Genome-Scale Metabolic Modelling Of Industrial Microbes 6 Months Internship H/F - Lesaffre

As a global player in yeasts and fermentation, Lesaffre designs, manufactures and markets innovative solutions for Baking, Food Taste & Pleasure, Health Care and Biotechnology. Born in northern France in 1853, Lesaffre is a multi-national and multicultural company committed to working towards better nourishing and protecting the planet. Its capabilities to explore the potential of microorganisms, as well as its leading fermentation knowledge, make Lesaffre one of the most promising answers to this challenge.

At the heart of this dynamic, the BioData Department plays a strategic role supporting R&D through biological data engineering, management, and analysis. Within this department, this internship will be jointly supervised by a researcher from our Bioinformatics team (BioInfo), who is responsible for data management, code infrastructure, and pipeline development, and our Computational Biology team (BioAI), who is responsible for data analysis, modelling, and method development & application. As an innovative and multidisciplinary team, BioData develops software to efficiently handle large amounts of data, process data collected at different levels of biological organization using diverse experimental methods and provide timely analysis and interpretation of this data in the context of Lesaffre's research activities.

The project will be part of ongoing initiatives to further develop methodologies and tools with the potential to bring insights to existing genomics data. At Lesaffre, as a result of our R&D activities, we house a large genomic collection of industrial yeasts and bacteria. Under the supervision of a bioinformatician and a computational biologist, you will leverage both biological and software development skill sets to develop new pipelines that mine this genomic data to bring value to our biological research teams, strengthening our methodology portfolio and improving our data analysis capabilities, while working in close collaboration with both dry- and wet-lab researchers.

More precisely, the prioritized objectives of the internship will be to:

1. Implement and improve methods for automated high-throughput metabolic network reconstruction and analysis.

2. Further develop and diversify our existing microorganism genomic annotation workflows.

3. Apply the developed methods by formalizing reproducible and efficient pipelines for large-scale application.

Successful completion of the objectives will result in the following expected deliverables:

1. Successful Reconstruction and QC of Metabolic Networks of Strains from Our Genomic Collection.

2. Versioned Nextflow pipeline.

3. Presentation of Results to R&D teams.

• Final year master's or engineering student (Bioinformatics, Computational Biology, Systems Biology or related fields).

• Good programming skills in Python.

• Familiarity with pipeline tools (e.g., Nextflow).

• Exposure to version control (Git/Gitlab) and reproducible research (e.g., Docker, Anaconda).

• Solid knowledge of genomics, biochemistry, and microbial metabolism.

• Knowledge of constraint-based reconstruction and analysis (COBRA) methods (coursework or project).

• Analytical mindset, rigor, and autonomy.

• Strong communication skills.

• Fluency in English.