Section: New Results
Regulation and signaling: detecting complex and discriminant signatures of phenotypes
Participants : Catherine Belleannée, Samuel Blanquart, Célia Biane-Fourati, Nicolas Guillaudeux, Marine Louarn, Maxime Folschette, François Moreews, Anne Siegel, Nathalie Théret, Pierre Vignet, Méline Wery.
Comparative-genomics based prediction of non-model transcriptomes [C. Belleannée, S. Blanquart, N. Guillaudeux] In order to annotate the transcriptome of a non-model species, Canis lupus familiaris, we developed a method to predict whether or not a transcript known in a given species/gene could be expressed in an other species/gene. Exploiting knowledge in human, mouse and dog , we predicted a total of 7201 unknown yet transcripts and interpreted the evolutionary dynamics of gene's isoform sets. 
We introduced a new method to learn an interaction graph from the knowledge of its state space, without assumption on the semantics that was used to produce it. Proofs and characterizations are given for the synchronous, asynchronous and generalized semantics.
We also used the caspo time-series software to integrate large-scale time series phosphoproteomic data (HPN-DREAM Breast Cancer challenge) into protein signaling networks and infer a family of Boolean Networks. The method highlights commonalities and discrepancies between the four cell lines.
Static analysis of ruled-based models [P. Vignet, N. Théret] We used a model of TGF- to illustrate the main features of Kasa, a static analyzer for Kappa models. Kappa is a rule based language that describes systems of mechanistic interactions between proteins by the means of site-graph rewriting rules. The cornerstone of KaSa is a fix-point engine which detects some patterns that may never occur whatever the evolution of the system may be .