Post-doc - development of systems biology approaches for toxicity evaluation of cosmetic materials

Post-doc - development of systems biology approaches for toxicity evaluation of cosmetic materialsCDD·Postdoc·24 moisBac+8 / Doctorat, Grandes ÉcolesUMR Toxalim, INRAE in collaboration withL'Oreal Advanced Research·Toulouse (France)3130€/month brut metabolic network modeling transcriptomics toxicology modes of action DescriptionContext: In cosmetology, as in food toxicology, a better understanding of biological mechanisms of action has become an absolute priority when it comes to assessing the toxicity of raw materials. In cosmetology, the ability to predict the toxic potential of a molecule as far upstream as possible is also becoming essential in the process of researching new products, in order to limit the number of tested candidates and maximize the chances of success in the clinic. In this context, the ban on animal testing in the cosmetics field since 2013 and the limitations of classic in silico structural "read across" approaches have led to the need for new, more integrative systems biology methodological approaches (NAM) to assess the risk associated with the use of new cosmetic ingredients, and in particular to enable better prediction and understanding of this toxicity risk and the underlying biological mechanisms.Objective: This collaborative research project between the MeX team and L'Oreal aims to develop new systems biology approaches based on Genome Scale Metabolic Network (GSMN) modeling to interpret transcriptomic data in a more mechanistic way that would be relevant to toxicology issues, particularly in the cosmetics field, to enable the prediction and assessment of the toxicity associated with these compounds and the understanding of the underlying biological mechanisms.The main missions of the recruited candidate will be to:Perform the reconstruction of condition-specific metabolic networks for the classification of a large number of compounds based on their hepatic toxicity and their metabolic effect. This will involve adapting the previously developed pipeline to be able to integrate and interpret RNAseq data and for a much larger set of compounds (Rodríguez-Mier et al. PLoS Comput Biol. 2021;17. doi: 10.1371/JOURNAL.PCBI.1008730; Fresnais et al. bioRxiv. 2023. doi: https://doi.org/10.1101/2023.06.30.547200).Establish and validate relevant metrics for the classification of compounds based on the comparison of metabolic networks perturbations and metabolic functions analysis. Such metrics will be developed with the aim of being integrated in a biological read-across strategy.Explore in greater details the mechanisms of action associated with one or more exposure conditions. This will require to refine our methodology for subnetwork identification and extraction, by integrating complementary data (omics and/or toxicity) and by using flux analysis techniques allowing for a more detailed functional analysis of the metabolic perturbation.Integrate the developed algorithms and tools within the L’Oréal bioinformatics ecosystem.Contribute to the biological interpretation of modulations induced by molecules on metabolism. This will involve interactions with biologists (physiologists and toxicologists) to make the methodologies and visualization tools usable and understandable by them.Be involved in outreach through publications and presentations in conferences.Working environment: The proposed research project is part of a collaborative project between the MeX (Metabolism and Xenobiotics) research team of the TOXALIM unit (INRAE Occitanie-Toulouse research center) and the Systems Biology team of L'Oréal Advanced Research located in Aulnay-Sous-Bois. It follows on from initial work on network-based analysis of metabolic modes of actions (Fresnais et al., bioRxiv 2023.06.30.547200; doi:https://doi.org/10.1101/2023.06.30.547200 ). The research project will focus on developing bioinformatics and modeling methods, using mainly constraint-based modelling and graph algorithms techniques, but will be also embedded in a broader multidisciplinary context including toxicology, omics technologies and biochemistry. The candidate will be under the main supervision of the MeX team regarding the bioinformatics developments and will also closely interact with the L'Oreal team for all the safety and regulatory context, data generation, results interpretation … Interpretation of metabolic mechanisms and identification of toxicity modes of action will also be performed in collaboration with Dr Bernard Fromenty (Senior researcher, INSERM), who is an expert in hepatic physiology.The MeX team has been developing, for several years, bioinformatics methods to study metabolic modulations at cell or tissue level. These approaches rely upon modeling the metabolism as a genome-scale metabolic network, using constrained-based modeling and graph algorithms approaches, together with omics data (in particular metabolomics and transcriptomics). The approaches developed within the team are applied to the study of chemical compounds that may be found in foods (e.g. Bisphenol A) in order to identify their metabolic effects. The team is composed of around thirty scientists, including biologists/toxicologists/biochemists and bioinformaticians (currently 5 permanent staff and 7 fixed-term contracts and PhD students).The Systems Biology team within L'Oréal research aims to develop in vitro and digital models and methods to ensure safety of cosmetic ingredients without animal testing. Thus, omics data are extensively explored, to take advantage of their richness, generated either during in-house studies or available in the public domain. The aim of all these approaches is to enrich our knowledge of the biological mechanisms of action of a cosmetic raw material, leading to the identification of new targets for the development of new safe and effective cosmetic products.Candidature Procédure : By e-mail, to [email protected] and [email protected] Poupinna NOSPAM [email protected] #J-18808-Ljbffr