed and worked out in order to calculate a solution for the corresponding optimal control problem. The application of the framework to find pharmacological intervention points or effective drug combinations is pointed out and discussed. Furthermore the framework is related to existing network analysis tools and their combination for network analysis in order to find dedicated external stimuli is discussed. The total framework is verified with biological examples by comparing the calculated results with data from literature. For this purpose platelet aggregation is investigated based on a corresponding gene regulatory network and associated receptors are detected. Furthermore a transition from one to another type of T-helper cell is analyzed in a tumor setting where missing agents are calculated to induce the corresponding switch in vitro. Next a gene regulatory network of a myocardiocyte is investigated where it is shown how the presented framework can be used to compare different treatment strategies with respect to their beneficial effects and side effects quantitatively. Moreover a constitutively activated signaling pathway, which thus causes maleficent effects, is modeled and intervention points with corresponding treatment strategies are determined that steer the gene regulatory network from a pathological expression pattern to physiological one again.},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{Wolter2014,
  author    = {Wolter, Steve},
  title     = {Single-molecule localization algorithms in super-resolution microscopy},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-109370},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Lokalisationsmikroskopie ist eine Methodenklasse der superaufl{\"o}senden Fluoreszenzmikroskopie, deren Methoden sich durch stochastische zeitliche Isolation der Fluoreszenzemission auszeichnen. Das Blinkverhalten von Fluorophoren wird so ver{\"a}ndert, dass gleichzeitige Aktivierung von einander nahen Fluorophoren unwahrscheinlich ist. Bekannte okalisationsmikroskopische Methoden umfassen dSTORM, STORM, PALM, FPALM, oder GSDIM. Lokalisationsmikroskopie ist von hohem biologischem Interesse, weil sie die Aufl{\"o}sung des Fluoreszenzmikroskops bei minimalem technischem Aufwand um eine Gr{\"o}ßenordnung verbessert. Der verbundene Rechenaufwand ist allerdings erheblich, da Millionen von Fluoreszenzemissionen einzeln mit Nanometergenauigkeit lokalisiert werden m{\"u}ssen. Der Rechen- und Implementationsaufwand dieser Auswertung hat die Verbreitung der superaufl{\"o}senden Mikroskopie lange verz{\"o}gert. Diese Arbeit beschreibt meine algorithmische Grundstruktur f{\"u}r die Auswertung lokalisationsmikroskopischer Daten. Die Echtzeitf{\"a}higkeit, d.h. eine Auswertegeschwindigkeit oberhalb der Datenaufnahmegeschwindigkeit an normalen Messaufbauten, meines neuartigen und quelloffenen Programms wird demonstriert. Die Geschwindigkeit wird auf verbrauchermarktg{\"a}ngigen Prozessoren erreicht und dadurch spezialisierte Rechenzentren oder der Einsatz von Grafikkarten vermieden. Die Berechnung wird mit dem allgemein anerkannten Gaussschen Punktantwortmodell und einem Rauschmodell auf Basis der gr{\"o}ßten Poissonschen Wahrscheinlichkeit durchgef{\"u}hrt. Die algorithmische Grundstruktur wird erweitert, um robuste und optimale Zweifarbenauswertung zu realisieren und damit korrelative Mikroskopie zwischen verschiedenen Proteinen und Strukturen zu erm{\"o}glichen. Durch den Einsatz von kubischen Basissplines wird die Auswertung von dreidimensionalen Proben vereinfacht und stabilisiert, um pr{\"a}zisem Abbilden von mikrometerdicken Proben n{\"a}her zu kommen. Das Grenzverhalten von Lokalisationsalgorithmen bei hohen Emissionsdichten wird untersucht. Abschließend werden Algorithmen f{\"u}r die Anwendung der Lokalisationsmikroskopie auf verbreitete Probleme der Biologie aufgezeigt. Zellul{\"a}re Bewegung und Motilit{\"a}t werden anhand der in vitro Bewegung von Myosin-Aktin-Filamenten studiert. Lebendzellbildgebung mit hellen und stabilen organischen Fluorophoren wird mittels SNAP-tag-Fusionsproteinen realisiert. Die Analyse des Aufbaus von Proteinklumpen zeigt, wie Lokalisationsmikroskopie neue quantitative Ans{\"a}tze jenseits reiner Bildgebung bietet.},
  subject      = {Fluoreszenzmikroskopie},
  language  = {en}
}
@phdthesis{ZeeshangebMajeed2014,
  author    = {Zeeshan [geb. Majeed], Saman},
  title     = {Implementation of Bioinformatics Methods for miRNA and Metabolic Modelling},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-102900},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Dynamic interactions and their changes are at the forefront of current research in bioinformatics and systems biology. This thesis focusses on two particular dynamic aspects of cellular adaptation: miRNA and metabolites. miRNAs have an established role in hematopoiesis and megakaryocytopoiesis, and platelet miRNAs have potential as tools for understanding basic mechanisms of platelet function. The thesis highlights the possible role of miRNAs in regulating protein translation in platelet lifespan with relevance to platelet apoptosis and identifying involved pathways and potential key regulatory molecules. Furthermore, corresponding miRNA/target mRNAs in murine platelets are identified. Moreover, key miRNAs involved in aortic aneurysm are predicted by similar techniques. The clinical relevance of miRNAs as biomarkers, targets, resulting later translational therapeutics, and tissue specific restrictors of genes expression in cardiovascular diseases is also discussed. In a second part of thesis we highlight the importance of scientific software solution development in metabolic modelling and how it can be helpful in bioinformatics tool development along with software feature analysis such as performed on metabolic flux analysis applications. We proposed the "Butterfly" approach to implement efficiently scientific software programming. Using this approach, software applications were developed for quantitative Metabolic Flux Analysis and efficient Mass Isotopomer Distribution Analysis (MIDA) in metabolic modelling as well as for data management. "LS-MIDA" allows easy and efficient MIDA analysis and, with a more powerful algorithm and database, the software "Isotopo" allows efficient analysis of metabolic flows, for instance in pathogenic bacteria (Salmonella, Listeria). All three approaches have been published (see Appendices).},
  subject      = {miRNS},
  language  = {en}
}
@phdthesis{Foerster2010,
  author    = {F{\"o}rster, Frank},
  title     = {Making the most of phylogeny: Unique adaptations in tardigrades and 216374 internal transcribed spacer 2 structures},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-51466},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {The phylum Tardigrada consists of about 1000 described species to date. The animals live in habitats within marine, freshwater and terrestrial ecosystems allover the world. Tardigrades are polyextremophiles. They are capable to resist extreme temperature, pressure or radiation. In the event of desiccation, tardigrades enter a so-called tun stage. The reason for their great tolerance capabilities against extreme environmental conditions is not discovered yet. Our Funcrypta project aims at finding answers to the question what mechanisms underlie these adaption capabilities particularly with regard to the species Milnesium tardigradum. The first part of this thesis describes the establishment of expressed sequence tags (ESTs) libraries for different stages of M. tardigradum. From proteomics data we bioinformatically identified 144 proteins with a known function and additionally 36 proteins which seemed to be specific for M. tardigradum. The generation of a comprehensive web-based database allows us to merge the proteome and transcriptome data. Therefore we created an annotation pipeline for the functional annotation of the protein and nucleotide sequences. Additionally, we clustered the obtained proteome dataset and identified some tardigrade-specific proteins (TSPs) which did not show homology to known proteins. Moreover, we examined the heat shock proteins of M. tardigradum and their different expression levels depending on the actual state of the animals. In further bioinformatical analyses of the whole data set, we discovered promising proteins and pathways which are described to be correlated with the stress tolerance, e.g. late embryogenesis abundant (LEA) proteins. Besides, we compared the tardigrades with nematodes, rotifers, yeast and man to identify shared and tardigrade specific stress pathways. An analysis of the 50 and 30 untranslated regions (UTRs) demonstrates a strong usage of stabilising motifs like the 15-lipoxygenase differentiation control element (15-LOX-DICE) but also reveals a lack of other common UTR motifs normally used, e.g. AU rich elements. The second part of this thesis focuses on the relatedness between several cryptic species within the tardigrade genus Paramacrobiotus. Therefore for the first time, we used the sequence-structure information of the internal transcribed spacer 2 (ITS2) as a phylogenetic marker in tardigrades. This allowed the description of three new species which were indistinguishable using morphological characters or common molecular markers like the 18S ribosomal ribonucleic acid (rRNA) or the Cytochrome c oxidase subunit I (COI). In a large in silico simulation study we also succeeded to show the benefit for the phylogenetic tree reconstruction by adding structure information to the ITS2 sequence. Next to the genus Paramacrobiotus we used the ITS2 to corroborate a monophyletic DO-group (Sphaeropleales) within the Chlorophyceae. Additionally we redesigned another comprehensive database—the ITS2 database resulting in a doubled number of sequence-structure pairs of the ITS2. In conclusion, this thesis shows the first insights (6 first author publications and 4 coauthor publications) into the reasons for the enormous adaption capabilities of tardigrades and offers a solution to the debate on the phylogenetic relatedness within the tardigrade genus Paramacrobiotus.},
  subject      = {Phylogenie},
  language  = {en}
}
@phdthesis{Beisser2011,
  author    = {Beisser, Daniela},
  title     = {Integrated functional analysis of biological networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-70150},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {In recent years high-throughput experiments provided a vast amount of data from all areas of molecular biology, including genomics, transcriptomics, proteomics and metabolomics. Its analysis using bioinformatics methods has developed accordingly, towards a systematic approach to understand how genes and their resulting proteins give rise to biological form and function. They interact with each other and with other molecules in highly complex structures, which are explored in network biology. The in-depth knowledge of genes and proteins obtained from high-throughput experiments can be complemented by the architecture of molecular networks to gain a deeper understanding of biological processes. This thesis provides methods and statistical analyses for the integration of molecular data into biological networks and the identification of functional modules, as well as its application to distinct biological data. The integrated network approach is implemented as a software package, termed BioNet, for the statistical language R. The package includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes and edges of these networks as well as methods for subnetwork search and visualisation. The exact algorithm is extensively tested in a simulation study and outperforms existing heuristic methods for the calculation of this NP-hard problem in accuracy and robustness. The variability of the resulting solutions is assessed on perturbed data, mimicking random or biased factors that obscure the biological signal, generated for the integrated data and the network. An optimal, robust module can be calculated using a consensus approach, based on a resampling method. It summarizes optimally an ensemble of solutions in a robust consensus module with the estimated variability indicated by confidence values for the nodes and edges. The approach is subsequently applied to two gene expression data sets. The first application analyses gene expression data for acute lymphoblastic leukaemia (ALL) and differences between the subgroups with and without an oncogenic BCR/ABL gene fusion. In a second application gene expression and survival data from diffuse large B-cell lymphomas are examined. The identified modules include and extend already existing gene lists and signatures by further significant genes and their interactions. The most important novelty is that these genes are determined and visualised in the context of their interactions as a functional module and not as a list of independent and unrelated transcripts. In a third application the integrative network approach is used to trace changes in tardigrade metabolism to identify pathways responsible for their extreme resistance to environmental changes and endurance in an inactive tun state. For the first time a metabolic network approach is proposed to detect shifts in metabolic pathways, integrating transcriptome and metabolite data. Concluding, the presented integrated network approach is an adequate technique to unite high-throughput experimental data for single molecules and their intermolecular dependencies. It is flexible to apply on diverse data, ranging from gene expression changes over metabolite abundances to protein modifications in a combination with a suitable molecular network. The exact algorithm is accurate and robust in comparison to heuristic approaches and delivers an optimal, robust solution in form of a consensus module with confidence values. By the integration of diverse sources of information and a simultaneous inspection of a molecular event from different points of view, new and exhaustive insights into biological processes can be acquired.},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{Arumugam2010,
  author    = {Arumugam, Manimozhiyan},
  title     = {Comparative metagenomic analysis of the human intestinal microbiota},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-55903},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {The human gut is home for thousands of microbes that are important for human life. As most of these cannot be cultivated, metagenomics is an important means to understand this important community. To perform comparative metagenomic analysis of the human gut microbiome, I have developed SMASH (Simple metagenomic analysis shell), a computational pipeline. SMASH can also be used to assemble and analyze single genomes, and has been successfully applied to the bacterium Mycoplasma pneumoniae and the fungus Chaetomium thermophilum. In the context of the MetaHIT (Metagenomics of the human intestinal tract) consortium our group is participating in, I used SMASH to validate the assembly and to estimate the assembly error rate of 576.7 Gb metagenome sequence obtained using Illumina Solexa technology from fecal DNA of 124 European individuals. I also estimated the completeness of the gene catalogue containing 3.3 million open reading frames obtained from these metagenomes. Finally, I used SMASH to analyze human gut metagenomes of 39 individuals from 6 countries encompassing a wide range of host properties such as age, body mass index and disease states. We find that the variation in the gut microbiome is not continuous but stratified into enterotypes. Enterotypes are complex host-microbial symbiotic states that are not explained by host properties, nutritional habits or possible technical biases. The concept of enterotypes might have far reaching implications, for example, to explain different responses to diet or drug intake. We also find several functional markers in the human gut microbiome that correlate with a number of host properties such as body mass index, highlighting the need for functional analysis and raising hopes for the application of microbial markers as diagnostic or even prognostic tools for microbiota-associated human disorders.},
  subject      = {Darmflora},
  language  = {en}
}
@phdthesis{Vainshtein2010,
  author    = {Vainshtein, Yevhen},
  title     = {Applying microarray-based techniques to study gene expression patterns: a bio-computational approach},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-51967},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {The regulation and maintenance of iron homeostasis is critical to human health. As a constituent of hemoglobin, iron is essential for oxygen transport and significant iron deficiency leads to anemia. Eukaryotic cells require iron for survival and proliferation. Iron is part of hemoproteins, iron-sulfur (Fe-S) proteins, and other proteins with functional groups that require iron as a cofactor. At the cellular level, iron uptake, utilization, storage, and export are regulated at different molecular levels (transcriptional, mRNA stability, translational, and posttranslational). Iron regulatory proteins (IRPs) 1 and 2 post-transcriptionally control mammalian iron homeostasis by binding to iron-responsive elements (IREs), conserved RNA stem-loop structures located in the 5'- or 3'- untranslated regions of genes involved in iron metabolism (e.g. FTH1, FTL, and TFRC). To identify novel IRE-containing mRNAs, we integrated biochemical, biocomputational, and microarray-based experimental approaches. Gene expression studies greatly contribute to our understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production and manipulations are limiting factors, affecting data quality. The use of customized DNA microarrays improves overall data quality in many situations, however, only if for these specifically designed microarrays analysis tools are available. Methods In this project response to the iron treatment was examined under different conditions using bioinformatical methods. This would improve our understanding of an iron regulatory network. For these purposes we used microarray gene expression data. To identify novel IRE-containing mRNAs biochemical, biocomputational, and microarray-based experimental approaches were integrated. IRP/IRE messenger ribonucleoproteins were immunoselected and their mRNA composition was analysed using an IronChip microarray enriched for genes predicted computationally to contain IRE-like motifs. Analysis of IronChip microarray data requires specialized tool which can use all advantages of a customized microarray platform. Novel decision-tree based algorithm was implemented using Perl in IronChip Evaluation Package (ICEP). Results IRE-like motifs were identified from genomic nucleic acid databases by an algorithm combining primary nucleic acid sequence and RNA structural criteria. Depending on the choice of constraining criteria, such computational screens tend to generate a large number of false positives. To refine the search and reduce the number of false positive hits, additional constraints were introduced. The refined screen yielded 15 IRE-like motifs. A second approach made use of a reported list of 230 IRE-like sequences obtained from screening UTR databases. We selected 6 out of these 230 entries based on the ability of the lower IRE stem to form at least 6 out of 7 bp. Corresponding ESTs were spotted onto the human or mouse versions of the IronChip and the results were analysed using ICEP. Our data show that the immunoselection/microarray strategy is a feasible approach for screening bioinformatically predicted IRE genes and the detection of novel IRE-containing mRNAs. In addition, we identified a novel IRE-containing gene CDC14A (Sanchez M, et al. 2006). The IronChip Evaluation Package (ICEP) is a collection of Perl utilities and an easy to use data evaluation pipeline for the analysis of microarray data with a focus on data quality of custom-designed microarrays. The package has been developed for the statistical and bioinformatical analysis of the custom cDNA microarray IronChip, but can be easily adapted for other cDNA or oligonucleotide-based designed microarray platforms. ICEP uses decision tree-based algorithms to assign quality flags and performs robust analysis based on chip design properties regarding multiple repetitions, ratio cut-off, background and negative controls (Vainshtein Y, et al., 2010).},
  subject      = {Microarray},
  language  = {en}
}
@phdthesis{Liang2009,
  author    = {Liang, Chunguang},
  title     = {Tools for functional genomics applied to Staphylococci, Listeriae, Vaccinia virus and other organisms},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-48051},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2009},
  abstract  = {Genome sequence analysis A combination of genome analysis application has been established here during this project. This offers an efficient platform to interactively compare similar genome regions and reveal loci differences. The genes and operons can be rapidly analyzed and local collinear blocks (LCBs) categorized according to their function. The features of interests are parsed, recognized, and clustered into reports. Phylogenetic relationships can be readily examined such as the evolution of critical factors or a certain highly-conserved region. The resulting platform-independent software packages (GENOVA and inGeno), have been proven to be efficient and easy to handle in a number of projects. The capabilities of the software allowed the investigation of virulence factors, e.g., rsbU, strains' biological design, and in particular pathogenicity feature storage and management. We have successfully investigated the genomes of Staphylococcus aureus strains (COL, N315, 8325, RN1HG, Newman), Listeria spp. (welshimeri, innocua and monocytogenes), E.coli strains (O157:H7 and MG1655) and Vaccinia strains (WR, Copenhagen, Lister, LIVP, GLV-1h68 and parental strains). Metabolic network analysis Our YANAsquare package offers a workbench to rapidly establish the metabolic network of such as Staphylococcous aureus bacteria in genome-scale size as well as metabolic networks of interest such as the murine phagosome lipid signalling network. YANAsquare recruits reactions from online databases using an integrated KEGG browser. This reduces the efforts in building large metabolic networks. The involved calculation routines (METATOOL-derived wrapper or native Java implementation) readily obtain all possible flux modes (EM/EP) for metabolite fluxes within the network. Advanced layout algorithms visualize the topological structure of the network. In addition, the generated structure can be dynamically modified in the graphic interface. The generated network as well as the manipulated layout can be validated and stored (XML file: scheme of SBML level-2). This format can be further parsed and analyzed by other systems biology software, such as CellDesigner. Moreover, the integrated robustness-evaluation routine is able to examine the synthesis rates affected by each single mutation throughout the whole network. We have successfully applied the method to simulate single and multiple gene knockouts, and the affected fluxes are comprehensively revealed. Recently we applied the method to proteomic data and extra-cellular metabolite data of Staphylococci, the physiological changes regarding the flux distribution are studied. Calculations at different time points, including different conditions such as hypoxia or stress, show a good fit to experimental data. Moreover, using the proteomic data (enzyme amounts) calculated from 2D-Gel-EP experiments our study provides a way to compare the fluxome and the enzyme expression. Oncolytic vaccinia virus (VACV) We investigated the genetic differences between the de novo sequence of the recombinant oncolytic GLV-1h68 and other related VACVs, including function predictions for all found genome differences. Our phylogenetic analysis indicates that GLV-1h68 is closest to Lister strains but has lost several ORFs present in its parental LIVP strain, including genes encoding CrmE and a viral Golgi anti-apoptotic protein, v-GAAP. Functions of viral genes were either strain-specific, tissue-specific or host-specific comparing viral genes in the Lister, WR and COP strains. This helps to rationally design more optimized oncolytic virus strains to benefit cancer therapy in human patients. Identified differences from the comparison in open reading frames (ORFs) include genes for host-range selection, virulence and immune modulation proteins, e.g. ankyrin-like proteins, serine proteinase inhibitor SPI-2/CrmA, tumor necrosis factor (TNF) receptor homolog CrmC, semaphorin-like and interleukin-1 receptor homolog proteins. The contribution of foreign gene expression cassettes in the therapeutic and oncolytic virus GLV-1h68 was studied, including the F14.5L, J2R and A56R loci. The contribution of F14.5L inactivation to the reduced virulence is demonstrated by comparing the virulence data of GLV-1h68 with its F14.5L-null and revertant viruses. The comparison suggests that insertion of a foreign gene expression cassette in a nonessential locus in the viral genome is a practical way to attenuate VACVs, especially if the nonessential locus itself contains a virulence gene. This reduces the virulence of the virus without compromising too much the replication competency of the virus, the key to its oncolytic activity. The reduced pathogenicity of GLV-1h68 was confirmed by our experimental collaboration partners in male mice bearing C6 rat glioma and in immunocompetent mice bearing B16-F10 murine melanoma. In conclusion, bioinformatics and experimental data show that GLV-1h68 is a promising engineered VACV variant for anticancer therapy with tumor-specific replication, reduced pathogenicity and benign tissue tropism.},
  subject      = {Genanalyse},
  language  = {en}
}
@phdthesis{Blenk2007,
  author    = {Blenk, Steffen},
  title     = {Bioinformatical analysis of B-cell lymphomas},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-27421},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {Background: The frequency of the most observed cancer, Non Hodgkin Lymphoma (NHL), is further rising. Diffuse large B-cell lymphoma (DLBCL) is the most common of the NHLs. There are two subgroups of DLBCL with different gene expression patterns: ABC ("Activated B-like DLBCL") and GCB ("Germinal Center B-like DLBCL"). Without therapy the patients often die within a few months, the ABC type exhibits the more aggressive behaviour. A further B-cell lymphoma is the Mantle cell lymphoma (MCL). It is rare and shows very poor prognosis. There is no cure yet. Methods: In this project these B-cell lymphomas were examined with methods from bioinformatics, to find new characteristics or undiscovered events on the molecular level. This would improve understanding and therapy of lymphomas. For this purpose we used survival, gene expression and comparative genomic hybridization (CGH) data. In some clinical studies, you get large data sets, from which one can reveal yet unknown trends. Results (MCL): The published proliferation signature correlates directly with survival. Exploratory analyses of gene expression and CGH data of MCL samples (n=71) revealed a valid grouping according to the median of the proliferation signature values. The second axis of correspondence analysis distinguishes between good and bad prognosis. Statistical testing (moderate t-test, Wilcoxon rank-sum test) showed differences in the cell cycle and delivered a network of kinases, which are responsible for the difference between good and bad prognosis. A set of seven genes (CENPE, CDC20, HPRT1, CDC2, BIRC5, ASPM, IGF2BP3) predicted, similarly well, survival patterns as proliferation signature with 20 genes. Furthermore, some bands could be associated with prognosis in the explorative analysis (chromosome 9: 9p24, 9p23, 9p22, 9p21, 9q33 and 9q34). Results (DLBCL): New normalization of gene expression data of DLBCL patients revealed better separation of risk groups by the 2002 published signature based predictor. We could achieve, similarly well, a separation with six genes. Exploratory analysis of gene expression data could confirm the subgroups ABC and GCB. We recognized a clear difference in early and late cell cycle stages of cell cycle genes, which can separate ABC and GCB. Classical lymphoma and best separating genes form a network, which can classify and explain the ABC and GCB groups. Together with gene sets which identify ABC and GCB we get a network, which can classify and explain the ABC and GCB groups (ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5; Altogether these findings are useful for diagnosis, prognosis and therapy (cytostatic drugs).},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{Thakar2006,
  author    = {Thakar, Juilee},
  title     = {Computational models for the study of responses to infections},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-17266},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2006},
  abstract  = {In diesem Jahrhundert haben neue experimentelle Techniken und Computer-Verfahren enorme Mengen an Information erzeugt, die bereits viele biologische R{\"a}tsel enth{\"u}llt haben. Doch die Komplexit{\"a}t biologischer Systeme wirft immer weitere neue Fragen auf. Um ein System zu verstehen, bestand der Hauptansatz bis jetzt darin, es in Komponenten zu zerlegen, die untersucht werden k{\"o}nnen. Ein neues Paradigma verkn{\"u}pft die einzelnen Informationsteile, um sie auf globaler Ebene verstehen zu k{\"o}nnen. In der vorgelegten Doktorarbeit habe ich deshalb versucht, infekti{\"o}se Krankheiten mit globalen Methoden („Systembiologie") bioinformatisch zu untersuchen. Im ersten Teil wird der Apoptose-Signalweg analysiert. Apoptose (Programmierter Zelltod) wird bei verschiedenen Infektionen, zum Beispiel bei Viruserkrankungen, als Abwehrmaßnahme eingesetzt. Die Interaktionen zwischen Proteinen, die ‚death' Dom{\"a}nen beinhalten, wurden untersucht, um folgende Fragen zu kl{\"a}ren: i) wie wird die Spezifit{\"a}t der Interaktionen erzielt? -sie wird durch Adapter erreicht, ii) wie werden Proliferation/ {\"U}berlebenssignale w{\"a}hrend der Aktivierung der Apoptose eingeleitet? - wir fanden Hinweise f{\"u}r eine entscheidende Rolle des RIP Proteins (Rezeptor-Interagierende Serine/Threonine-Proteinkinase 1). Das Modell erlaubte uns, die Interaktions-Oberfl{\"a}chen von RIP vorherzusagen. Der Signalweg wurde anschließend auf globaler Ebene mit Simulationen f{\"u}r verschiedene Zeitpunkte analysiert, um die Evolution der Aktivatoren und Inhibitoren des Signalwegs und seine Struktur besser zu verstehen. Weiterhin wird die Signalverarbeitung f{\"u}r Apoptosis-Signalwege in der Maus detailliert modelliert, um den Konzentrationsverlauf der Effektor-Kaspasen vorherzusagen. Weitere experimentelle Messungen von Kaspase-3 und die {\"U}berlebenskurven von Zellen best{\"a}tigen das Modell. Der zweite Teil der Resultate konzentriert sich auf das Phagosom, eine Organelle, die eine entscheidende Rolle bei der Eliminierung von Krankheitserregern spielt. Dies wird am Beispiel von M. tuberculosis veranschaulicht. Die Fragestellung wird wiederum in zwei Aspekten behandelt: i) Um die Prozesse, die durch M. tuberculosis inhibiert werden zu verstehen, haben wir uns auf das Phospholipid-Netzwerk konzentriert, das bei der Unterdr{\"u}ckung oder Aktivierung der Aktin-Polymerisation eine große Rolle spielt. Wir haben f{\"u}r diese Netzwerkanalyse eine Simulation f{\"u}r verschiedene Zeitpunkte {\"a}hnlich wie in Teil eins angewandt. ii) Es wird vermutet, dass Aktin-Polymere bei der Fusion des Phagosoms mit dem Lysosom eine Rolle spielen. Um diese Hypothese zu untersuchen, wurde ein in silico Modell von uns entwickelt. Wir fanden heraus, dass in der Anwesenheit von Aktin-Polymeren die Suchzeit f{\"u}r das Lysosom um das F{\"u}nffache reduziert wurde. Weiterhin wurden die Effekte der L{\"a}nge der Aktin-Polymere, die Gr{\"o}ße der Lysosomen sowie der Phagosomen und etliche andere Modellparameter analysiert. Nach der Untersuchung eines Signalwegs und einer Organelle f{\"u}hrte der n{\"a}chste Schritt zur Untersuchung eines komplexen biologischen Systems der Infektabwehr. Dies wurde am Beispiel der Wirt-Pathogen Interaktion bei Bordetella pertussis und Bordetella bronchiseptica dargestellt. Die geringe Menge verf{\"u}gbarer quantitativer Daten war der ausschlaggebende Faktor bei unserer Modellwahl. F{\"u}r die dynamische Simulation wurde ein selbst entwickeltes Bool'sches Modell verwendet. Die Ergebnisse sagen wichtige Faktoren bei der Pathologie von Bordetellen hervor, besonders die Bedeutung der Th1 assoziierten Antworten und dagegen nicht der Th2 assoziierten Antworten f{\"u}r die Eliminierung des Pathogens. Einige der quantitativen Vorhersagen wurden durch Experimente wie die Untersuchung des Verlaufs einer Infektion in verschiedenen Mutanten und Wildtyp-M{\"a}usen {\"u}berpr{\"u}ft. Die begrenzte Verf{\"u}gbarkeit kinetischer Daten war der kritische Faktor bei der Auswahl der computer-gest{\"u}tzten Modelle. Der Erfolg unserer Modelle konnte durch den Vergleich mit experimentellen Beobachtungen belegt werden. Die vergleichenden Modelle in Kapitel 6 und 9 k{\"o}nnen zur Untersuchung neuer Wirt-Pathogen Interaktionen verwendet werden. Beispielsweise f{\"u}hrt in Kapitel 6 die Analyse von Inhibitoren und inhibitorischer Signalwege aus drei Organismen zur Identifikation wichtiger regulatorischer Zentren in komplexen Organismen und in Kapitel 9 erm{\"o}glicht die Identifikation von drei Phasen in B. bronchiseptica und der Inhibition von IFN-\&\#947; durch den Faktor TTSS die Untersuchung {\"a}hnlicher Phasen und die Inhibition von IFN-\&\#947; in B. pertussis. Eine weitere wichtige Bedeutung bekommen diese Modelle durch die m{\"o}gliche Identifikation neuer, essentieller Komponenten in Wirt-Pathogen Interaktionen. In silico Modelle der Effekte von Deletionen zeigen solche Komponenten auf, die anschließend durch experimentelle Mutationen weiter untersucht werden k{\"o}nnen.},
  subject      = {Bordetella pertussis},
  language  = {en}
}