Intégration

Bioinformatics within "Iron and Liver" Group bases its activity on iron metablism as a use-case study. We seek however to propose computationnal approaches that can be applied to other life science domains. Our bioinformatics concerns are data integration in life science, with data continuously evolving in :

• diversity, representation (raw data, textual data, structured XML data, ontologies, graphical data), quality,  quantity and last but not least in density.

More recently we focus on text annotations and large scale extraction of bio-events associating recognized bio-entities of different sorts (genes, diseases, drugs, species, cells and organs) within PubMed publications. Massive computations and mining of the extracted events is challenging : Big data that need to be turned in patterns of pertinent information easily interpretable by biologists and big data that require important computationnal infrastructures.

Dyliss is a research team in bioinformatics. We focus on sequence analysis and systems biology. We use qualitative formal systems to characterize genetic actors from non model species, such as algae or mining baceria, that control phenotypic answers when challenged by their environment.

• Methods: constraint logic programming, symbolic dynamics, machine learning, formal systems.
• Expertise: functional characterization, non-model species, multi-scale integration.
• Application domains: marine biology, micro-environmental biology...

The main computational challenge is to face lacks and incompleteness in both expert knowledge and experimental observations. Our strategy relies on three main points.

• We focus on a targeted functional characterization rather than on a complete understanding of the species.
• We rely on knowledge rather than on amounts of experimentations.
• We use model-species to validate methods.

We use qualitative formal systems for knowledge acquisition and integration. All our methods aim at identifying the space of all models that are consistent with both knowledge and observations. Then we provide tools to navigate in this space in order to investigate which properties are shared by a large proportion of the space.

The Genomic Networks group develops different Bioinformatic and Biostatistic approaches to infer the biological function of A. thaliana genes and transcriptional regulatory elements. We developped mainly 2 databases (FLAGdb++, CATdb) supporting the IPS2 Transcriptomic platform and projects. These databases are useful for comparative plant genomics and transfer of knowledge from model plant genomes to plants of agronomical interest. Using this tool, we study, in a multidisciplinary project, the transcriptional re-programming that occurs in the A. thaliana response to biotic and abiotic stresses in order to identify novel gene targets and establish networks in the perspective of biotechnological applications or genetic selection for plant stress resistance.