Septinas são proteínas que ligam GTP e interagem entre si formando heterocomplexos, os quais formam filamentos e estruturas de maior nível de organização. Tais filamentos, além de se mostrarem importantes durante a citocinese também podem estar envolvidos em outros processos celulares tais como determinação da polaridade celular e reorganização do citoesqueleto. As septinas, inicialmente descobertas em Saccharomyces cerevisiae, já foram identificadas também em fungos, algas-verdes, mamíferos, porém nunca em plantas. Tipicamente, septinas apresentam três domínios estruturais compostos por um domínio central de ligação ao GTP flanqueado por um N-terminal variável e um C-terminal que pode conter sequências do tipo coiled-coil. Este trabalho teve como objetivo a caracterização do domínio de ligação a GTP da septina 8 humana (SEPT8G) por meio de estudos biofísicos. Nesse sentido, SEPT8G foi eficientemente produzida em E. coli, tendo seu estado dimérico em solução confirmado por cromatografia de exclusão molecular. Diferentemente das outras septinas já reportadas, mesmo dimérica a SEPT8G apresentou-se na forma apo. Ensaios de atividade GTPásica foram realizados, confirmando a incapacidade dessa septina em hidrolisar o GTP. Ainda, análises de estabilidade térmica por Dicroísmo Circular revelaram que a presença do íon magnésio leva à diminuição de sua estabilidade estrutural. Baseando-se em resultados prévios de interação obtidos pela técnica do duplo-híbrido em leveduras, estudos voltados à análise da interação entre as septinas 7 e 8 e, posteriormente, entre as septinas 5, 7 e 8 foram realizados. A co-purificação do complexo formado pelas septinas 7 e 8 mostrou-se dependente da região C-terminal completa de SEPT7 de modo que, quando ausente, a interação mostrou-se expressivamente prejudicada. Já o complexo formado pelas septinas 5/7/8 foi obtido contendo as três proteínas em frações equimolares e solúveis, disponibilizando assim um novo heterocomplexo de septinas para ensaios funcionais e estruturais. / Septins are GTP-binding proteins that interact with each other to form heterocomplexes, which form filaments and higher order structures. These filaments are important for cytokinesis and may be involved in other cellular processes such as the determination of cell polarity and cytoskeleton reorganization. The septins, initially discovered in Saccharomyces cerevisiae, have also been identified in fungi, green algae, mammals but not in plants. Typically, septins have three structural domains comprising a central GTP binding domain flanked by a variable N-terminal and a C-terminal which can contain coiled-coil structures. This work sought to characterize the GTP-binding domain of the human septin 8 (SEPT8G) through biophysical studies. Accordingly, SEPT8G was efficiently produced in E. coli, and its dimeric state in solution was confirmed by size exclusion chromatography. Compared to other septins previously reported, the dimeric SEPT8G presented itself as an apo-protein. GTPase activity assays were performed, confirming the inability of this septin to hidrolyse GTP. Additionally, thermal stability analyses by Circular Dichroism showed that the presence of the magnesium ion leads to a decrease of structural stability. Considering previous results of septins interactions from yeast two-hybrid experiments, we have analyzed the interaction between the septins 7, 8 and subsequently among septins 5, 7 and 8. Co-purification of the complex formed by the septins 7 and 8 showed to be dependent on the complete SEPT7 C-terminal region so that, when absent, the interaction was significantly impaired. The obtained complex formed by septins 5/7/8 contained the three proteins in soluble and equimolar fractions, providing a new septin heterocomplex for functional and structural studies.

Microarrays have been widely used to study differential gene expression at the genomic level. They can also provide genome-wide co-expression information. Robust approaches are needed for integration and validation of independently-collected datasets which may contribute to a common hypothesis. Previously, attempts at meta-analysis have contributed to solutions to this problem. As an alternative, for microarray data from multiple highly similar biological experimental designs, a more direct combined approach is possible. In this thesis, a novel approach is described for microarray combined analysis, including gene-level unification into a virtual platform followed by normalization and a method for ranking candidate genes based on co-expression information - called Coex-Rank. We applied this approach to our Sppar (a PPARγ mutant) dataset, which illustrated an improvement in statistical power and a complementary advantage of the Coex-Rank method from a biological perspective. We also performed analysis to other PPARγ-related microarray datasets. From the perspective of gene sets, we observed that up-regulated genes from mice treated with the PPARγ ligand rosiglitazone were significantly down-regulated in mice with a global knock-in dominant-negative mutation of PPARγ. Integrated with publicly available PPRE (PPAR Response Element) datasets, we found that the genes which were most up-regulated by rosiglitazone treatment and which were also down-regulated by the global knock-in mutation of PPARγ were robustly enriched in PPREs near transcription start sites. In addition, we identified several potential PPARγ targets in the aorta and mesenteric artery for further experimental validation, such as Rhobtb1 and Rgs5.

It is well documented that transcriptionally coordinated genes tend to be functionally related, and that such relationships may be conserved across different species, and even kingdoms. (Ihmels et al., 2004). Such relationships was initially utilized to reveal functional gene modules in yeast and mammals (Ihmels et al., 2004), and to explore orthologous gene functions between different species and kingdoms (Stuart et al., 2003; Bergmann et al., 2004). Model organisms, such as Arabidopsis, are readily used in basic research due to resource availability and relative speed of data acquisition. A major goal is to transfer the acquired knowledge from these model organisms to species that are of greater importance to our society. However, due to large gene families in plants, the identification of functional equivalents of well characterized Arabidopsis genes in other plants is a non-trivial task, which often returns erroneous or inconclusive results. In this thesis, concepts of utilizing co-expression networks to help infer (i) gene function, (ii) organization of biological processes and (iii) knowledge transfer between species are introduced. An often overlooked fact by bioinformaticians is that a bioinformatic method is as useful as its accessibility. Therefore, majority of the work presented in this thesis was directed on developing freely available, user-friendly web-tools accessible for any biologist. / Es ist bereits ausgiebig gezeigt worden, dass Gene, deren Expression auf Transkriptionsebene koordiniert ist, häufig auch funktional in verwandten Stoffwechselwegen vorkommen, und dass sich dies wahrscheinlich auch Spezies- und sogar Reichübergreifend sagen lässt (Ihmels et al., 2004). Anfänglich wurden solche Beziehungen verwendet, um sogenannte Genfunktionsmodule in Hefe und Säugern aufzudecken (Ihmels et al., 2004), um dann orthologe Genfunktionen zwischen verschiedene Spezies und Reichen zu entdecken (Stuart et al., 2003; Bergmann et al., 2004). Modellorganismen wie Arabidopsis werden bevorzugt in der Forschung verwendet, weil man durch die schnelle Generationszeit in kurzer Zeit viele Daten erheben kann und aufgrund dessen die Ressourcen- und Informationsvielfalt um ein Vielfaches größer ist. Ein Hauptziel ist der Wissenstransfer von Modellorganismen auf Spezies, die gesellschaftlich von höherer Bedeutung sind wie z.B. Getreidearten oder andere Feldfrüchte. Pflanzen besitzen oft große Genfamilien und die eindeutige Identifizierung von gut charakterisierten Arabidopsisorthologen in besagten Nutzpflanzen ist kein triviales Vorhaben. In der vorliegenden Arbeit werden Konzepte zur Nutzung von Co-expressionsnetzwerken beschrieben, die helfen sollen (i) Genfunktionen zu identifizieren, (ii) die Organisation von biologischen Prozessen aufzuklären und (iii) das erworbene Wissen auf andere Spezies übertragbar zu machen. Ein häufig von Bioinformatikern übersehender Umstand ist, dass bioinformatische Methoden nur so sinnvoll sind wie ihre Zugänglichkeit. Deshalb basiert der Großteil dieser Arbeit auf freiverfügbaren und vor allem für Biologen nutzerfreundlichen Webtools.

Septinas são proteínas que ligam GTP e interagem entre si formando heterocomplexos, os quais formam filamentos e estruturas de maior nível de organização. Tais filamentos, além de se mostrarem importantes durante a citocinese também podem estar envolvidos em outros processos celulares tais como determinação da polaridade celular e reorganização do citoesqueleto. As septinas, inicialmente descobertas em Saccharomyces cerevisiae, já foram identificadas também em fungos, algas-verdes, mamíferos, porém nunca em plantas. Tipicamente, septinas apresentam três domínios estruturais compostos por um domínio central de ligação ao GTP flanqueado por um N-terminal variável e um C-terminal que pode conter sequências do tipo coiled-coil. Este trabalho teve como objetivo a caracterização do domínio de ligação a GTP da septina 8 humana (SEPT8G) por meio de estudos biofísicos. Nesse sentido, SEPT8G foi eficientemente produzida em E. coli, tendo seu estado dimérico em solução confirmado por cromatografia de exclusão molecular. Diferentemente das outras septinas já reportadas, mesmo dimérica a SEPT8G apresentou-se na forma apo. Ensaios de atividade GTPásica foram realizados, confirmando a incapacidade dessa septina em hidrolisar o GTP. Ainda, análises de estabilidade térmica por Dicroísmo Circular revelaram que a presença do íon magnésio leva à diminuição de sua estabilidade estrutural. Baseando-se em resultados prévios de interação obtidos pela técnica do duplo-híbrido em leveduras, estudos voltados à análise da interação entre as septinas 7 e 8 e, posteriormente, entre as septinas 5, 7 e 8 foram realizados. A co-purificação do complexo formado pelas septinas 7 e 8 mostrou-se dependente da região C-terminal completa de SEPT7 de modo que, quando ausente, a interação mostrou-se expressivamente prejudicada. Já o complexo formado pelas septinas 5/7/8 foi obtido contendo as três proteínas em frações equimolares e solúveis, disponibilizando assim um novo heterocomplexo de septinas para ensaios funcionais e estruturais. / Septins are GTP-binding proteins that interact with each other to form heterocomplexes, which form filaments and higher order structures. These filaments are important for cytokinesis and may be involved in other cellular processes such as the determination of cell polarity and cytoskeleton reorganization. The septins, initially discovered in Saccharomyces cerevisiae, have also been identified in fungi, green algae, mammals but not in plants. Typically, septins have three structural domains comprising a central GTP binding domain flanked by a variable N-terminal and a C-terminal which can contain coiled-coil structures. This work sought to characterize the GTP-binding domain of the human septin 8 (SEPT8G) through biophysical studies. Accordingly, SEPT8G was efficiently produced in E. coli, and its dimeric state in solution was confirmed by size exclusion chromatography. Compared to other septins previously reported, the dimeric SEPT8G presented itself as an apo-protein. GTPase activity assays were performed, confirming the inability of this septin to hidrolyse GTP. Additionally, thermal stability analyses by Circular Dichroism showed that the presence of the magnesium ion leads to a decrease of structural stability. Considering previous results of septins interactions from yeast two-hybrid experiments, we have analyzed the interaction between the septins 7, 8 and subsequently among septins 5, 7 and 8. Co-purification of the complex formed by the septins 7 and 8 showed to be dependent on the complete SEPT7 C-terminal region so that, when absent, the interaction was significantly impaired. The obtained complex formed by septins 5/7/8 contained the three proteins in soluble and equimolar fractions, providing a new septin heterocomplex for functional and structural studies.

No description available.

De nombreuses fonctions cellulaires essentielles telles que la traduction, l'épissage, la biogenèse des ribosomes et la réplication des télomères font appels aux particules RNP non codantes. La biogenèse de ces dernières chez les eucaryotes est un processus très complexes qui fait intervenir de nombreux facteurs cellulaires. La biogenèse du ribosome nécessite au moins 150 facteurs. Ceux-ci sont importants pour faciliter mais également contrôler la biogenèse de cette machinerie cellulaire essentielle qu'est le ribosome. Parmi ces facteurs, nous avons les snoRNP à boîtes C/D. Ces RNP sont impliqués dans la maturation des pré-ARNr (méthylation post-transcriptionnelle des riboses et clivages endonucléolytiques). Récemment notre laboratoire a participé à la découverte de facteurs d'assemblage de ces RNP. Il s'agit entre autre des protéines Rsa1p, du complexe R2TP (Rvb1p, Rvb2p, Tah1p et Pih1p) et de Hit1p chez la levure Saccharomyces cerevisiae. En utilisant une approche de co-expression à haut débit, nos travaux ont révélé un réseau complexe d'interactions entre les protéines constitutives des snoRNP et leurs facteurs d'assemblage. Couplé à une stratégie de protéolyse ménagée, la co-expression nous a permis d'obtenir des sous-complexes protéiques Snu13p/Rsa1p et Rsa1p/Hit1p qui font actuellement l'objet d'une étude structurale par RMN. En collaboration avec l'équipe de F. Allain (ETH Zurich), nous avons également déterminé la structure tridimensionnelle de la protéine Tah1p, ainsi que de son complexe avec le peptide C-terminale de la protéine chaperonne Hsp90 à haute résolution. Ces travaux ont révélé un mode d'association particulier entre le domaine TPR de la protéine et le peptide / A lot of essential cellular functions like translation, splicing, ribosome biogenesis and telomere replication need the activity of non coding RNPs. The biogenesis of non coding RNPs in eukaryotes is a complex pathway involving numerous cellular factors. For instance, ribosome biogenesis requires more than 150 factors. They are important to facilitate and to control the biogenesis of this essential cellular machinery. These factors include the C/D box snoRNPs. These RNPs are involved in pre-rRNA maturation (post-transcriptional ribose methylation and endo-nucleolytic cleavages). Recently, our laboratory participated to the discovery of snoRNP assembly factors: the Rsa1p protein, R2TP complex (Rvb1p, Rvb2p, Tah1p and Pih1p) and Hit1p in the yeast Saccharomyces cerevisiae. Using a high throughput co-expression approach, we deciphered a network of interactions between RNP core proteins and the assembly factors. Coupled with a limited proteolysis strategy, the co-expression method allowed us to obtain proteins sub-complexes Snu13p/Rsa1p and Rsa1p/Hit1p which are currently studied by NMR. In collaboration with the F. Allain team (ETH Zurich), we also determined the tridimensional structure of protein Tah1p and its complex with the chaperon Hsp90 C-terminal peptide at high resolution. The data obtained reveal a particular mode of association of the Tah1p TPR domain with the Hsp90 peptide

Résumé en français confidentiel / Résumé en anglais confidentiel

Septinas compreendem uma conservada família de proteínas de ligação a nucleotídeo de guanina, capazes de formar filamentos. No entanto, apesar de sua importância em vários eventos celulares, ainda há pouca informação disponível no que diz respeita aos detalhes de suas funções moleculares e o mecanismo que conduz a formação de hetero-oligômeros. O objetivo desta tese foi a clonagem, co-expressão e cristalização de septinas e seus complexos (SEPT9-SEPT11-SEPT7; SEPT2-SEPT6-SEPT7-SEPT9; SEPT4-SEPT6-SEPT7 e SEPT4- SEPT8-SEPT7) seguidas por análise estrutural comparativa. Os cDNAs que codificam para as proteínas SEPT9, SEPT6, SEPT2, SEPT11, SEPT8, SEPT4, SEPT9GCα0 (resíduos 262-568) e SEPT9GC (resíduos 279-568) foram clonadas com sucesso em vetores de transferências ou de expressão. O complexo que foi melhor caracterizado, foi o composto por SEPT2-SEPT6-SEPT7-SEPT9GCα0, o qual foi confirmado por LC-MS/MS após digestão tríptica, revelando a produção in vitro de um complexo de septina tetramérico. Além disso, caracterizou-se o hetero dímero formado por SEPT7NGc-SEPT9GC. No entanto, as proteínas que foram mais plenamente investigadas foram as construções SEPT9GCα0 e SEPT9GC que foram estudados individualmente para caracterizações biofísicos e estruturais. SEPT9GCα0 apresentou-se bastante instável, porém, SEPT9GC foi eficientemente produzida e caracterizada. O estado oligomérico da proteína (monômero) foi confirmado por cromatografia de exclusão molecular. A proteína foi produzida na forma apo, e foi incapaz de hidrolisar GTP. A finidade de SEPT9GC pelos nucleotídeos GDP e GTPγS, foi avaliada por Calorimetria de Titulação Isotérmica -ITC, revelando que SEPT9GC possui uma maior afinidade por GTPγS, com Kd de 25,9 µM, o qual é dependente do íon Mg2+. Foram realizados estudos de cristalização dessa proteína, que resultou em cristais de alta resolução, com a proteína complexada a GDP e GTPγS. Interessantemente, a estrutura do complexo de SEPT9GC com o nucleotídeo GTPγS foi obtido por soaking de um cristal previamente obtido complexado com GDP. Este é o primeiro relatório da aplicação deste método para septinas. Finalmente, obtivemos três conjuntos de dados cristalográficos, dois para SEPT9GC-GDP, com resolução de 2.8 Å e 2.1Å e um conjunto de dados de SEPT9GC-GTPγS obtido a 2.8 Å. Dentre as principais características observadas na estrutura de SEPT9GC, tem-se a interface NC responsável pela polimerização dos filamentos, a qual se mostrou diferente dependendo do tipo de nucleotídeo ligado, com encurtamento do filamento na presença de GTPγS. Isso indica a comunicação entre as interfaces consecutivas G e NC ao longo do filamento. A mudança conformacional na interface envolve o rearranjo dos resíduos que são altamente conservados em todas as septinas, bem como alguns que são específicos do grupo de SEPT9 e podem ter implicações para a montagem de filamentos e de associação da membrana. / Septins belong to a highly conserved family of guanine nucleotide binding proteins, capable of forming filaments. Despite their importance for several critical cellular events including cell division, little information is available about their molecular functions and the mechanism which leads to the formation of hetero-oligomers. The aim of this thesis was the cloning, co-expression and crystallization of septins and their complexes (SEPT9-SEPT11-SEPT7; SEPT2-SEPT6-SEPT7-SEPT9; SEPT4-SEPT6-SEPT7 and SEPT4-SEPT8-SEPT7), followed by comparative structural analysis. The cDNAs coding for the proteins SEPT9, SEPT6, SEPT2, SEPT11, SEPT8, SEPT4, SEPT9GCα0 (residues 262-568) and SEPT9GC (residues 279-568) were successfully cloned into transferable or expression vectors. The best characterized complex was that composed of SEPT2-SEPT6-SEPT7-SEPT9GCα0, which was confirmed by LC-MS/MS analysis after triptic digestion, unveiling the in vitro production of a tetrameric septin complex. Additionally, we characterized the hetero-dimer formed by SEPT7NGc-SEPT9GC. However, the most fully investigated proteins were the constructs SEPT9GCα0 and SEPT9GC which were studied individually for biophysical and structural characterizations. SEPT9GCα0 was highly unstable contrasting with SEPT9GC that was efficiently produced and characterized. The presence of the oligomeric state of SEPT9GC (monomer) was confirmed by molecular exclusion chromatography. The protein was produced in the apo form and was capable of hydrolyzing GTP. The affinity of SEPT9GC for GDP and GTPγS nucleotides was evaluated by Isothermal Titration Calorimetry (ITC) revealing that SEPT9GC has a higher affinity for GTPγS, with a Kd of 25.9 µM, which is dependent on the presence of Mg2+. Crystallization studies of this protein were performed, obtaining high quality crystals of protein complexes with GDP and GTPγS. Interestingly, the structure of the complex of SEPT9GC with the nucleotide GTPγS was obtained by soaking a previously grown crystal of the GDP complex. This is the first report of the application of this method for septins. Finally, we have obtained three sets of crystallographic data, two for SEPT9GC-GDP, at resolutions of 2.8 Å and 2.1 Å, and one set of data for SEPT9GC-GTPγS at 2.8 Å. Among the main characteristics observed in the SEPT9GC structure is the NC interface responsible for filament polymerization, which is shown to vary according to the type of bound nucleotide, with foreshortening of the filament in the presence of GTPγS. This indicates communication between consecutive G and NC interfaces along the filament. The conformational change at the interface involves the rearrangement of residues which are highly conserved in all septins as well as several which are SEPT9 group specific and may have implications for filament assembly and membrane association.

Le but de mon projet est de créer et d’appliquer des outils pour l’analyse de la biologie des systèmes musculaires en utilisant différentes données OMICS. Ce projet s’intéresse plus particulièrement à la dysferlinopathie due la déficience d’une protéine appelée dysferline qui est exprimée principalement dans les muscles squelettiques et cardiaque. La perte du dysferline due à la mutation (autosomique-récessive) du gène DYSF entraîne une dystrophie musculaire progressive (LGMD2B, MM, DMAT). Nous avons déjà développé des outils bio-informatiques qui peuvent être utilisés pour l’analyse fonctionnelle de données OMICS, relative à la dyspherlinopathie. Ces derniers incluent le test dit «gene set enrichment analysis», test comparant les profils OMICS d’intérêts aux données OMICS musculaires préalablement publiées ; et l’analyse des réseaux impliquant les diffèrent(e)s protéines et transcrits entre eux/elles. Ainsi, nous avons analysé des centaines de données omiques publiées provenant d’archives publiques. Les outils informatiques que nous avons développés sont CellWhere et MyoMiner. CellWhere est un outil facile à utiliser, permettant de visualiser sur un graphe interactif à la fois les interactions protéine-protéine et la localisation subcellulaire des protéines. Myominer est une base de données spécialisée dans le tissu et les cellules musculaires, et qui fournit une analyse de co-expression, aussi bien dans les tissus sains que pathologiques. Ces outils seront utilisés dans l'analyse et l'interprétation de données transcriptomiques pour les dyspherlinopathies mais également les autres pathologies neuromusculaires. / The aim of this project was to build and apply tools for the analysis of muscle omics data, with a focus on Dysferlin deficiency. This protein is expressed mainly in skeletal and cardiac muscles, and its loss due to mutation (autosomal-recessive) of the DYSF gene, results in a progressive muscular dystrophy (Limb Girdle Muscular Dystrophy type 2B (LGMD2B), Miyoshi myopathy and distal myopathy with tibialis anterior onset (DMAT)). We have developed various tools and pipelines that can be applied towards a bioinformatics functional analysis of omics data in muscular dystrophies and neuromuscular disorders. These include: tests for enrichment of gene sets derived from previously published muscle microarray data and networking analysis of functional associations between altered transcripts/proteins. To accomplish this, we analyzed hundreds of published omics data from public repositories. The tools we developed are called CellWhere and MyoMiner. CellWhere is a user-friendly tool that combines protein-protein interactions and protein subcellular localizations on an interactive graphical display (https://cellwhere-myo.rhcloud.com). MyoMiner is a muscle cell- and tissue-specific database that provides co-expression analyses in both normal and pathological tissues. Many gene co-expression databases already exist and are used broadly by researchers, but MyoMiner is the first muscle-specific tool of its kind (https://myominer-myo.rhcloud.com). These tools will be used in the analysis and interpretation of transcriptomics data from dysferlinopathic muscle and other neuromuscular conditions and will be important to understand the molecular mechanisms underlying these pathologies.

Mathematical modeling of biological phenomena has experienced increasing interest since new high-throughput technologies give access to growing amounts of molecular data. These modeling approaches are especially able to test hypotheses which are not yet experimentally accessible or guide an experimental setup. One particular attempt investigates the evolutionary dynamics responsible for today's composition of organisms. Computer simulations either propose an evolutionary mechanism and thus reproduce a recent finding or rebuild an evolutionary process in order to learn about its mechanism. The quest for evolutionary fingerprints in metabolic and gene-coexpression networks is the central topic of this cumulative thesis based on four published articles. An understanding of the actual origin of life will probably remain an insoluble problem. However, one can argue that after a first simple metabolism has evolved, the further evolution of metabolism occurred in parallel with the evolution of the sequences of the catalyzing enzymes. Indications of such a coevolution can be found when correlating the change in sequence between two enzymes with their distance on the metabolic network which is obtained from the KEGG database. We observe that there exists a small but significant correlation primarily on nearest neighbors. This indicates that enzymes catalyzing subsequent reactions tend to be descended from the same precursor. Since this correlation is relatively small one can at least assume that, if new enzymes are no "genetic children" of the previous enzymes, they certainly be descended from any of the already existing ones. Following this hypothesis, we introduce a model of enzyme-pathway coevolution. By iteratively adding enzymes, this model explores the metabolic network in a manner similar to diffusion. With implementation of an Gillespie-like algorithm we are able to introduce a tunable parameter that controls the weight of sequence similarity when choosing a new enzyme. Furthermore, this method also defines a time difference between successive evolutionary innovations in terms of a new enzyme. Overall, these simulations generate putative time-courses of the evolutionary walk on the metabolic network. By a time-series analysis, we find that the acquisition of new enzymes appears in bursts which are pronounced when the influence of the sequence similarity is higher. This behavior strongly resembles punctuated equilibrium which denotes the observation that new species tend to appear in bursts as well rather than in a gradual manner. Thus, our model helps to establish a better understanding of punctuated equilibrium giving a potential description at molecular level. From the time-courses we also extract a tentative order of new enzymes, metabolites, and even organisms. The consistence of this order with previous findings provides evidence for the validity of our approach. While the sequence of a gene is actually subject to mutations, its expression profile might also indirectly change through the evolutionary events in the cellular interplay. Gene coexpression data is simply accessible by microarray experiments and commonly illustrated using coexpression networks where genes are nodes and get linked once they show a significant coexpression. Since the large number of genes makes an illustration of the entire coexpression network difficult, clustering helps to show the network on a metalevel. Various clustering techniques already exist. However, we introduce a novel one which maintains control of the cluster sizes and thus assures proper visual inspection. An application of the method on Arabidopsis thaliana reveals that genes causing a severe phenotype often show a functional uniqueness in their network vicinity. This leads to 20 genes of so far unknown phenotype which are however suggested to be essential for plant growth. Of these, six indeed provoke such a severe phenotype, shown by mutant analysis. By an inspection of the degree distribution of the A.thaliana coexpression network, we identified two characteristics. The distribution deviates from the frequently observed power-law by a sharp truncation which follows after an over-representation of highly connected nodes. For a better understanding, we developed an evolutionary model which mimics the growth of a coexpression network by gene duplication which underlies a strong selection criterion, and slight mutational changes in the expression profile. Despite the simplicity of our assumption, we can reproduce the observed properties in A.thaliana as well as in E.coli and S.cerevisiae. The over-representation of high-degree nodes could be identified with mutually well connected genes of similar functional families: zinc fingers (PF00096), flagella, and ribosomes respectively. In conclusion, these four manuscripts demonstrate the usefulness of mathematical models and statistical tools as a source of new biological insight. While the clustering approach of gene coexpression data leads to the phenotypic characterization of so far unknown genes and thus supports genome annotation, our model approaches offer explanations for observed properties of the coexpression network and furthermore substantiate punctuated equilibrium as an evolutionary process by a deeper understanding of an underlying molecular mechanism. / Die biologische Zelle ist ein sehr kompliziertes Gebilde. Bei ihrer Betrachtung gilt es, das Zusammenspiel von Tausenden bis Millionen von Genen, Regulatoren, Proteinen oder Molekülen zu beschreiben und zu verstehen. Durch enorme Verbesserungen experimenteller Messgeräte gelingt es mittlerweile allerdings in geringer Zeit enorme Datenmengen zu messen, seien dies z.B. die Entschlüsselung eines Genoms oder die Konzentrationen der Moleküle in einer Zelle. Die Systembiologie nimmt sich dem Problem an, aus diesem Datenmeer ein quantitatives Verständnis für die Gesamtheit der Wechselwirkungen in der Zelle zu entwickeln. Dabei stellt die mathematische Modellierung und computergestützte Analyse ein eminent wichtiges Werkzeug dar, lassen sich doch am Computer in kurzer Zeit eine Vielzahl von Fällen testen und daraus Hypothesen generieren, die experimentell verifiziert werden können. Diese Doktorarbeit beschäftigt sich damit, wie durch mathematische Modellierung Rückschlüsse auf die Evolution und deren Mechanismen geschlossen werden können. Dabei besteht die Arbeit aus zwei Teilen. Zum Einen wurde ein Modell entwickelt, dass die Evolution des Stoffwechsels nachbaut. Der zweite Teil beschäftigt sich mit der Analyse von Genexpressionsdaten, d.h. der Stärke mit der ein bestimmtes Gen in ein Protein umgewandelt, "exprimiert", wird. Der Stoffwechsel bezeichnet die Gesamtheit der chemischen Vorgänge in einem Organismus; zum Einen werden Nahrungsstoffe für den Organismus verwertbar zerlegt, zum Anderen aber auch neue Stoffe aufgebaut. Da für nahezu jede chemische Reaktion ein katalysierendes Enzym benötigt wird, ist davon auszugehen, dass sich der Stoffwechsel parallel zu den Enzymen entwickelt hat. Auf dieser Annahme basiert das entwickelte Modell zur Enzyme-Stoffwechsel-Koevolution. Von einer Anfangsmenge von Enzymen und Molekülen ausgehend, die etwa in einer primitiven Atmosphäre vorgekommen sind, werden sukzessive Enzyme und die nun katalysierbaren Reaktionen hinzugefügt, wodurch die Stoffwechselkapazität anwächst. Die Auswahl eines neuen Enzyms geschieht dabei in Abhängigkeit von der Ähnlichkeit mit bereits vorhandenen und ist so an den evolutionären Vorgang der Mutation angelehnt: je ähnlicher ein neues Enzym zu den vorhandenen ist, desto schneller kann es hinzugefügt werden. Dieser Vorgang wird wiederholt, bis der Stoffwechsel die heutige Form angenommen hat. Interessant ist vor allem der zeitliche Verlauf dieser Evolution, der mittels einer Zeitreihenanalyse untersucht wird. Dabei zeigt sich, dass neue Enzyme gebündelt in Gruppen kurzer Zeitfolge auftreten, gefolgt von Intervallen relativer Stille. Dasselbe Phänomen kennt man von der Evolution neuer Arten, die ebenfalls gebündelt auftreten, und wird Punktualismus genannt. Diese Arbeit liefert somit ein besseres Verständnis dieses Phänomens durch eine Beschreibung auf molekularer Ebene. Im zweiten Projekt werden Genexpressionsdaten von Pflanzen analysiert. Einerseits geschieht dies mit einem eigens entwickelten Cluster-Algorithmus. Hier läßt sich beobachten, dass Gene mit einer ähnlichen Funktion oft auch ein ähnliches Expressionsmuster aufweisen. Das Clustering liefert einige Genkandidaten, deren Funktion bisher unbekannt war, von denen aber nun vermutet werden konnte, dass sie enorm wichtig für das Wachstum der Pflanze sind. Durch Experimente von Pflanzen mit und ohne diese Gene zeigte sich, dass sechs neuen Genen dieses essentielle Erscheinungsbild zugeordnet werden kann. Weiterhin wurden Netzwerke der Genexpressionsdaten einer Pflanze, eines Pilzes und eines Bakteriums untersucht. In diesen Netzwerken werden zwei Gene verbunden, falls sie ein sehr ähnliches Expressionsprofil aufweisen. Nun zeigten diese Netzwerke sehr ähnliche und charakteristische Eigenschaften auf. Im Rahmen dieser Arbeit wurde daher ein weiteres evolutionäres Modell entwickelt, das die Expressionsprofile anhand von Duplikation, Mutation und Selektion beschreibt. Obwohl das Modell auf sehr simplen Eigenschaften beruht, spiegelt es die beobachteten Eigenschaften sehr gut wider, und es läßt sich der Schluss ziehen, dass diese als Resultat der Evolution betrachtet werden können. Die Ergebnisse dieser Arbeiten sind als Doktorarbeit in kumulativer Form bestehend aus vier veröffentlichten Artikeln vereinigt.