Genetically identical individuals growing in the same environment often show substantial phenotypic variation. The reasons for this phenotypic variation are usually unknown. We start from genome wide expression profiling on genetically identical C. elegans roundworms to characterize molecular differences, understand their phenotypic impact and infer the mechanisms that generate such variation. I will show how using this strategy we previously found maternal age to be an important determinant of phenotypic variation with changes in maternal provisioning of yolk to the embryo explain some of the observed phenotypic differences. Then I will show we have now found the ‘smell’ of the worms (i.e pheromones) – which is perceived by neurons – also induces important phenotypic variation generating an inherited memory that alters germline development and minimum generation time in the progeny of exposed worms. 

Dimensional reduction approaches are crucial to extract relevant information from high-dimensional datasets, such as clinical or omics data. We have developed a toolbox based onnon-negative matrix factorization, in order to extract relevant biological and clinicalsignatures from high-dimensional datasets and combine various modalities into molecularsignatures. I will present some recent applications of these approaches, fromneuroblastoma epigenomics to the study of comorbidities between mental and somaticdisorders and the application to transfer learning from single-cell to bulk datasets.

The problem of controlling a dynamic network has a long history in control theory, with roots in a diversity of mathematical methods from complex analysis to topology, graph theory and computational complexity. The basic problem setup is that of a dynamical system represented as a directed graph, with nodes influencing each other’s dynamics. Control is sought over a given set of targets, in the sense of being able to change their configuration through external interventions on some well-chosen input nodes in the network, taking advantage of the network topology. We are interested in finding a minimal set of input nodes in the network such that the behavior of the target nodes may be changed arbitrarily through a well-chosen sequence of signals to the input nodes, cascaded throughout the network through its wiring. We focus on formalizations of this network controllability problem that maximize its applicability in biomedicine, including a specific set of…

Osteosarcoma microenvironment composition is a major feature to identify hard-to-treat patient tumors at diagnosis. Using machine learning to capture this microenvironment from bulk RNA-seq, we define the biological pathway and potential actionable targets associated with these tumors and validate a minimal set of biomarkers to detect hard-to-treat patients by RNA-seq and Nanostring in 178 additional tumors. In line with these results and to improve our understanding of the crosstalk between microenvironment and tumor cells leading to disease relapse, we initiated a spatio-temporal investigation at near single-cell level of Osteosarcoma in PDX and patients tumors.

Why biological quality-control systems fail is often mysterious. Checkpoints in yeast and animals are overridden (adapt) after prolonged arrests allowing self-replication to proceed despite the continued presence of errors. Although critical for biological systems, checkpoint adaptation is not understood quantitatively or at the system level by experiment or theory. To uncover potential patterns in checkpoint adaptation, we derived the mathematically optimal checkpoint strategy, balancing the trade-off between risk and opportunities for growth. The theory predicts the optimal override time based on two inputs, the statistics i) of error correction and ii) of survival. We applied the theory experimentally to the DNA damage checkpoint in budding yeast whose override is not understood quantitatively, functionally, or at the system level. Using a fluorescent construct which allowed cells with DNA breaks to be isolated by flow cytometry, we quantified i) the probability distribution of repair for a double-strand DNA break (DSB), including for rare events…

Our genome is constantly damaged by a variety of exogenous andendogenous agents. Among the various forms of DNA damage, double-strandbreaks (DSBs) are the most cytotoxic and genotoxic for the cell.Eukaryotic organisms use several mechanisms to repair DSBs among themnon-homologous end-joining (NHEJ) and homologous recombination (HR).              Here, here, I will present our recent work to investigatethe molecular mechanisms of HR proteins inside cells at the singlemolecule level. In response to DSB, repair proteins colocalize from adiffuse distribution to repair foci located at the damaged DNA site. Anenduring question in the DNA damage field is how do repair proteins findtheir correct target, accumulate within repair foci and disassemble withthe proper time-window? Despite their functional importance, thephysical nature of repair sub-compartments remains unclear. To answerthese questions, we use single particle tracking (SPT) and PALM (PhotoActivable Localization Microscopy) to assess the physical propertiesunderlying repair foci formation and the internal dynamics of…

Seasonal influenza viruses repeatedly infect humans in part because they rapidly change their antigenic properties and evade host immune responses, necessitating frequent updates of the vaccine composition. A better understanding of this evolution could allow for better predictions and would be important for vaccine design.We investigated the predictability of frequency dynamics and fixation of amino acid substitutions and found that the current frequency was the strongest predictor of eventual fixation, as expected in neutral evolution. Other properties, such as occurrence in previously characterized epitopes or high Local Branching Index (LBI) had little predictive power. Simulations of models of adapting populations, in contrast, show clear signals of predictability. This indicates that the evolution of influenza HA and NA, while driven by strong selection pressure to change, is poorly described by common models of directional selection such as traveling fitness waves.Influenza diversity is not only generated by mutation but also by reassortment: the exchange…