Mots-Clés

modelling metabolism systems biology bioinformatics minipig metabolomics

Description

Context

Metabolism is the set of crucial biological processes which ensures that cells have the energy and metabolites required to survive, function, and grow. Being able to experimentally observe and measure the presence, quantities and variations of metabolites is of major importance in areas of human health, especially metabolic diseases in which the metabolism is perturbed.

The main function of blood circulation is to provide and distribute the metabolites required for organ/tissue metabolism, and to collect the by-products of all tissues to be further metabolized or eliminated. The simultaneous study of arterial (A) and venous (V) specific metabolites is a relevant approach for understanding and studying the metabolism of a given organ, as it captures the metabolites present in the blood before (ingoing) and after (outgoing) each organ. In particular, AV blood samples were collected across several organs from a minipig model fed with a high fat/high sugar diet to study the metabolic changes which occur during the onset of obesity at the tissue level [1,2].

One challenge when interpreting this type of metabolomics data is exploring the direct physiological roles of metabolites and their involvement in the metabolism of organs. Computational approaches, such as metabolic modelling, can complement experimental techniques by simulating metabolism, providing mechanistic context for understanding metabolic profiles, and exploring the metabolic pathways altered during the onset of obesity.

The known metabolism of an organism can be modelled in the form of a genome-scale metabolic network, containing interconnected sets of metabolites, reactions, and metabolic genes [3]. We can then modify this metabolic network to simulate any sort of metabolic perturbation or change in cellular environment. In this case, parameters defining the rate of import and export (flux) for each metabolite can be edited to match experimental data for each organ. SAMBA [4], a constraint-based modelling approach developed within the team, can use this disrupted metabolic network to simulate the impact on all imported and exported metabolites as well as on internal reactions.

Internship objective

The main objective of the internship is to link the experimental minipig data with the metabolic flux modelling approach SAMBA, in order to simulate and analyse the impact of obesity on the metabolism of various organs.

During this project, you will:
- Learn about metabolic modelling and constraint-based modelling for flux simulation
- Use Python and the cobrapy package for managing data and flux modelling
- Use experimental data in conjunction with modelling techniques to answer biological questions
- Learn R libraries for plotting such as ggplot2 and ggraph, and the tidyverse collection.
- Build upon and develop an RShiny application which takes user-uploaded SAMBA files as input, and runs analyses and plots (depending on available time and progress).

Skills

We are looking for a Master’s 2 (second year) student in bioinformatics, computational biology or equivalent, with the following skills:
- Experience with Python
- Knowledge of biology/systems biology
- Experience with git
- Scientific English

The following skills would be a bonus:
- Knowledge of metabolic networks and/or metabolomics
- Experience with R and RShiny

Work environment

The internship will take place in our bioinformatics team (within the MeX team), supervised by Juliette Cooke (INRAE post-doc) and Nathalie Poupin (INRAE Researcher) in the INRAE Toxalim UMR1331 laboratory in Toulouse, for 6 months in 2026.

References

[1] Bousahba, I., David, J., Castelli, F. et al. Despite similar clinical features metabolomics reveals distinct signatures in insulin resistant and progressively obese minipigs. J Physiol Biochem 79, 397–413 (2023). https://doi.org/10.1007/s13105-022-00940-2

[2] Poupin, N., Tremblay-Franco, M., Amiel, A. et al. Arterio-venous metabolomics exploration reveals major changes across liver and intestine in the obese Yucatan minipig. Sci Rep 9, 12527 (2019). https://doi.org/10.1038/s41598-019-48997-2

[3] Jonathan L. Robinson et al. “An Atlas of Human Metabolism”. In: Science Signaling 13.624 (Mar. 2020), eaaz1482. issn: 1937-9145. doi: 10.1126/scisignal.aaz1482.

[4] Juliette Cooke et al. “Genome Scale Metabolic Network Modelling for Metabolic Profile Predictions”. In: PLOS Computational Biology 20.2 (Feb. 2024), e1011381. issn: 1553-7358. doi: 10.1371/journal.pcbi.1011381.