Internship - Predicting ecological partners for gut colonization using genome-scale metabolic models

Context and challenge of the internship

In this internship, you will work on a fundamental challenge in microbiome engineering: how to transfer beneficial microbes from one environment to another. Many microbes have unique properties that could be valuable in new ecosystems. For example, bacteria adapted to fermenting plant-based foods might offer significant health benefits if they can survive and thrive in the human gut.

However, establishing a flow of microbes between ecosystems—such as from food to the gut—is far from simple. Microbes must not only adapt to new environmental conditions but also compete with well-established microbiota. This raises a key question: How can beneficial microbes be helped to colonize and thrive in new ecosystems, like the gut microbiome?

Mathematical models and ecological theory suggest that a microbe's chances of successful colonization improve when introduced alongside ecological partners. These partners can support the target microbe by sharing metabolic tasks, providing nutrients, or helping outcompete existing microbiota.

Objective and description 

In this internship, you will use genomics and genome-scale metabolic modelling combined with mathematical modelling and state-of-the-art algorithms to find optimal partner combinations that maximize the probability of successful colonization in the gut environment. Working together with modellers you will have the opportunity to use your skills in bioinformatics, programming, and data science and apply them to a new field: namely, metabolic modelling. 

In the beginning of the internship you will have the opportunity to learn how to reconstruct genome-scale metabolic models from genome sequences, evaluate their quality, and apply ecological algorithms to predict optimal partnerships for gut colonization. If your predictions are successful, they will be tested in microfermenters under experimental conditions.

Location and supervision

The daily activities of the internship will be carried at the PROSE/INRAE unit in Antony under the supervision of Daniel GARZA and Théodore BOUCHEZ. Julien TAP from MICALIS/INRAE unit of Jouy-en-Josas will provide the genomes, potential experimental validation, and external supervision.

Desired profile

• Student BAC +4/+5 or Master 2
• Basic skills in Python combined with strong motivation to further develop these skills
• Ability to work independently
• Ability to work with an English-speaking supervisor.

To apply

Please send your CV and cover letter to Théodore BOUCHEZ (theodore.bouchez@inrae.fr), Daniel GARZA (daniel-rios.garza@inrae.fr), or Julien Tap (julien.tap@inrae.fr).