Missions

The human intestinal microbiota is made up of commensal bacteria essential to our health and well-being. The progressive acquisition of these bacteria in newborns and children influences the development of the immune system and susceptibility to certain diseases. Indeed, these commensal communities have a fundamental impact on the host's physiology, mainly by aiding digestion and efficient absorption of nutrients, shaping the immune system and also protecting against the invasion of pathogens. In particular, bacteria or lactic ferments and fermented products can have a beneficial effect on health. Comparative studies of microbial taxonomic and genomic diversity in healthy individuals, the acquisition and regulation of these commensal communities, and their association with diet or chronic disease, are gradually giving us a better understanding of the role of our 'second genome', the human microbiome, hosted by our 'second brain', the enteric nervous system, and their complex interactions at the intestinal barrier. Together, these players form a complex system between humans and their microbiota that cannot be modelled or understood in a reductionist way. Despite the crucial importance of the microbiota-gut-brain dialogue throughout life, the mechanisms of reciprocal molecular interactions between the host and its microbiota are less well known and remain poorly characterised.
In this context, computer modelling is essential to characterise and better understand the mechanisms of complex and multiple molecular interactions that maintain homeostasis between the host and its microbiota. We want to develop a constraint-based model of the co-metabolism of the human host in interaction with lactic ferments of interest. This modelling work will aim to explore and predict in silico the effect of a simple bacterial consortium (lactic ferments) on the functions of the human intestinal epithelial barrier. The modelling of these interactions will propose solutions that will define a Pareto space that will need to be explored using optimisation techniques.

Activities

The main tasks of the project will be to:
- Improve an existing multi-objective constraint optimisation modelling paradigm for the representation of co-metabolism between a consortium of lactic acid bacteria and the human host.
- To improve this in silico model by integrating more realistic diversity into the model of the co-metabolism of the microbiome.
- To create a Python package integrating these modelling tools and enabling non-expert users to use it.
- To comment on and test this Python package with a view to its possible integration into cobrapy (https://opencobra.github.io/cobrapy/).

Skills

The candidate should hold a PhD in (bio)informatics, computational biology, biostatistics or related fields, with skills in modelling biological systems.
- Experience with network-based approaches and metabolic modelling at genome and community scale.
- Experience of data analysis, statistics and visualisation, for example in Python or R.
- Ability to work independently and as part of a team, and at the interface between life sciences and computer science.
- Good writing and communication skills in English are expected, as well as the ability to work in a team.

Work Context

The candidate will work in the LS2N ComBi team in Nantes, on the UFR Sciences et Techniques site.
Recruitment as part of the bi-regional project (Bretagne et Pays de la Loire) PROLIFIC (https://www.milkvalley.fr/project/prolific/).