Microbial Community Responses to Host-derived

**Type de contrat **:Stage**Niveau de diplôme exigé **:Bac + 4 ou équivalent**Fonction **:Stagiaire de la rechercheA propos du centre ou de la direction fonctionnelle

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Contexte et atouts du poste

In their natural environment, plants are surrounded by a huge number of microbes, both above and
below ground. Although some can be pathogenic, many microbial communities can have substantial
beneficial effects on the plant host, including improved acquisition of nutrients, accelerated growth,
protection against pathogens, and improved resistance against abiotic stress, such as heat, drought,
and salinity. The functional benefits of a microbial community depend on its composition i.e. the
presence and abundance of individual (class of) microbial species. Plants indeed have evolved the
ability to to shape their associated microbiome by means of a vast array of specialized metabolites,
that are released near plant roots or at the leaf surface. These molecules often represent carbon and
nitrogen substrates for microbial growth, but can also act as attractants/repellents,
stimulants/inhibitors for specific microbial groups.
In addition to their interaction with the host, microbes exert a strong influence on each other by
nutritional competition, exchange or cross-feeding. Cooperative or competitive interactions among
the community members have profound effects on microbiome composition and can therefore
determine the outcome of plant microbiota interactions in a given condition.
A variety of mathematical approaches have been used to investigate the dynamics and functioning
of microbial communities1. Generalized consumer resources (CR) models are particularly well
- suited to investigate host-microbiota interactions as they allow for an explicit modelling of resource
dynamics and species metabolism (resource consumption rates, metabolic by-products,
maintenance). CR models indeed have been used to characterize the behaviour (coexistence,
diversity, resilience) of microbial communities as a function of the network topology (size of the
community, cross-feeding matrix), members preferences (specialist vs generalist) and
environmental richness (number of available substrates).
Most of these studies however have focused on the microbial community only, in a fixed
environment, disregarding the interaction with the host. Here we would like to go a step further by
considering the case of a microbial community in a dynamic environment. In particular, we will
focus on a microbial community growing on a set of host-derived substrates that can change in
composition (novel substrate) or in abundance (change in host secretion rate) during time.
The aim of the project is to conduct a computational study on a statistical ensemble of simulated
communities of increasing complexity, in order to address the following questions:
Can the addition of a new substrate induce a shift in community composition (species
relative abundance)?
How does the quantitative effect of substrate addition depend on the community structure
(specialist vs generalist, niche overlap, cross-feeding) ?

Ref.

van den Berg et al. Nature Ecology and Evolution (2022)

Marsland et al. PloS Computation Biology (2019), Pacheco et al. Nature Communication (2021),
Dal Bello et al. Nature Ecology and Evolution (2021)

The students will develop the simulation code (Python or Matlab), perform computational
experiments and define appropriate indicators to measure the impact of a substrate change on the
community composition. Community simulators codes are available from previous studies. These
codes have to be adapted to the case under study but can serve as an useful starting point.