Offres

Post-doctoral position - Mathematical modeling of Bacteria colonization

Contract type : Fixed-term contract 

Duration : 1 year and a half 

Renewable contract : No 

Level of qualifications required : PhD or equivalent 

Fonction : Post-Doctoral Research Position 

Location : LJLL, Sorbonne Université 

Context and Location

This position is in the frame of the project 'Towards a realistic model for biofilm formation' funded by the Institut des Mathématiques pour la Planète Terre. The project involves an interdisciplinary collaboration between Sophie Hecht, Diane Peurichard (LJLL, Sorbonne Université) and Nicolas Desprat (LPENS, Ecole Normale Supérieure), and aims to develop realistic mathematical models  to understand the main mechanisms of biofilm formation. The recruited person will be hosted at LJLL, Sorbonne Université, with strong interactions and exchanges with the mentioned collaborators. The expected starting date can be as soon as possible.

Short description

Bacteria colonize surfaces to settle sessile colonies called biofilms. Biofilm formation is crucial in industrial processes, in animal health and in biotechnological applications. Biofilm formation has been extensively studied and modeled in-silico using rigid spherocylinders [1]. However, experimental data have shown that bacteria can significantly bend in order to maximize cell-cell interactions in the colony. In this project, we propose to model the local deformations of bacterial skeletons and determine how these effects impact biofilm density. We will perform mathematical modeling, and run in-vitro experiments to calibrate and validate the model. On the mathematical viewpoint, we will develop an individual-based model composed of a minimal set of heuristic (mechanical) rules to identify the key factors required to recover realistic structured patterns observed in bacterial populations. The project will be highly interdisciplinary, as the mathematical model will be systematically confronted to experimental data generated in N. Desprat's lab in order to validate the model assumptions, and in turn the model might lead to testable experimental predictions. 

The postdoc will participate in the development of the model as well as its calibration and validation on experimental data. Extension of the project to either 3D modeling on curved surface or micro-macro limit toward a continuous model will be considered.

Main Activities

Specifically, the following aspects will be considered:

Throughout the post-doctoral period, frequent visits between the various members of the projects will be encouraged. Dedicated fundings will be allocated to these visits and to participation to workshops/conferences to disseminate the results of the project.

Skills

The candidate should hold a PhD in mathematics, physics or other relevant fields. We are looking for a highly motivated candidate with a strong background in mathematical modelling, computational physics / numerics, in particular if in the fields of classical mechanics, bio-physics, soft matter or granular matter, and a deep interest in biological applications. Proven experience in a programming language (Fortran90, C++, matlab and / or  python) is required. The ideal candidates are able to work effectively as part of a team, but also to develop and pursue independent ideas. Experience in modelling and applications in biology is highly recommended and collaboration with other people, best cross-disciplinary will be highly appreciated. Previous experience with academic environment and software is an advantage.

Technical skills and level required :

Relational skills :

Instruction to apply

To apply, candidates should send the following documents by email to sophie.hecht@sorbonne-universite.fr and  diane.a.peurichard@inria.fr

Documents:

References

[1] M. Doumic, S. Hecht, D. Peurichard, A purely mechanical model with asymmetric features for early morphogenesis of rod-shaped bacteria micro-colony, Math. Biosci. and Eng., (2020), 17(6): 6873-6908

[2] S.M. Lundberg, S-I Lee, "A Unified Approach to Interpreting Model Predictions". Advances in Neural Information Processing Systems. (2017) 30: 4765–4774