Mots-Clés

systems biology microbiota metabolic modelling dynamics

Description

The position is funded by Inria and is part of the Inria Exploratory Action SLIMMEST: Statistical Learning of the Intestinal Microbiota MEtabolism in Space and Time. The main objective of the SLIMMEST project is to resolve a numerical bottleneck in spatio-temporal modeling of microbiota: the coupling between microbe-scale metabolic models with community-scale dynamics described with PDE models. The recruited person will provide expertise in system biology, microbial metabolism, and community-wide metabolic network modeling. Missions will include modelling and simplification of metabolic models in order to identify metabolic drivers of ecosystems. Such drivers will in turn be used by the second postdoc scientist of the project to develop machine learning techniques and build a partial differential equations (PDE) model of the gut microbiota.

Project

The dynamics of a microbial community is driven by the metabolism of its microorganisms, the interactions between those microorganisms, and spatio-temporal interactions between them and the environment. Mathematical and computational models of such dynamics are crucial to build mechanistic hypotheses of the biological observations, as well as predict the evolution of the ecosystems, and actions to lead ecosystems in a desired state. The SLIMMEST project will combine logic programming and metamodelling of metabolism in a scalable framework applied to communities of the gut microbiota. The purpose is to build a quantitative model of the gut microbiota in time and space.

The recruited person will be taken to carry the metabolic-modelling related part of the SLIMMEST project. This project gathers scientists working in systems biology, microbial ecology, mathematical and logic modelling. Two postdoc scientists will be recruited, one specialised in systems biology (this offer), and one mathematician who will build metamodels of the community using partial differential equations. In order to build the spatiotemporal model of the gut microbiota, we need to identify key species and functions within the microbial community, that will constitute the set of variables tracked during the mathematical simulation. This selection and community simplification step is of the highest importance due to scaling issues associated to quantitative simulations.

For that purpose, we must model the metabolism of bacteria from their genome, and connect metabolic networks to the available literature resources on pathways of interest such as short-chain fatty acid production in the context of the gut microbiota. But more generally, for any community, we would like to be able to identify species that are cornerstone to the microbial ecosystems, as well as functions or interactions that are important for the assembly of microbes. The simplification of the community metabolism will be performed by modelling constraints and solving combinatorial optimisation problems. In addition, the postdoc scientist will contextualise the results of community simulations back to metabolic networks in order to understand the mechanistic processes that could drive the observed dynamics. The first application of the project will be a model of the murine gut microbiota, with expectations to successfully scale up the size of the community and generalize the developped methods during the project.

The mission of this postdoc position is to develop methods suitable to the reduction of metabolic models for a community. The purpose is to simplify the metabolism of an ecoystem by targeting crucial functions and species that are main drivers of the ecosystem. The results will be used in mathematical models by the second postdoc scientist of the project.

Main activities

• Build high quality metabolic models of a simplified model of murine gut microbiota using state-of-the-art methods.
• Model them using qualitative and quantitative (Flux Balance Analysis) techniques.
• Develop methods to simplify a community of metabolic models using logic constraints and combinatorial optimisation.
• Characterize the main functions and interactions that drive the community
• Analyse results of metamodelling by identifying and visualising metabolic functions provided by the simulations
• Share the results of the projects through scientific publications and code/documentation distribution

Additional activities

• Collaborate with the second post-doc of the project by providing expertise on community-scale metabolic modeling. This expertise will be crucial to build the community scale problem and to analyse the results to identify the metabolic significance of the results.
• Participate in supervising students in the team.

Skills

Systems biology skills

• metabolic network modelling or skills in a close area.
• Python programming
• Data analysis: Python or R

Scientific writing

Languages

• English for scientific communition
• English or French for day to day work

Relational skills

• Ability to work in a collaborative environment
• Good communication skills (sharing results, supervising students)

Other valued appreciated: logic programming (e.g. Answer Set Programming) is a plus but is not mandatory.

Work Environment

The two recruited candidates will be members of the Pleiade team, a joint research group between Inria and INRAE, in the beautiful city of Bordeaux. Pleiade is an interdisciplinary group at the frontier of computer science, mathematics, bioinformatics and biology. One of our main research interests is to develop and validate new computational and numerical models for microbial ecology, that we dedicate to better understand the complex interactions occurring in complex communities of microorganisms known as microbiotas.

The position includes:

• Subsidized meals
• Partial reimbursement of public transport costs
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Do not hesitate to contact us for more information and details on the position.