Société Française de Bioinformatique

Mots-Clés

metabolism cyanobacteria ecology constraint modeling

Description

The Computational Biology group at LS2N, Nantes Université, in partnership with the Roscoff Biological Station, is excited to announce an internship opportunity focused on exploring the metabolic niche space of alpha-cyanobacteria using Constraint-Based Modeling (CBM). This thesis is part of the framework of the TaxCy ANR research program, launched in April 2024.
alpha-Cyanobacteria, a diverse group of photosynthetic bacteria, are essential contributors to aquatic food webs and biogeochemical cycles, especially in marine ecosystems where they account for about 25% of global net primary productivity. Their success stems from their remarkable adaptability, enabling them to thrive in diverse marine environments, ranging from warm tropical to cold subpolar waters and from nutrient-rich coastal areas to the most nutrient-poor oceanic gyres. This adaptability is supported by their metabolic versatility and ability to utilize diverse inorganic and organic nutrients as well as orchestrate complex biochemical pathways for growth and survival. This master’s internship project will utilize the power of CBM to shed light on the relationships between cyanobacterial metabolism and ecological niche occupation.

Research Objectives :

• Adapt existing automatic metabolic model reconstruction to alpha-cyanobacteria,
• Reconstruct a genome-scale metabolic model (GSM) for six alpha-cyanobacterial strains, representatives of distinct ecotypes colonizing different niches in the marine environment (i.e., clades I, II, III, IV, V, and CRD1).
• Implement and validate the GSM using existing transcriptomic data from the Roscoff Marine Station, along with existing cyanobacterial models and literature.
• Apply CBM techniques, such as Flux Balance Analysis (FBA) and Metabolic Engineering, to:
  • Predict the most efficient metabolic fluxes for alpha-cyanobacteria in each ecological niche.
    -Identify metabolic adaptations that facilitate niche occupation and environmental tolerance.
  • Examine how -cyanobacterial metabolism interacts with environmental factors.
  • Infer metabolic niche boundaries based on predicted metabolic capabilities.

Why You Should Apply:

This unique joint project offers a remarkable opportunity to:
* Be at the forefront of computational systems biology research at the intersection of ecology and bioinformatics.
* Gain expertise in CBM, metabolic network analysis, and ecological modeling.
* Contribute to a groundbreaking project that advances our understanding of microbial ecology and biogeography.
* Work in a collaborative and interdisciplinary environment with researchers at LS2N and at the Roscoff Biological Station.

Applicant Qualifications :

• Student in a Master’s degree in a relevant field (e.g., Bioinformatics, Computational Biology, Marine Sciences).
• Strong background or at least interest in biochemistry, microbiology, ecology, and/or computational modeling.
• Programming proficiency (e.g., Python, MATLAB).
• Excellent analytical and problem-solving skills.
• Passion for research and a commitment to scientific excellence.

We invite applications from highly motivated and enthusiastic students eager to explore the fascinating world of cyanobacteria, their metabolic adaptations, and their role in shaping marine ecosystems.