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Ce séminaire porte sur les interactions entre protéines, abordées d'un point de vue structural. Le fait qu'une protéine donnée puisse reconnaître différents partenaires protéiques, en utilisant le même site d'interaction, suggère l'existence de "motifs communs" reconnus sur ces différents partenaires. Que sont ces "motifs communs", présents chez des protéines adoptant des structures 3D différentes ? En utilisant une description des interfaces par leurs propriétés physico-chimiques, nous avons trouvé peu de preuves de l'existence de "motifs communs" et concluons donc que la reconnaissance se fait selon un autre mécanisme.

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Natural populations are known to differ not only in DNA but also in their chromatin-associated epigenetic marks. This diversity was reported in humans and model organisms, reflecting variations of cytosine methylation, histone acetylation, and global chromatin footprints and compaction. Understanding how this variability contributes to physiological differences is therefore highly exciting. However, this task remains challenging because epigenotypes can be of two different natures: they may be genetically encoded by DNA regulatory variation, or they may result from chromatin changes that occurred stochastically or in response to environmental conditions. Consequently, the history and heritability of epigenomic variations often escape interpretation, preventing the construction of a reliable framework for population epigenetics. We have performed a comprehensive analysis of environmentally induced resetting of yeast acetylation epi-polymorphisms, and we have generated a genome-by-epigenome map of their genetic control. This allowed us to tease apart ‘labile’ epi-polymorphisms that were not recovered after a transient environmental perturbation,…

For many years, the temporal program governing DNA replication in budding yeast (S. cerevisiae) has been thought to be actively controlled by a deterministic timing mechanism. We present a reanalysis of microarray studies of DNA replication that supports an interpretation where stochastic effects play a key role and where timing is regulated indirectly by stochastic mechanisms, with no active control being required. Using a quantitative modeling approach, we show that it is possible for replication to be highly stochastic at the level of a single replication origin while simultaneously being highly reproducible at the level of population averages seen in microarray studies. We also discuss how one can extract useful information about replication origins from experiments performed on asynchronously replicating populations of cells, a result that opens up a much larger group of experiments to quantitative analysis.

Les algues vertes (Chlorophyte) forment un groupe d'organismes eukaryotes photosynthétiques le plus souvent unicellulaires au sein duquel ont émergé les plantes terrestres. Depuis le séquençage initial de Chlamydomonas reinhardtii terminé en 2007, la génomique des algues vertes a fortement accéléré puisque à l'heure actuelle, pas moins 10 espèces de Chlorophyte ont leur génome entièrement séquencé. Notre équipe a activement participé à trois de ces projets. Je reviendrai donc ici sur nos trois dernieres années d'immersion dans le monde des algues vertes et de leurs génomes en présentant des données comparatives qui révèlent une grande variabilité dans leurs métabolismes et l'organisation de l'information génétique, bien loin de ce que laissait présager naïvement leur morphologie de "simple petit rond vert". Les algues vertes sont aussi infectés par de grands virus à ADN double brin, les phycoDNAvirus. Ces derniers jouent probablement un très grand rôle dans le contrôle des populations algales. Je présenterai quelques résultats…

Unraveling biological networks from experimental data and prior knowledge can be expressed in the framework of statistical learning. We consider two kinds of networks : gene regulatory networks which are considered as dynamical systems, and protein-protein network which are not a biological object per se but a convenient representation of a set of possible physical relations between proteins. Targetting network inference in both cases, we present two approaches we have developed for a few years: modeling approaches and predictive approaches. Modeling approaches are based on a dynamical model of the behaviour of gene regulatory networks as dynamical systems. We have chosen the probabilistic framework of state-space models or hidden Markov models to represent the evolution of gene regulatory networks and we show how frequentist and Bayesian approches provide very good estimation of dynamics parameters. Results are provided on experimental data ( on microorganisms) as well as toy data such as DREAM data…