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Algorithmes pour l'étude de la structure secondaire des ARN et l'alignement de séquences / Algorithms for the study of RNA secondary structure and sequence alignmentLou, Feng 30 January 2012 (has links)
Ces travaux de thèse concernent la conception et l'étude d'algorithmes, d'une part pourprédire les quantités thermodynamiques et la structure secondaire des ARN, d'autre part pour l'alignement de séquences.Dans une première partie, nous appliquons un algorithme de Monte-Carlo pour approximer la densité d'états d'énergie des structures secondaires d'une séquence d'ARN, ou d'une hybridation de deux molécules d'ARN données. Nous montrons d'abord que la densité estimée par notre programme est aussi bonne que la densité exacte, et le temps d'exécution de notre programme est beaucoup plus rapide. Nous calculons ensuite la température de dénaturation d'une hybridation de deux molécules d'ARN. Nous montrons que nos températures de dénaturation sont plus proches des valeurs expérimentales que celles des deux autres programmes existants.Puis, dans une deuxième partie, nous implémentons un algorithme de programmation dynamique qui engendre des structures sous-optimales, dédié principalement à la prédiction des deux structures fonctionnelles des riboswitchs. Nous appliquons d'abord notre programme sur un riboswitch TPP dans lequel nous avons réussi à détecter les deux structures fonctionnelles. Nous montrons ensuite que les structures prédites par notre programme sont plus proches de la structure réelle par rapport aux cinq autres programmes existants, sur un échantillon de riboswitch purine.Enfin, dans une troisième partie, nous présentons un algorithme de recherche des alignements sous-optimaux de séquences pour améliorer la qualité d'alignement des séquences. Nous comparons d'abord nos alignement à ceux produits par l'algorithme de Needdleman-Wunsch. Nous prédissons plus d'alignements de référrence par rapport l'algorithme de Needdleman-Wunsch. Nous calculons ensuite les fréquences des paires de bases alignées et les entropies de position spécifique dans nos alignements sous-optimaux. Nous montrons que les entropies calculées à partir de notre programme sont plus corrélées que celles des autres programmes avec les positions des paires de résidus fiablement alignées selon BAliBASE. / This thesis concerns the design and study of algorithms, on the one hand to predict the thermodynamic quantities and the secondary structure of RNA, the other for sequence alignment.In the first part, we apply a Monte Carlo algorithm to approximate the density of states for secondary structures of a given RNA sequence, and for hybridizations of two RNA sequences. We first show that the density estimated by our program is as good as the exact density, and the execution time of our program is much faster. We then calculate the melting temperature for a hybridization of two RNA sequences. We show that our melting temperatures are closer to experimental values than the other two programs.Then in the second part, we implement a dynamic programming algorithm that generates sub-optimal structures, mainly dedicated to the prediction of functional structures of riboswitchs. We first apply our program on a TPP riboswitch in which we were able to detect both functional structures. We then show that the structures predicted by our program are closer to the real structure compared to five other existing programs, on a sample of purine riboswitch.Finally, in the third part, we present a novel algorithm to produce sub-optimal pairwise alignments. We first compare our alignments to those produced by the algorithm of Needdleman-Wunsch. We predict more reference alignments than the algorithm of Needdleman-Wunsch. We then calculate the frequencies and position-specific entropies from our sub-optimal alignments. We show that entropies calculated from our program are more correlated than other programs with locations in the core block of BAliBASE reference alignments.
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Extended Entropy Maximisation and Queueing Systems with Heavy-Tailed DistributionsMohamed, Ismail A.M. January 2022 (has links)
Numerous studies on Queueing systems, such as Internet traffic flows, have shown to be bursty, self-similar and/or long-range dependent, because of the heavy (long) tails for the various distributions of interest, including intermittent intervals and queue lengths. Other studies have addressed vacation in no-customers’ queueing system or when the server fails. These patterns are important for capacity planning, performance prediction, and optimization of networks and have a negative impact on their effective functioning. Heavy-tailed distributions have been commonly used by telecommunication engineers to create workloads for simulation studies, which, regrettably, may show peculiar queueing characteristics. To cost-effectively examine the impacts of different network patterns on heavy- tailed queues, new and reliable analytical approaches need to be developed. It is decided to establish a brand-new analytical framework based on optimizing entropy functionals, such as those of Shannon, Rényi, Tsallis, and others that have been suggested within statistical physics and information theory, subject to suitable linear and non-linear system constraints. In both discrete and continuous time domains, new heavy tail analytic performance distributions will be developed, with a focus on those exhibiting the power law behaviour seen in many Internet scenarios.
The exposition of two major revolutionary approaches, namely the unification of information geometry and classical queueing systems and unifying information length theory with transient queueing systems. After conclusions, open problems arising from this thesis and limitations are introduced as future work.
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