Structural Measures for Network Biology Using QuACN

Background: Structural measures for networks have been extensively developed, but many of them have not yet demonstrated their sustainably. That means, it remains often unclear whether a particular measure is useful and feasible to solve a particular problem in network biology. Exemplarily, the classification of complex biological networks can be named, for which structural measures are used leading to a minimal classification error. Hence, there is a strong need to provide freely available software packages to calculate and demonstrate the appropriate usage of structural graph measures in network biology. Results: Here, we discuss topological network descriptors that are implemented in the R-package QuACN and demonstrate their behavior and characteristics by applying them to a set of example graphs. Moreover, we show a representative application to illustrate their capabilities for classifying biological networks. In particular, we infer gene regulatory networks from microarray data and classify them by methods provided by QuACN. Note that QuACN is the first freely available software written in R containing a large number of structural graph measures. Conclusion: The R package QuACN is under ongoing development and we add promising groups of topological network descriptors continuously. The package can be used to answer intriguing research questions in network biology, e.g., classifying biological data or identifying meaningful biological features, by analyzing the topology o

Chromatic number of graphs with special distance sets, I

Given a subset D of positive integers, an integer distance graph is a graph G(Z, D) with the set Z of integers as vertex set and with an edge joining two vertices u and v if and only if |u−v| ∈ D. In this paper we consider the problem of determining the chromatic number of certain integer distance graphs G(Z, D)whose distance set D is either 1) a set of (n + 1) positive integers for which the nth power of the last is the sum of the nth powers of the previous terms, or 2) a set of pythagorean quadruples, or 3) a set of pythagorean n-tuples, or 4) a set of square distances, or 5) a set of abundant numbers or deficient numbers or carmichael numbers, or 6) a set of polytopic numbers, or 7) a set of happy numbers or lucky numbers, or 8) a set of Lucas numbers, or 9) a set of Ulam numbers, or 10) a set of weird numbers. Besides finding the chromatic number of a few specific distance graphs we also give useful upper and lower bounds for general cases. Further, we raise some open problems

BIOINFORMATICS TOOL AND MODEL DEVELOPMENT FOR STUDYING BIOLOGICAL NETWORKS AND PROTEIN-PROTEIN INTERACTIONS

Ph.DDOCTOR OF PHILOSOPH

NOVEL ALGORITHMS AND TOOLS FOR LIGAND-BASED DRUG DESIGN

Computer-aided drug design (CADD) has become an indispensible component in modern drug discovery projects. The prediction of physicochemical properties and pharmacological properties of candidate compounds effectively increases the probability for drug candidates to pass latter phases of clinic trials. Ligand-based virtual screening exhibits advantages over structure-based drug design, in terms of its wide applicability and high computational efficiency. The established chemical repositories and reported bioassays form a gigantic knowledgebase to derive quantitative structure-activity relationship (QSAR) and structure-property relationship (QSPR). In addition, the rapid advance of machine learning techniques suggests new solutions for data-mining huge compound databases. In this thesis, a novel ligand classification algorithm, Ligand Classifier of Adaptively Boosting Ensemble Decision Stumps (LiCABEDS), was reported for the prediction of diverse categorical pharmacological properties. LiCABEDS was successfully applied to model 5-HT1A ligand functionality, ligand selectivity of cannabinoid receptor subtypes, and blood-brain-barrier (BBB) passage. LiCABEDS was implemented and integrated with graphical user interface, data import/export, automated model training/ prediction, and project management. Besides, a non-linear ligand classifier was proposed, using a novel Topomer kernel function in support vector machine. With the emphasis on green high-performance computing, graphics processing units are alternative platforms for computationally expensive tasks. A novel GPU algorithm was designed and implemented in order to accelerate the calculation of chemical similarities with dense-format molecular fingerprints. Finally, a compound acquisition algorithm was reported to construct structurally diverse screening library in order to enhance hit rates in high-throughput screening

Contrôle de la fabrication des composites par injection sur renforts

RÉSUMÉ: Les procédés de mise en forme des composites par injection sur renforts (LCM) sont de plus en plus utilisés pour fabriquer des composites à haute performance. Lors d’une mise en oeuvre par les procédés LCM, le renfort fibreux sec est tout d'abord drapé à l’intérieur d’un moule. Après la fermeture du moule ou le recouvrement du renfort par une membrane, une résine polymère est injectée ou infusée sous vide à travers le renfort. Les renforts fibreux couramment utilisés dans les procédés LCM possèdent généralement une structure à porosité bimodale: des pores microscopiques existent entre les filaments dans les mèches de fibres, tandis que des pores macroscopiques sont créés entre les mèches suite à la couture ou au tissage du tissu. À l’échelle microscopique, les forces capillaires à l’intérieur des mèches fibreuses jouent un rôle majeur sur la qualité des composites fabriqués par injection de résine. En effet, les forces capillaires régissent l’apparition de défauts d’imprégnation du renfort par emprisonnement d’air. Ces défauts sont critiques car ils ont un impact négatif sur les performances mécaniques des pièces composites au cours de leur vie en service. Toutefois, très peu de données expérimentales sont disponibles sur la formation de ces défauts microscopiques et macroscopiques. De telles données seraient utiles pour les ingénieurs qui veulent accroître l’efficacité, la productivité et la robustesse des procédés d’injection sur renforts. Par conséquent, une approche expérimentale multi-échelle est présentée dans cette thèse afin de mieux comprendre la physique fondamentale des phénomènes d’imprégnation et d’emprisonnement d’air dans les renforts fibreux à porosité bimodale et ainsi proposer des solutions pratiques pour les ingénieurs en contrôle de procédé. Dans un premier temps, un montage est développé pour étudier la saturation des renforts fibreux à l’échelle macroscopique, durant le moulage des composites par transfert de résine (RTM). Ce montage permet de déterminer les paramètres qui gouvernent la qualité de l’imprégnation des renforts fibreux lors de l’étape du remplissage et pendant les stratégies industrielles de post-remplissage (purge du moule et consolidation). Ces paramètres sont identifiés à l’aide de trois plans d’expérience. Il s’agit de la vitesse d’avancée du front de résine, de la pression appliquée aux ports d’injection et du débit d’écoulement de la résine lors de la purge du moule. L’analyse effectuée dans ces plans d’expérience s’appuie sur une procédure ASTM standard de détermination de la teneur en vides dans les pièces composites par carbonisation.----------ABSTRACT: Liquid Composite Molding (LCM) is an increasingly used class of processes to manufacture high performance composites. In LCM, the fibrous reinforcement is first laid in a mold cavity. After closure of the mold or covering of reinforcement with a plastic bag, a polymer resin is either injected or infused under vacuum through the fiber bed. The engineering fabrics commonly used in LCM possess generally dual scale architecture in terms of porosity: microscopic pores exist between the filaments in the fiber tows, while macroscopic pores appear between the tows as a result of the stitching/weaving fabrication process. On a microscopic scale, capillary flows in fiber tows play a major role on the impregnation quality of composites made by resin injection through fibrous reinforcements. Indeed, the capillary forces control the formation of impregnation defects in the fibrous reinforcement as a result of mechanical air entrapment. These defects are critical because they have a negative impact on the mechanical performance of composite parts during their operating life. However, very little experimental data are available in the literature on the formation of these microscopic and macroscopic impregnation defects. Such data would be useful for process engineers that want to increase efficiency, productivity and robustness of LCM processes. Therefore, a multiscale study is presented in this thesis in order to better understand the fundamental physics of impregnation and air entrapment phenomena in dual scale fibrous reinforcements and thus propose practical solutions for process control engineers. First of all, an experimental setup is developed to study the saturation of fibrous reinforcements, at the macroscopic scale, during the Resin Transfer Molding (RTM).This setup is used to determine some key parameters of the part filling step and industrial post-filling strategies (mold bleeding and consolidation) that control the impregnation quality of fibrous reinforcements. These key parameters are identified using three series of experiments. These parameters are the flow front velocity, the inlet mold pressure and the bleeding flow rate. The analyses in these three series of experiments are based on an ASTM standard procedure for void content determination in the composite parts by carbonization (also called loss on ignition (LOI))