Candidature
Procédure : Candidater par email avant une candidature sur https://theses.doctorat-bretagneloire.fr/vaame
Bibliographie
1. David, B., J.-L. Wolfender, and D.A. Dias, The pharmaceutical industry and natural products: historical status and new trends. Phytochemistry Reviews, 2014. 14(2): p. 299-315.
2. Bode, H.B., et al., Big effects from small changes: possible ways to explore nature's chemical diversity. ChemBioChem, 2002. 3(7): p. 619-627.
3. Bertrand, S., et al., Metabolite induction via microorganism co-culture: a potential way to enhance chemical diversity for drug discovery. Biotechnology Advances, 2014. 32(6): p. 1180-1204.
4. Romano, S., et al., Extending the “One Strain Many Compounds” (OSMAC) Principle to Marine Microorganisms. Marine Drugs, 2018. 16(7).
5. Wolfender, J.-L., et al., Current approaches and challenges for the metabolite profiling of complex natural extracts. Journal of Chromatography A, 2015. 1382: p. 136–164.
6. Wolfender, J.-L., et al., Accelerating metabolite identification in natural product research: toward an ideal combination of LC-HRMS/MS and NMR profiling, in silico databases and chemometrics. Analytical Chemistry, 2019. 91(1): p. 704-742.
7. Blin, K., et al., antiSMASH 4.0—improvements in chemistry prediction and gene cluster boundary identification. Nucleic Acids Research, 2017. 45(Web Server issue): p. W36-W41.
8. Cairns, T. and V. Meyer, In silico prediction and characterization of secondary metabolite biosynthetic gene clusters in the wheat pathogen Zymoseptoria tritici. BMC Genomics, 2017. 18(1): p. 631.
9. Kim, H.U., et al., Recent development of computational resources for new antibiotics discovery. Current Opinion in Microbiology, 2017. 39: p. 113-120.
10. Eustáquio, A.S. and N. Ziemert, Identification of Natural Product Biosynthetic Gene Clusters from Bacterial Genomic Data. 2018.
11. Dittami, S.M., et al., Genome and metabolic network of “Candidatus Phaeomarinobacter ectocarpi” Ec32, a new candidate genus of Alphaproteobacteria frequently associated with brown algae. Frontiers in Genetics, 2014. 5: p. 241.
12. Prigent, S., et al., The genome-scale metabolic network of Ectocarpus siliculosus (EctoGEM): a resource to study brown algal physiology and beyond. The Plant Journal, 2014. 80(2): p. 367-381.
13. Daletos, G., et al., Microbial Coculture and OSMAC Approach as Strategies to Induce Cryptic Fungal Biogenetic Gene Clusters. 2017. p. 233-284.
14. Liu, M., et al., A systems approach using OSMAC, Log P and NMR fingerprinting: An approach to novelty. Synthetic and Systems Biotechnology, 2017. 2(4): p. 276-286.
15. da Silva Lima, G., et al., Metabolic response of Aspergillus sydowii to OSMAC modulation produces acetylcholinesterase inhibitors. Phytochemistry Letters, 2018. 24: p. 39-45.
16. Nègre, D., A. Larhlimi, and S. Bertrand, Reconciliation and Evolution of Penicillium rubens Genome-Scale Metabolic Networks – What about Specialised Metabolism? bioRxiv, 2022: p. 2022.11.10.515991.
17. Agren, R., et al., The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum. PLoS Computational Biology, 2013. 9(3): p. e1002980.
18. Prigent, S., et al., Reconstruction of 24 Penicillium genome-scale metabolic models shows diversity based on their secondary metabolism. Biotechnology and Bioengineering, 2018. 115(10): p. 2604-2612.
Date limite : 14 avril 2023
Contacts
Samuel BERTRAND
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