De nombreux systèmes biologiques présentent une organisation complexe. Par exemple, les biopolymères peuvent posséder une structure très hiérarchisée responsable de leur fonction particulière. Comprendre la complexité de cette organisation permet de décrire des phénomènes biologiques et de prédire les fonctions des molécules. En outre, en supposant que la structure primaire du polymère est formée aléatoirement, nous pouvons essayer de caractériser ce phénomène par des grandeurs probabilistes (variances, moyennes, etc). Cette formulation est propre aux problèmes d'évolution.Les réseaux biologiques sont d'autres objets communs de la physique statistique possédant de riches propriétés fonctionnelles. Pour décrire un mécanisme biologique, on utilise différents types de réseaux biomoléculaires. Le développement de nouvelles approches peut nous aider à structurer, représenter et interpréter des données expérimentales, comprendre les processus cellulaires et prédire la fonction d'une molécule.L'objectif de cette thèse est de développer des méthodes pour l'étude d'objets statiques ou dynamiques, ayant une architecture complexe. Ici, nous nous intéressons à deux problèmes.La première partie est consacrée à l'analyse statistique des biopolymères aléatoires. Nous étudions une transition de phase présente dans les séquences aléatoires de l'ARN. On met alors en évidence deux modes : le régime où presque toutes les bases qui composent l'ARN sont couplées et la situation où une fraction finie de ces bases restent non complémentaires.La deuxième partie de cette thèse se concentre sur les propriétés statistiques des réseaux. Nous développons des méthodes pour l'identification d'amas de gènes co-expressifs sur les réseaux et la prédiction de gènes régulateurs novateurs. Pour cela, nous utilisons la fonction du plus court chemin et l'analyse du profil des motifs formés par ces amas. Ces méthodes ont pu prédire les facteurs de transcription impliqués dans le processus de longévité. Enfin, nous discutons de la formation de motifs stables sur les réseaux due à une évolution sélective. / Complex organization is found in many biological systems. For example, biopolymers could possess very hierarchic structure, which provides their functional peculiarity. Understating such, complex organization allows describing biological phenomena and predicting molecule functions. Besides, we can try to characterize the specific phenomenon by some probabilistic quantities (variances, means, etc), assuming the primary biopolymer structure to be randomly formed according to some statistical distribution. Such a formulation is oriented toward evolutionary problems.Artificially constructed biological network is another common object of statistical physics with rich functional properties. A behavior of cells is a consequence of complex interactions between its numerous components, such as DNA, RNA, proteins and small molecules. Cells use signaling pathways and regulatory mechanisms to coordinate multiple processes, allowing them to respond and to adapt to changing environment. Recent theoretical advances allow us to describe cellular network structure using graph concepts to reveal the principal organizational features shared with numerous non-biological networks.The aim of this thesis is to develop bunch of methods for studying statistical and dynamic objects of complex architecture and, in particular, scale-free structures, which have no characteristic spatial and/or time scale. For such systems, the use of standard mathematical methods, relying on the average behavior of the whole system, is often incorrect or useless, while a detailed many-body description is almost hopeless because of the combinatorial complexity of the problem. Here we focus on two problems.The first part addresses to statistical analysis of random biopolymers. Apart from the evolutionary context, our studies cover more general problems of planar topology appeared in description of various systems, ranging from gauge theory to biophysics. We investigate analytically and numerically a phase transition of a generic planar matching problem, from the regime, where almost all the vertices are paired, to the situation, where a finite fraction of them remains unmatched.The second part of this work focus on statistical properties of networks. We demonstrate the possibility to define co-expression gene clusters within a network context from their specific motif distribution signatures. We also show how a method based on the shortest path function (SPF) can be applied to gene interactions sub-networks of co-expression gene clusters, to efficiently predict novel regulatory transcription factors (TFs). The biological significance of this method by applying it on groups of genes with a shared regulatory locus, found by genetic genomics, is presented. Finally, we discuss formation of stable patters of motifs in networks under selective evolution in context of creation of islands of "superfamilies".

O processo de auto-organização da estrutura proteica a partir da cadeia de aminoácidos é conhecido como enovelamento. Apesar de conhecermos a estrutura tridimencional de muitas proteínas, para a maioria delas, não possuímos uma compreensão suficiente para descrever em detalhes como a estrutura se organiza a partir da sequência de aminoácidos. É bem conhecido que a formação de núcleos de estruturas locais, conhecida como estrutura secundária, apresenta papel fundamental no enovelamento final da proteína. Desta forma, o desenvolvimento de métodos que permitam não somente predizer a estrutura secundária adotada por um dado resíduo, mas também, a maneira como esse processo deve ocorrer ao longo do tempo é muito relevante em várias áreas da biologia estrutural. Neste trabalho, desenvolvemos dois métodos de predição de estruturas secundárias utilizando modelos com o potencial de fornecer informações mais detalhadas sobre o processo de predição. Um desses modelos foi construído utilizando autômatos celulares, um tipo de modelo dinâmico onde é possível obtermos informações espaciais e temporais. O outro modelo foi desenvolvido utilizando redes neurais residuais profundas. Com este modelo é possível extrair informações espaciais e probabilísticas de suas múltiplas camadas internas de convolução, o que parece refletir, em algum sentido, os estados de formação da estrutura secundária durante o enovelamento. A acurácia da predição obtida por esse modelo foi de ~78% para os resíduos que apresentaram consenso na estrutura atribuída pelos métodos DSSP, STRIDE, KAKSI e PROSS. Tal acurácia, apesar de inferior à obtida pelo PSIPRED, o qual utiliza matrizes PSSM como entrada, é superior à obtida por outros métodos que realizam a predição de estruturas secundárias diretamente a partir da sequência de aminoácidos. / The process of self-organization of the protein structure is known as folding. Although we know the structure of many proteins, for a majority of them, we do not have enough understanding to describe in details how the structure is organized from its amino acid sequence. In this work, we developed two methods for secondary structure prediction using models that have the potential to provide detailed information about the prediction process. One of these models was constructed using cellular automata, a type of dynamic model where it is possible to obtain spatial and temporal information. The other model was developed using deep residual neural networks. With this model it is possible to extract spatial and probabilistic information from its multiple internal layers of convolution. The accuracy of the prediction obtained by this model was ~ 78% for residues that showed consensus in the structure assigned by the DSSP, STRIDE, KAKSI and PROSS methods. Such value is higher than that obtained by other methods which perform the prediction of secondary structures from the amino acid sequence only.

L’utilisation d’oligomères d’α-amino-γ-lactame (Agl-αAA) comme vecteurs de pénétration intracellulaire est décrite dans ce manuscrit. Nous avons montré que ces oligomères structurés en ruban sont capables de traverser la membrane plasmique pour atteindre le cytosol et y délivrer un cargo biologiquement actif. A la différence des séquences peptidiques, ces oligomères ont une très bonne résistance enzymatique. Une nouvelle famille de foldamères d’α-amino-γ-lactame (Agl-βAA) obtenus à partir de séquences /β peptidiques est également décrite. La structure secondaire de ces composés a été étudiée par RMN, IR-TF, CD et DRX. Nous avons montré que ces oligomères sont capables d’adopter une structure stable en hélice 12. De façon remarquable, ces oligomères sont solubles en milieux aqueux malgré une absence totale de chaînes latérales hydrophiles. / The use of α-amino-γ-lactam oligomers (Agl-αAA) as cell penetrating vectors are described in this work. These ribbon structured oligomers are able to cross the cell membrane to reach the cytosol and deliver a biologically active cargo. Unlike peptide sequences, these oligomers display a strong enzymatic resistance. A new family of α-amino-γ-lactam oligomers (Agl-βAA) obtained from conversion of /β peptide sequences are also described. Secondary structure of these molecules have been studied by NMR, FTIR, CD and XRD. These oligomers are able to adopt a stable 12-helix structure. Unexpectedly, these oligomers are soluble in aqueous mediums without any hydrophilic side chains

The informational content of 3' splice sites is low and the mechanisms whereby they are selected are not clear. Here we enunciate a set of rules that govern their selection. For many introns, secondary structures are a key factor, because they occlude alternative 3'ss from the spliceosome and reduce the effective distance between the BS and the 3'ss to a maximum of 45 nucleotides. Further alternative 3'ss are disregarded by the spliceosome because they lie at 9 nucleotides or less from the branch site, or because they are weak splice sites. With these rules, we are able to explain the splicing pattern of the vast majority of introns in Saccharomyces cerevisiae. When in excess, L30 blocks the splicing of its own transcript by interfering with a critical rearrangement that is required for the proper recognition of the intron 3' end, and thus for splicing to proceed. We show that the protein Cbp80 has a role in promoting this rearrangement and therefore antagonizes splicing regulation by L30. / Tanto la información que define el sitio de splicing 3' como los mecanismos de selección del mismo son poco conocidos. En este trabajo, proponemos una serie de reglas que gobiernan esta selección. Las estructuras secundarias son claves en el caso de muchos intrones, porque son capaces de ocultar sitios de splicing alternativos 3' al spliceosoma, y además reducen la distancia efectiva entre el punto de ramificación y el sitio de splicing 3' a un máximo de 45 nucleotidos. Otros sitios de splicing alternativo 3' no son considerados por el spliceosoma como tales porque se encuentran a 9 nucleotidos o menos del punto de ramificación, o porque son sitios de splicing débiles. Con estas reglas somos capaces de explicar el splicing de la mayoría de intrones de Saccharomyces cerevisiae. El exceso de proteína L30 bloquea el splicing de su propio tránscrito porque interfiere con la reorganización necesaria para el correcto reconocimiento del 3' final del intrón, y por tanto de su splicing. Demostramos que la proteína Cbp80 está implicada en promover esta reorganización y que por tanto antagoniza la regulación del splicing por L30.

No description available.

Since the human inclination to estimate and trace natural diversity, usable species definitions as well as taxonomical systems are required. As a consequence, the first proposed classification schemes assigned the filamentous and parenchymatous taxa to the green algal order Chaetophorales sensu Wille. The introduction of ultrastructural and molecular methods provided novel insight into algal evolution and generated taxonomic revisions based on phylogenetic inference. However, until now, the number of molecular phylogenetic studies focusing on the Chaetophorales s.s. is surprisingly low. To enhance knowledge about phylogenetic relationships among taxa within the order, the nuclear?encoded SSU rDNA sequences from 30 strains covering all three chaetophoralean families have been investigated. All revealed monophyletic groupings were further screened for molecular non-homoplasious synapomorphies within the Viridiplantae. To address the question of the correspondence between morphological characters traditionally used for taxonomical delimitation of the Chaetophorales and the tree topology favored by molecular data, the list of morphological/ ultrastructural/ecological characters was elaborated and further analyzed. In addition, to obtain a close-up view into the evolution of Compensatory Base Changes (CBCs) of the second internal transcribed spacer (ITS2) which is currently often used to delimit putative biological species, 86 newly obtained/published sequences of ITS2 for five families of the order Ulvales were analyzed. Furthermore, a detailed comparative study of all ITS2 substitutions has been done. Subsequently all revealed CBCs and hemi- CBCs have been mapped upon the ITS2 phylogenetic tree topology. Finally, CBCs/hCBCs taxonomic inference in the Ulvales has been discussed.

Le VIH-1 bourgeonne sous forme immature et doit subir l’étape de maturation afin d’acquérir son caractère infectieux. La maturation protéolytique du précurseur Pr55Gag induit le réarrangement morphologique de la particule alors que le dimère d’ARNg acquiert une compaction optimale. Ces réarrangements conformationnels restent encore inconnus et sont facilités par l’activité chaperonne de la protéine NCp7. Notre but a été de déterminer les différentes étapes menant à l’obtention d’un dimère d’ARNg mature. Nous avons donc étudié la structure des 550 premiers nucléotides du génome par cartographie chimique, à la fois 1. in vitro en présence des protéines Pr55Gag, GagΔp6, intermédiaires contenant le domaine NC et NCp7 et 2. in viro par l’approche hSHAPE-Seq que nous avons développé. Les particules matures et bloquées aux différentes étapes de maturation de Pr55Gag ont été analysées ainsi que des particules matures et totalement immatures traitées avec l’éjecteur de zinc AT-2. Ce traitement permet d’identifier les sites de protection de Pr55Gag et NCp7 ainsi que leur activité déstabilisatrice. / The HIV-1 particle buds from the infected cell as an immature particle and has to undergo a maturation process to become infectious. Proteolytic processing of Pr55Gag triggers morphological rearrangements of the particle whereas the gRNA dimer becomes more stable. Genomic rearrangements remain poorly understood and are facilitated by the RNA chaperone activity of the NCp7 protein. Our goal was to determining the different steps leading to the formation of the mature dimeric gRNA. To this end, the structure of the first 550 nucleotides of the HIV-1 genome was assessed by chemical probing 1. in vitro with Pr55Gag, GagΔp6, NC-containing intermediates and NCp7 proteins and 2. in viro with the hSHAPE-Seq approach we developed. Wild type and mutant viruses mimicking the sequential processing of Pr55Gag were analysed, as well as immature PR- and mature particles treated with the AT-2 zinc ejector, in order to identify the Pr55Gag and NCp7 binding sites and their gRNA destabilising activity.

O processo de auto-organização da estrutura proteica a partir da cadeia de aminoácidos é conhecido como enovelamento. Apesar de conhecermos a estrutura tridimencional de muitas proteínas, para a maioria delas, não possuímos uma compreensão suficiente para descrever em detalhes como a estrutura se organiza a partir da sequência de aminoácidos. É bem conhecido que a formação de núcleos de estruturas locais, conhecida como estrutura secundária, apresenta papel fundamental no enovelamento final da proteína. Desta forma, o desenvolvimento de métodos que permitam não somente predizer a estrutura secundária adotada por um dado resíduo, mas também, a maneira como esse processo deve ocorrer ao longo do tempo é muito relevante em várias áreas da biologia estrutural. Neste trabalho, desenvolvemos dois métodos de predição de estruturas secundárias utilizando modelos com o potencial de fornecer informações mais detalhadas sobre o processo de predição. Um desses modelos foi construído utilizando autômatos celulares, um tipo de modelo dinâmico onde é possível obtermos informações espaciais e temporais. O outro modelo foi desenvolvido utilizando redes neurais residuais profundas. Com este modelo é possível extrair informações espaciais e probabilísticas de suas múltiplas camadas internas de convolução, o que parece refletir, em algum sentido, os estados de formação da estrutura secundária durante o enovelamento. A acurácia da predição obtida por esse modelo foi de ~78% para os resíduos que apresentaram consenso na estrutura atribuída pelos métodos DSSP, STRIDE, KAKSI e PROSS. Tal acurácia, apesar de inferior à obtida pelo PSIPRED, o qual utiliza matrizes PSSM como entrada, é superior à obtida por outros métodos que realizam a predição de estruturas secundárias diretamente a partir da sequência de aminoácidos. / The process of self-organization of the protein structure is known as folding. Although we know the structure of many proteins, for a majority of them, we do not have enough understanding to describe in details how the structure is organized from its amino acid sequence. In this work, we developed two methods for secondary structure prediction using models that have the potential to provide detailed information about the prediction process. One of these models was constructed using cellular automata, a type of dynamic model where it is possible to obtain spatial and temporal information. The other model was developed using deep residual neural networks. With this model it is possible to extract spatial and probabilistic information from its multiple internal layers of convolution. The accuracy of the prediction obtained by this model was ~ 78% for residues that showed consensus in the structure assigned by the DSSP, STRIDE, KAKSI and PROSS methods. Such value is higher than that obtained by other methods which perform the prediction of secondary structures from the amino acid sequence only.

Background: RNA secondary structure prediction by energy minimization is the central computational tool for the analysis of structural non-coding RNAs and their interactions. Sparsification has been successfully applied to improve the time efficiency of various structure prediction algorithms while guaranteeing the same result; however, for many such folding problems, space efficiency is of even greater concern, particularly for long RNA sequences. So far, spaceefficient sparsified RNA folding with fold reconstruction was solved only for simple base-pair-based pseudo-energy models. Results: Here, we revisit the problem of space-efficient free energy minimization. Whereas the space-efficient minimization of the free energy has been sketched before, the reconstruction of the optimum structure has not even been discussed. We show that this reconstruction is not possible in trivial extension of the method for simple energy models. Then, we present the time- and space-efficient sparsified free energy minimization algorithm SparseMFEFold that guarantees MFE structure prediction. In particular, this novel algorithm provides efficient fold reconstruction based on dynamically garbage-collected trace arrows. The complexity of our algorithm depends on two parameters, the number of candidates Z and the number of trace arrows T; both are bounded by n2, but are typically much smaller. The time complexity of RNA folding is reduced from O(n3) to O(n2 + nZ); the space complexity, from O(n2) to O(n + T + Z). Our empirical results show more than 80 % space savings over RNAfold [Vienna RNA package] on the long RNAs from the RNA STRAND database (≥2500 bases). Conclusions: The presented technique is intentionally generalizable to complex prediction algorithms; due to their high space demands, algorithms like pseudoknot prediction and RNA–RNA-interaction prediction are expected to profit even stronger than \"standard\" MFE folding. SparseMFEFold is free software, available at http://www.bioinf.unileipzig. de/~will/Software/SparseMFEFold.

No description available.