@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{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{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{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}
}
@article{DandekarLiangKrueger2013,
  author    = {Dandekar, Thomas and Liang, Chunguang and Kr{\"u}ger, Beate},
  title     = {GoSynthetic database tool to analyse natural and engineered molecular processes},
  series = {Database},
  journal   = {Database},
  doi       = {10.1093/database/bat043},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-97023},
  year      = {2013},
  abstract  = {An essential topic for synthetic biologists is to understand the structure and function of biological processes and involved proteins and plan experiments accordingly. Remarkable progress has been made in recent years towards this goal. However, efforts to collect and present all information on processes and functions are still cumbersome. The database tool GoSynthetic provides a new, simple and fast way to analyse biological processes applying a hierarchical database. Four different search modes are implemented. Furthermore, protein interaction data, cross-links to organism-specific databases (17 organisms including six model organisms and their interactions), COG/KOG, GO and IntAct are warehoused. The built in connection to technical and engineering terms enables a simple switching between biological concepts and concepts from engineering, electronics and synthetic biology. The current version of GoSynthetic covers more than one million processes, proteins, COGs and GOs. It is illustrated by various application examples probing process differences and designing modifications.},
  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{Foerstner2008,
  author    = {F{\"o}rstner, Konrad Ulrich},
  title     = {Computational analysis of metagenomic data: delineation of compositional features and screens for desirable enzymes},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-33577},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2008},
  abstract  = {The topic of my doctorial research was the computational analysis of metagenomic data. A metagenome comprises the genomic information from all the microorganisms within a certain environment. The currently available metagenomic data sets cover only parts of these usually huge metagenomes due to the high technical and financial effort of such sequencing endeavors. During my thesis I developed bioinformatic tools and applied them to analyse genomic features of different metagenomic data sets and to search for enzymes of importance for biotechnology or pharmaceutical applications in those sequence collections. In these studies nine metagenomic projects (with up to 41 subsamples) were analysed. These samples originated from diverse environments like farm soil, acid mine drainage, microbial mats on whale bones, marine water, fresh water, water treatment sludges and the human gut flora. Additionally, data sets of conventionally retrieved sequence data were taken into account and compared with each other},
  subject      = {Bioinformatik},
  language  = {en}
}
@phdthesis{Karl2016,
  author    = {Karl, Stefan},
  title     = {Control Centrality in Non-Linear Biological Networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-150838},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Biological systems such as cells or whole organisms are governed by complex regulatory networks of transcription factors, hormones and other regulators which determine the behavior of the system depending on internal and external stimuli. In mathematical models of these networks, genes are represented by interacting "nodes" whose "value" represents the activity of the gene. Control processes in these regulatory networks are challenging to elucidate and quantify. Previous control centrality metrics, which aim to mathematically capture the ability of individual nodes to control biological systems, have been found to suffer from problems regarding biological plausibility. This thesis presents a new approach to control centrality in biological networks. Three types of network control are distinguished: Total control centrality quantifies the impact of gene mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signaling cascades (e.g control in mouse colon stem cells). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Well-defined network manipulations define all three centralities not only for nodes, but also for the interactions between them, enabling detailed insights into network pathways. The calculation of the new metrics is made possible by substantial computational improvements in the simulation algorithms for several widely used mathematical modeling paradigms for genetic regulatory networks, which are implemented in the regulatory network simulation framework Jimena created for this thesis. Applying the new metrics to biological networks and artificial random networks shows how these mathematical concepts correspond to experimentally verified gene functions and signaling pathways in immunity and cell differentiation. In contrast to controversial previous results even from the Barab{\´a}si group, all results indicate that the ability to control biological networks resides in only few driver nodes characterized by a high number of connections to the rest of the network. Autoregulatory loops strongly increase the controllability of the network, i.e. its ability to control itself, and biological networks are characterized by high controllability in conjunction with high robustness against mutations, a combination that can be achieved best in sparsely connected networks with densities (i.e. connections to nodes ratios) around 2.0 - 3.0. The new concepts are thus considerably narrowing the gap between network science and biology and can be used in various areas such as system modeling, plausibility trials and system analyses. Medical applications discussed in this thesis include the search for oncogenes and pharmacological targets, as well their functional characterization.},
  subject      = {Bioinformatik},
  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{Pils2005,
  author    = {Pils, Birgit},
  title     = {Insights into the evolution of protein domains give rise to improvements of function prediction},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-16805},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2005},
  abstract  = {The growing number of uncharacterised sequences in public databases has turned the prediction of protein function into a challenging research field. Traditional annotation methods are often error-prone due to the small subset of proteins with experimentally verified function. Goal of this thesis was to analyse the function and evolution of protein domains in order to understand molecular processes in the cell. The focus was on signalling domains of little understood function, as well as on functional sites of protein domains in general. Glucosaminidases (GlcNAcases) represent key enzymes in signal transduction pathways. Together with glucosamine transferases, they serve as molecular switches, similar to kinases and phosphatases. Little was known about the molecular function and structure of the GlcNAcases. In this thesis, the GlcNAcases were identified as remote homologues of N-acetyltransferases. By comparing the homologous sequences, I was able to predict functional sites of the GlcNAcase family and to identify the GlcNAcases as the first family member of the acetyltransferase superfamily with a distinct catalytic mechanism, which is not involved in the transfer of acetyl groups. In a similar approach, the sensor domain of a plant hormone receptor was studied. I was able to predict putative ligand-binding sites by comparing evolutionary constraints in functionally diverged subfamilies. Most of the putative ligand-binding sites have been experimentally confirmed in the meantime. Due to the importance of enzymes involved in cellular signalling, it seems impossible to find substitutions of catalytic amino acids that turn them catalytically inactive. Nevertheless, by scanning catalytic positions of the protein tyrosine phosphatase families, I found many inactive domains among single domain and tandem domain phosphatases in metazoan proteomes. In addition, I found that inactive phosphatases are conserved throughout evolution, which led to the question about the function of these catalytically inactive phosphatase domains. An analysis of evolutionary site rates of amino acid substitutions revealed a cluster of conserved residues in the apparently redundant domain of tandem phosphatases. This putative regulatory center might be responsible for the experimentally verified dimerization of the active and inactive domain in order to control the catalytic activity of the active phosphatase domain. Moreover, I detected a subgroup of inactive phosphatases, which presumably functions in substrate recognition, based on different evolutionary site rates within the phosphatase family. The characterization of these new regulatory modules in the phosphatase family raised the question whether inactivation of enzymes is a more general evolutionary mechanism to enlarge signalling pathways and whether inactive domains are also found in other enzyme families. A large-scale analysis of substitutions at catalytic positions of enzymatic domains was performed in this work. I identified many domains with inactivating substitutions in various enzyme families. Signalling domains harbour a particular high occurrence of catalytically inactive domains indicating that these domains have evolved to modulate existing regulatory pathways. Furthermore, it was shown that inactivation of enzymes by single substitutions happened multiple times independently in evolution. The surprising variability of amino acids at catalytic positions was decisive for a subsequent analysis of the diversity of functional sites in general. Using functional residues extracted from structural complexes I could show that functional sites of protein domains do not only vary in their type of amino acid but also in their structural location within the domain. In the process of evolution, protein domains have arisen from duplication events and subsequently adapted to new binding partners and developed new functions, which is reflected in the high variability of functional sites. However, great differences exist between domain families. The analysis demonstrated that functional sites of nuclear domains are more conserved than functional sites of extracellular domains. Furthermore, the type of ligand influences the degree of conservation, for example ion binding sites are more conserved than peptide binding sites. The work presented in this thesis has led to the detection of functional sites in various protein domains involved in signalling pathways and it has resulted in insights into the molecular function of those domains. In addition, properties of functional sites of protein domains were revealed. This knowledge can be used in the future to improve the prediction of protein function and to identify functional sites of proteins.},
  subject      = {Dom{\"a}ne <Biochemie>},
  language  = {en}
}