Section: New Results

Data integration

Participants : Jacques Nicolas [contact] , Geoffroy Andrieux, Andres Aravena, Pierre Blavy, Jérémie Bourdon, Guillaume Collet, Damien Eveillard, Michel Le Borgne, Sylvain Prigent, Anne Siegel, Sven Thiele, Valentin Wucher.

• Identification of key regulators by the integration of flux and regulatory information [P. Blavy, A. Siegel] We introduced a new method to combine reaction-based "flux" information (consumption and prediction of molecules) and regulatory "causal" information (effect of the variation of a molecule on the variation of another molecule) in order to find potential key regulators of a set of molecules. It has been validated by recovering among the causal graph derived from the Transpath database the main regulators of 190 groups of genes which are known to share a transcription factor according to the TRED database. [22] [Online publication]

• Reconstruction of transcriptional networks [A. Aravena, A. Siegel] Transcriptional regulatory network models can be reconstructed ab initio from DNA sequence data by locating the binding sites, defined by position specific score matrices, and identifying transcription factors by homology with known ones in other organisms. In general the resulting network contains spurious elements. We use differential expression experimental data, in the form of Mutual Information, as ASP logical constraints to be satisfied by any valid regulatory network subgraph. These rules are used to determine the minimal sets of motif and transcription factors which constitute a genetic regulatory network compatible with experimental data [20] [Online publication] .

• Studying diversity in marine environment [D. Eveillard] We proposed a statistical-based data analysis of environmental microarrays. It shows that similar physical parameters drive bacterial and archae communities that share common ammonia oxidizing capacities [12] [Online publication]

• Brown algae metabolic network reconstruction [S. Prigent, S. Thiele, A. Siegel] In order to better understand the functioning of cellular metabolism in the model brown alga E. siliculosus, metabolic networks are under construction based on genomic information. Two approaches are conducted in parallel to complete the network, a stochastic one that proceeds by sampling the solution space and a combinatorial one that tries to minimize the number of added reactions [23] .