La génomique comparative est un sujet essentiel pour éclaircir la biologie évolutionnaire. La première étape pour dépasser une connaissance seulement descriptive est de développer une méthode pour représenter le contenu du génome. Nous avons choisi la représentation modulaire des génomes pour étudier les lois quantitatives qui réglementent leur composition en unités élémentaires de type fonctionnel ou évolutif. La première partie de la thèse se fonde sur l'observation que le nombre de domaines ayant la même fonction est lié à la taille du génome par une loi de puissance. Puisque les catégories fonctionnelles sont des agrégats de familles de domaines, on se demande comment le nombre de domaines dans la même catégorie fonctionnelle est lié à l'évolution des familles. Le résultat est que les familles suivent également une loi de puissance. Le deuxième partie présente un modèle positif qui construit une réalisation à partir des composants liés dans un réseau de dépendance. L'ensemble de toutes les réalisations reproduit la distribution des composants partagés et la relation entre le nombre de familles distinctes et la taille du génome. Le dernier chapitre étend l'approche modulaire aux écosystèmes microbiens. Sur la base des constatations que nous avons faites sur les lois de puissance pour les familles de domaines, nous avons analysé comment le nombre de familles dans un metagénome en est influencé. Par conséquence, nous avons défini une nouvelle observable dont la forme fonctionnelle comprend des informations quantitatives sur la composition originelle du metagénome. / Comparative genomics is as a fundamental discipline to unravel evolutionary biology. To overcome a mere descriptive knowledge of it the first challenge is to develop a higher-level description of the content of a genome. Therefore we used the modular representation of genomes to explore quantitative laws that regulate how genomes are built from elementary functional and evolutionary ingredients. The first part sets off from the observation that the number of domains sharing the same function increases as a power law of the genome size. Since functional categories are aggregates of domain families, we asked how the abundance of domains performing a specific function emerges from evolutionary moves at the family level. We found that domain families are also characterized by family-dependent scaling laws. The second chapter provides a theoretical framework for the emergence of shared components from dependency in empirical component systems with non-binary abundances. We defined a positive model that builds a realization from a set of components linked in a dependency network. The ensemble of resulting realizations reproduces both the distribution of shared components and the law for the growth of the number of distinct families with genome size. The last chapter extends the component systems approach to microbial ecosystems. Using our findings about families scaling laws, we analyzed how the abundance of domain families in a metagenome is affected by the constraint of power-law scaling of family abundance in individual genomes. The result is the definition of an observable, whose functional form contains quantitative information on the original composition of the metagenome.

I den här uppsatsen beskrivs en ansats till att med hjälp av statistiska metoder förutse utfallet i förmögenhetsrättsliga dispositiva tvistemål. Logistiska- och multilogistiska regressionsmodeller skattades på data för 13299 tvistemål från 5 tingsrätter och användes  till att förutse utfallet för 1522 tvistemål från 3 andra tingsrätter.   Modellerna presterade bättre än slumpen vilket ger stöd för slutsatsen att man kan använda statistiska metoder för att förutse utfallet i denna typ av tvistemål. / BACKROUND: The idea of legal automatization is a controversial topic that's been discussed for hundreds of years, in modern times in the context of Law & Artificial Intelligence. Strangely, real world applications are very rare. Assuming that the judicial system is like any system that transforms inputs into outputs one would think that we should be able measure it and and gain insight into its inner workings and ultimately use these measurements to make predictions about its output. In this thesis, civil procedures on commercial matters amenable to out-of-court settlement (Förmögenhetsrättsliga Dispositiva Tvistemål) was devoted particular interest and the question was posed: Can we predict the outcome of civil procedures using Statistical Methods? METHOD: By analyzing procedural law and legal doctrin, the civil procedure was modeled in terms of a random variable with a discrete observable outcome. Some data for 14821 cases was extracted from eight different courts. Five of these courts (13299 cases) were used to train the models and three courts (1522 cases) were chosen randomly and kept untouched for validation. Most cases seemed to concern monetary claims (66%) and/or damages (12%). Binary- and Multinomial- logistic regression methods were used as classifiers. RESULTS: The models where found to be uncalibrated but they clearly outperformed random score assignment at separating classes and at a preset threshold gave accuracies significantly higher (p<<0.001) than that of random guessing and in identifying settlements or the correct type of verdict performance was significantly better (p<<0.003) than consequently guessing the most common outcome. CONCLUSION: Using data for cases from one set of courts can to some extent predict the outcomes of cases from another set of courts. The results from applying the models to new data concludes that the outcome in civil processes can be predicted using statistical methods.