M2 - integration of -omics data into metabolic regulatory network analysis

Type de poste
Contrat renouvelable
Contrat non renouvelable
Date de prise de fonction
Date de fin de validité de l'annonce
Nom de la structure d'accueil

Grenoble / Lyon

ROPERS Delphine
SAGOT Marie-France
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<p><strong>M2 internship in bioinformatics: integration of -omics data into metabolic regulatory network analysis</strong></p><p>Adaptation of bacterial growth to environmental or genetic perturbations involves numerous regulations. Advanced &ndash;omics technologies allow monitoring the adaptive behavior, by tracking down modifications of metabolite, mRNA and enzyme concentrations. The biggest challenge nowadays is to integrate the data and especially to make sense of them. By relating the modifications of metabolic fluxes with the concentration changes, these data should inform us on how cells coordinate their response to a given perturbation. More precisely, we would like to infer which part of the metabolism was directly affected by the perturbation. As the &ndash;omics information may be incomplete or noisy, it is however a far from trivial task to both model and address the question, given the complexity of the mathematical representations of metabolism.</p><p>An approach developed at LBBE/Inria Lyon could help meet this objective. It is based on the representation of a metabolic network as a directed hypergraph, followed by the extraction from this graph of all sub-graphs that include reactions playing a role in the change of metabolite concentrations. To do so, an optimization problem is formulated, in which parsimonious changes of metabolite concentrations are assumed. This approach has been originally applied to study a smooth metabolic transition, the yeast response to cadmium exposure, for which only qualitative data were available (1,2). The objective of the internship is to study the applicability of the approach and its further extensions to the analysis of post-transcriptional regulations of the central carbon metabolism in the bacterium <em>Escherichia coli</em>. Does the approach permit a more global analysis of enzymatic reactions affected by the post-transcriptional regulator CSR using metabolomics data (2)? In a second step, transcriptomics data will be also exploited and the results compared with those using metabolomics data.</p><p>The internship student will be mainly based at Inria Grenoble, with frequent visits at Inria Lyon and in close collaboration with the laboratory LISBP at INSA Toulouse. We seek a highly motivated and autonomous student. Good relational skills are important for the project, as it will be carried out in an interdisciplinary and international environment. Basic knowledge in microbiology and previous experience with some of the above-mentioned techniques would be appreciated.</p><p>(1) Milreu <em>et al</em>. (2014), <em>Bioinformatics</em>, 30(1):61-70.</p><p>(2)&nbsp; Julien-Laferrière (2016) PhD thesis, Univ. Lyon.</p><p>(3) Morin <em>et al. </em>(2016), <em>Mol Microbiol</em>. 100(4):686-700</p><br/>
Lieu: <p>Inria Grenoble - Alpes</p><p>655 avenue de l&#39;Europe 38334 Montbonnot Saint-Martin</p><p>et</p><p>Inria/Laboratoire de Biométrie et Biologie Évolutive (LBBE), Université Claude Bernard (Lyon 1), 43 Boulevard du 11 Novembre 1918 69622 Villeurbanne cedex</p>