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Section: Partnerships and Cooperations
International Initiatives
Inria Associate Teams
IntegrativeBioChile
Title: Bioinformatics and mathematical methods for heterogeneous omics data
International Partner (Institution - Laboratory - Researcher):
IntegrativeBioChile is an Associate Team between Inria project-team "Dyliss" and the "Laboratory of Bioinformatics and Mathematics of the Genome" hosted at CMM at University of Chile. The Associated team is funded from 2011 to 2013. The project aims at developing bioinformatics and mathematical methods for heterogeneous omics data. Within this program, we funded long-stay visitings in France to initiate long-term research lines, in complement to short visit funded by and inria-conycit program.
Participation In International Programs
Argentina - MinCYT-Inria 2011-12
Partner: Universidad Nacional de Cordoba, Grupo de Procesamiento de Lenguaje Natural (PLN), Argentina.
Title: Modélisation linguistique de séquences génomiques par apprentissage de grammaires
The projects aims at developing new grammatical inference methods to learn automatically linguistic models of genomic sequences.
International joint supervision of PhD agreement
Title: Introduction des approches combinatoires dans des modèles probabilistes pour la découverte d'évènements de régulation d'un système biologique à partir de données hétérogènes
International Partners (Institution - Laboratory - Researcher):
Title: Analyse automatisée et générique de réseaux métaboliques en nutrition
International Partner (Institution - Laboratory - Researcher):
Germany. Egide Procope Program 2011-12
Title: Reasoning in systems biology with answer set programming.
The cooperation adresses various aspects of the development of the Answer Set Programming approach in bioinformatics.Based on formal methods for the Analysis of big metabolic networks we developed a new approach with Answer Set Programming This approach can be used to check whether a network contains the reaction pathways that explain the bio-synthetic behavior of the organism. Further we developed an approach for the learning of logical models of protein signaling networks.