Les systèmes concurrents sont un bon choix pour ajuster les données et analyser les mécanismes sous-jacents pour leur sémantique simple mais expressive. Cependant, l’apprentissage et l’analyse de tels systèmes concurrents sont difficiles pour ce qui concerne les calculs. Lorsqu’il s’agit de grands ensembles de données, les techniques les plus récentes semblent insuffisantes, que ce soit en termes d’efficacité ou de précision. Ici, nous proposons un cadre de modélisation raffiné ABAN (Asynchronous Binary Automata Network) et développons des outils pour analyser l’atteignabilité : PermReach (Reachability via Permutation search) et ASPReach (Reachability via Answer Set Programming). Nous proposons ensuite deux méthodes de construction et d’apprentissage des modèles: CRAC (Completion via Reachability And Correlations) et M2RIT (Model Revision via Reachability and Interpretation Transitions) en utilisant des données continues et discrètes pour s’ajuster au modèle et des propriétés d’accessibilité afin de contraindre les modèles en sortie. / Concurrent systems become a good choice to fit the data and analyze the underlying mechanics for their simple but expressive semantics. However, learning and analyzing such concurrent systems are computationally difficult. When dealing with big data sets, the state-of-the-art techniques appear to be insufficient, either in term of efficiency or in term of precision. In this thesis, we propose a refined modeling framework ABAN (Asynchronous Binary Automata Network) and develop reachability analysis techniques based on ABAN: PermReach (Reachability via Permutation search) and ASPReach (Reachability via Answer Set Programming). Then we propose two model learning/constructing methods: CRAC (Completion via Reachability And Correlations) and M2RIT (Model Revision via Reachability and Interpretation Transitions) using continuous and discrete data to fit the model and using reachability properties to constrain the output models.

The complexity of biological systems constitute a significant problem for the development of biological models. This inspired the creation of a few Computational Scientific Discovery systems that attempt to address this problem in the context of metabolomics through the use of computers and automation. These systems have important limitations, however, like limited revision and experiment design abilities and the inability to revise refuted models. The goal of this project was to address some of these limitations. The system developed for this project, "Huginn", was based on the use of Abductive Logic Programming to automate crucial development tasks, like experiment design, testing consistency of models with experimental results and revision of refuted models. The main questions of this project were (1) whether the proposed system can successfully develop Metabolic Network Models and (2) whether it can do it better than its predecessors. To answer these questions we tested Huginn in a simulated environment. Its task was to relearn the structures of disrupted fragments of a state-of-the-art model of yeast metabolism. The results of the simulations show that Huginn can relearn the structure of metabolic models, and that it can do it better than previous systems thanks to the specific features introduced in it. Furthermore, we show how the design of extended crucial experiments can be automated using Answer Set Programming for the first time.