I am a statistician in the metabolomics component of the Metabolism Exploration Platform (PFEM), which is a member of the MetaboHUB research infrastructure. (Untargeted) metabolomics is the comprehensive study of all small molecules in biological fluids or tissues, providing an overview of a system and the impact of different factors.

In this context, I design, pilot and participate in the creation, using R software, of tools (workflow, programs, packages), intended for the community, for the integration (i.e. joint analysis) of metabolomic data with data of other types, or to optimize their statistical processing.

I also provide statistical support for nutrition/health research projects involving PFEM, with a non-targeted metabolomic approach, which involves organizing data preparation, choosing and implementing the statistical and computer methods and tools best suited to the problem, and disseminating the results. The objective may be either to highlight metabolites whose relative intensities vary between experimental groups to characterize phenotypes or identify biomarkers, or to integrate metabolomic data with other data collected on the same observations.

Finally, I provide training in statistical methods applied to metabolomic data, including multi-block methods. In particular, I speak at and co-organize the ChemOmics joint school.

I am also a member of the board of the Chemometrics Group of the French Statistical Society, which is in charge of the annual Chemometrics congress and co-organizes the biennial Colloquium Chemiometricum Mediterraneum congress.

Most notable publications

• Brandolini-Bunlon, M., Jaillais, B., Cariou, V., Comte, B., Pujos-Guillot, E., & Vigneau, E. (2023). Global and Partial Effect Assessment in Metabolic Syndrome Explored by Metabolomics. Metabolites, 13(3), 373. https://doi.org/10.3390/metabo13030373
• Brandolini-Bunlon, M., Jaillais, B., Cariou, V., Vigneau, E., Comte, B., & Pujos, E. (2022). Input of sequential and orthogonalized partial least squares regression method in the exploration of metabolic syndrome. Analytics 2022, 75. https://hal.science/hal-03774517
• Comte, B., Monnerie, S., Brandolini-Bunlon, M., Canlet, C., Castelli, F., Chu-Van, E., Colsch, B., Fenaille, F., Joly, C., Jourdan, F., Lenuzza, N., Lyan, B., Martin, J.-F., Migné, C., Morais, J., Pétéra, M., Poupin, N., Vinson, F., Thevenot, E., … Pujos-Guillot, E. (2021). Multiplatform metabolomics for an integrative exploration of metabolic syndrome in older men. EBioMedicine, 69. https://doi.org/10.1016/j.ebiom.2021.103440
• Imbert, A., Rompais, M., Selloum, M., Castelli, F., Mouton-Barbosa, E., Brandolini-Bunlon, M., Chu-Van, E., Joly, C., Hirschler, A., Roger, P., Burger, T., Leblanc, S., Sorg, T., Ouzia, S., Vandenbrouck, Y., Médigue, C., Junot, C., Ferro, M., Pujos-Guillot, E., … Herault, Y. (2021). ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis. Scientific Data, 8(1), 311. https://doi.org/10.1038/s41597-021-01095-3
• Brandolini-Bunlon, M., Pétéra, M., Gaudreau, P., Comte, B., Bougeard, S., & Pujos-Guillot, E. (2019). Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10), np. https://doi.org/10.1007/s11306-019-1598-y

Other links

ORCID: https://orcid.org/0000-0003-0131-8216

HAL: https://cv.hal.science/marion-brandolini

Research gate : https://www.researchgate.net/profile/Marion-Brandolini

Metabolism exploration platform (PFEM)