Phd Position F/m Generation of Metabolomic-informed
il y a 4 semaines
Le descriptif de l’offre ci-dessous est en Anglais_
**Type de contrat **:CDD
**Niveau de diplôme exigé **:Bac + 5 ou équivalent
**Fonction **:Doctorant
**A propos du centre ou de la direction fonctionnelle**:
The Inria center at the University of Bordeaux is one of the nine Inria centers in France and has about twenty research teams.. The Inria centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative SMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute...
**Contexte et atouts du poste**:
This PhD project in computer science is a joint work between INRAE UMR BFP at Villenave d'Ornon and Inria Project Team Pleiade in Talence. The PhD student will be supervised by Clémence Frioux (Inria) and Sylvain Prigent (INRAE).
The job environment is interdisciplinary at the interface between computer science and biology.
**Mission confiée**:
The generation of metabolic networks at the scale of the genome is becoming a routine analysis for individual organisms and microbial consortia. These networks gather metabolic reactions whose presence prediction in the species is supported by gene-protein-reaction relationships. In practice, they translate genetic information into functional information, structured as a network depicting consumption and production of molecules through metabolic reactions. These networks can be used to predict the metabolic potential of the related species in given environments using flux-based simulations or reasoning-based approaches.
Automatically-generated genome-scale metabolic networks (GSMNs) are constructed by relying on the association of gene annotation and reactions available in generic databases. The latter are biased towards well-known or model organisms, and miss important functions related to the secondary metabolism. This is an obstacle when the purpose of metabolic modelling is to decipher the functions and interactions occurring in microbial communities, especially in complex environments (plant, soil). Manual curation of the metabolic models can mitigate these limits, through the incorporation of expert and literature-based knowledge, and is necessary for precise and quantitative predictions of metabolism. However, this does not suffice as the curation process is not scalable to large and complex communities, nor is it conceivable for poorly-studied organisms.
On the other hand, from the experimental data side, untargeted metabolomic data is a major resource for capturing the set of active functions in the metabolism of species or communities. A sample generates typically 20K to 30K metabolic profiles, that lead after filtration, to a few thousands of profiles to be analyzed. The annotation of metabolomic profiles constantly improves: up to 40% of the profiles can get a partial or complete annotation these days. Molecular networks can contribute to the inference of structural information as closely related metabolites will share patterns of their fragmentation profiles. We want to explore the link between metabolomic data, metabolic and molecular networks during this PhD in order to assess the extent to which the latter can facilitate and benefit profile annotations. Existing databases gather the results of metabolomic experiments and can be used during this PhD 3,4, as well as public data related to experiments on controlled communities 5.
1. Blin, K., Shaw, S., Kautsar, S.A., Medema, M.H., and Weber, T. (2020). The antiSMASH database version 3: increased taxonomic coverage and new query features for modular enzymes. Nucleic Acids Res 49, gkaa978-. 10.1093/nar/gkaa978.
2. Flissi, A., Ricart, E., Campart, C., Chevalier, M., Dufresne, Y., Michalik, J., Jacques, P., Flahaut, C., Lisacek, F., Leclère, V., et al. (2020). Norine: update of the nonribosomal peptide resource. Nucleic Acids Res 48, D465-D469. 10.1093/nar/gkz1000.
3. Wishart, D.S., Oler, E., Peters, H., Guo, A., Girod, S., Han, S., Saha, S., Lui, V.W., LeVatte, M., Gautam, V., et al. (2022). MiMeDB: the Human Microbial Metabolome Database. Nucleic Acids Res. 51, D611-D620. 10.1093/nar/gkac868.
4. Yurekten, O., Payne, T., Tejera, N., Amaladoss, F.X., Martin, C., Williams, M., and O’Donovan, C. (2023). MetaboLights: open data repository for metabolomics. Nucleic Acids Res. 52, D640-D646. 10.1093/nar/gkad1045.
5. Timm, C.M., Pelletier, D.A., Jawdy, S.S., Gunter, L.E., Henning, J.A., Engle, N., Aufrecht, J., Gee, E., Nookaew, I., Yang, Z., et al. (2016). Two Poplar-Associated Bacterial Isolates Induce Additive Favorable Responses in a Constructed Plant-Microbiome System. Front. Plant Sci. 7, 497. 10.3389/fpls.2016.00497.
**Principales activités**:
Main activities (5 maximum):
- Define a formalism for the representation of secondary metabolism obtained from the mining of dedicated annotation databases. This
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