Our research concerns the reconstruction, analysis and evolution of biomolecular networks at different scales and their implication on the organisms’ susceptibility to genetic diseases such as cancer. We develop quantitative statistical methods and computational tools to infer and analyze causal graphical models from biological data.
Evolution of large biomolecular networks
We are interested in the analysis of large biomolecular networks and their evolution due to single gene and whole genome duplications, which occurred repeatedly in the course of eukaryote evolution Fig.1, see http://ohnologs.curie.fr
Our early theoretical analyses focussed on duplication-divergence models to account for the generic properties of biomolecular networks. We have shown that duplication-divergence processes bring not only genetic novelty but also evolutionary constraints that restrict by construction the emerging properties of biomolecular networks. In particular, we demonstrated that networks with evolutionary conserved genes display also necessary topological properties by construction (such as hubs and scale-free degree distribution). We are also interested in the evolution of transcription networks and study the regulatory conflicts that arise through duplication of transcription factors and autoregulators.
More recently, we have analysed the evolutionary constraints on signaling networks implicated in cancer (Fig.2). We investigated the evidence that the emerging properties of these signaling pathways might actually reflect their susceptibility to oncogenic mutations and thus their implication in cancer. We have found, in particular, that “dangerous” gene families implicated in cancer have been greatly expanded through two rounds of whole genome duplication (WGD) (Fig. 2) in early vertebrates. These findings highlight the importance of WGD-induced nonadaptive selection for the emergence of vertebrate complexity, while rationalizing, from an evolutionary perspective, the expansion of gene families frequently implicated in genetic disorders and cancers.
RNA regulatory networks and RNA nanostructures.
We have developed a server for advanced RNA dynamic simulation (http://kinefold.curie.fr >100,000 online simulations) and studied the properties of small regulatory circuits primary based on RNAs and their interactions. In particular, we have used synthetic biology approaches to design efficient RNA-based repressor and activator modules. These modules control RNA transcription “on the fly” through simple RNA-RNA antisense interactions. We also discovered that DsrA, a small bacterial RNA of Escherichia coli could self-assemble, like many proteins do, to form long filaments and larger physical networks (Fig3). This finding further extends the already great versality of natural RNA functions.