PhD Position F / M Development and validation of small-scale models of metabolite production in[...]

INRIA; Montbonnot-Saint-Martin; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Un projet de doctorat au sein d'une équipe interdisciplinaire à INRIA invite les candidats à développer des modèles dynamiques pour optimiser la production de 1,3-propanediol. Ce projet inclut à la fois des travaux théoriques sur la modélisation mathématique et des expériences pratiques en laboratoire, visant à immerger les candidats dans un environnement de recherche innovant et collaboratif.

Prestations

Remboursement partiel des frais de transport public

7 semaines de congés annuels + 10 jours RTT

Possibilité de télétravail et horaires flexibles

Équipement professionnel disponible

Événements sociaux, culturels et sportifs

Accès à la formation professionnelle

Couverture de sécurité sociale

Qualifications

• Expérience en modélisation mathématique des systèmes biologiques souhaitée.
• Connaissance de la microbiologie ou biologie mathématique appréciée.

Responsabilités

• Développement de modèles pour optimiser la production de 1,3-PDO.
• Réalisation d'expériences sur mini-bioréacteurs pour la calibration des modèles.
• Utilisation de l'optimisation et simulations pour identifier les conditions optimales.

Connaissances

Modélisation mathématique

Biologie

Expérience en microbiologie

Recherche interdisciplinaire

Formation

Diplôme en biologie, biophysique ou domaine connexe

Description du poste

The PhD project will be carried out in the project-team MICROCOSME at the Centre Inria de l’Université de Grenoble Alpes and the Laboratoire Interdisciplinaire de Physique (LIPhy, CNRS / UGA) under the joint supervision of Hidde de Jong (

The project MUlti-SIze Hybrid Cell Models (MuSiHC) aims at developing novel hybrid approaches to the modeling of cells and bioreactors for the production of added-value compounds. In particular, the project will develop a toolkit of hybrid models of different sizes, combining a mechanistic description with AI / ML components [1], to obtain more reliable cell and bioreactor simulations. As a proof of concept, the project will focus on Escherichia coli as a platform for the bioproduction of 1,3-propanediol (1,3-PDO), a high-value compound with vast applications in the chemical industry.

The proposed PhD project is concerned with the development of a small-scale, dynamic models [2,3] for optimizing the production of 1,3-PDO by E. coli, involving such tasks as model formulation and reduction, running mini-bioreactor experiments for model calibration, using the models to identify conditions for optimal metabolite production, and the experimental test of these conditions. The PhD project involves active collaboration with other MuSiHC partners at INRAE (Jean-Loup Faulon, Wolfram Liebermeister) and Toulouse Biotechnology Institute (César Arturo Aceves Lara). Beyond the specific application of MuSiHC, the project aims at identifying general principles for the development and validation of small-scale models of biotechnological production systems.

1] Faure, L., Mollet, B., Liebermeister, W., & Faulon, J. L. A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models. Nature Communications, 14 : 4669. [3] Wortel, M. T., Noor, E., Ferris, M., Bruggeman, F. J., & Liebermeister, W. Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield. PLoS Computational Biology, 14 : e1006010.

The PhD project is an interdisciplinary project involving both the development of mathematical models describing the biological system and experimental work to calibrate and validate the models :

• Reduction of a medium-scale, kinetic model to a small-scale, whole-cell model of the production of 1,3-propanediol (1,3-PDO) by Escherichia coli, using previously developed reduction methods and taking inspiration from existing small-scale resource allocation models.
• Performance of experiments with selected E. coli strains on an in-house mini-bioreactor platform to obtain data (growth, gene expression, metabolite concentrations) for the calibration of the model.
• Use a combination of optimization and simulation approaches to identify conditions maximizing 1,3-PDO production.
• Validation of the predicted optimal operating conditions by performing the corresponding mini-bioreactor experiments, including the quantification of 1,3-PDO production.

Interested candidates are ideally expected to have some experience with the mathematical modelling of biological systems and / or laboratory work in microbiology, but we are open to consider students from a range of fields (microbiology, mathematical biology, ecology, biophysics, …) with good scholarly results and motivated by interdisciplinary research.

Avantages

• Partial reimbursement of public transport costs
• Leave : 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

2200 gross salary / month