PhD Position F/M Development of control strategies for synthetic microbial consortia (funded PhD offer)
INRIA
Montbonnot, France
il y a 3j

Contexte et atouts du poste

The project is in the context of the ANR project Ctrl-AB (https : / / anr.fr / Projet-ANR-20-CE45-0014). It is focused on theoretical and applied control in a highly interdisciplinary context.

Based in Grenoble within the Inria project-team IBIS, it will be co-supervised by Eugenio Cinquemani (IBIS, https : / / team.

inria.fr / ibis / eugenio-cinquemani / ) and Jean-Luc Gouzé (BIOCORE, Inria Sophia-Antipolis, http : / / www-sop.inria.fr / members / Jean-Luc.

Gouze / JLGouze-fra.html), both members of the ANR project CtrlAB. It will profit from the control-theoretic and systems-biology expertise of IBIS and BIOCORE, as well as from the interaction with other experimental partners of CtrlAB.

Mission confiée

In nature, microorganisms mostly occur in communities of different competing and / or cooperating species We,Ko . These interactions represent a challenge that goes beyond the characterization of single species, and a great opportunity for applications.

Current experimental monitoring and bioengineering capabilities lay the ground not only for the quantitative understanding of natural communities, but also for the synthesis of artificial consortia and their control for e.

g. waste treatment and biofuel production Sh . At present, full exploitation of microbial consortia presents great challenges ranging from appropriate modelling methods to design and deployment of real-time control systems Zo .

This funded Ph.D. proposal concerns the analysis, development and application of feedback control approaches for microbial communities.

The project is in the context of the ANR project Ctrl-AB, whose objective is the design, realization and automated control of an algal-bacterial consortium for the optimized synthesis of target proteins in lab-scale bioreactors Ma,Bar .

With reference to mathematical (ODE-type, nonlinear) models describing the biosynthesis process resulting from this algal-bacterial consortium, the Ph.

D. project will explore state-of-the-art feedback control approaches Ram,Do,Bas,Fi as well as novel directions in the control of microbial communities Fo,Ca,Tr by a combination of theoretical analysis and computer simulation.

In more detail, the Ph.D. project will be articulated along the following points :

1. Familiarization with existing literature on biochemical process control, and on the dynamical modelling of microbial communities

2. Controllability analysis of the algal-bacterial consortium models

3. Development, analysis and simulation of state-of-the-art feedback control methods for several algal-bacterial consortium control problems (regulation, real-time maximization of productivity, ...)

4. Exploration of data-driven control techniques and performance comparison with the approaches in 3.

5. Application to in-vivo experiments on automated platforms

We S.A. West, G.A. Cooper, Division of labour in microorganisms : an evolutionary perspective . Nature Rev Microbiol, 14(11) : 716 723, 2016

Sh J Shong et al., Towards synthetic microbial consortia for bioprocessing . Curr Opin Biotechnol, 23(5) : 798 802, 2012

Ko A Konopka et al., Dynamics in microbial communities : unraveling mechanisms to identify principles . ISME J, 9(7) : 1488 1495, 2015.

Zo A.R. Zomorrodi, D. Segrè, Synthetic ecology of microbes : mathematical models and applications . J Mol Biol, 428(5) : 837 861, 2016

Bar C. Baroukh et al., DRUM : A New Framework for Metabolic Modeling under Non-Balanced Growth. Application to the Carbon Metabolism of Unicellular Microalgae .

PLoS ONE, 9(8) : 1 15, 2014.

Ram S. Ramaswamy et al., Control of a continuous bioreactor using model predictive control . Process

Biochem, 40(8) : 2763 2770, 2005.

Do D. Dochain et al., Extremum seeking control and its application to process and reaction systems : A

survey . Math Comput Simulat, 82(3) : 369 380, 2011

Bas G. Bastin, D. Dochain. On-line Estimation and Adaptive Control of Bioreactors. Elsevier, 1990

Fi D.Fiore et al., "Feedback ratiometric control of two microbial populations in a single chemostat". bioRxiv, 2020. doi : https : / / doi.

org / 10.1101 / 2021.03.05.434159

Tr N.J. Treloar et al., Deep reinforcement learning for the control of microbial co-cultures in bioreactors . PLoS Comput Biol, 16(4) : e1007783, 2020

Ca L. Campestrini et al., Data-driven model reference control design by prediction error identification . J

Franklin Inst, 354(6) : 2628 2647, 2017

Fo S. Formentin et al., Direct learning of LPV controllers from data . Automatica, 65 : 98 110, 2016.

Principales activités

The main activities are those typical of interdisciplinary reserch. They include : literature reading, scientific development, programming and simulation, data processing, reporting and presentation, paper and thesis manuscript writing, collaboration with the team, the supervisors and other scientific partners, participation to conferences and workshops.

Course-taking and teaching activities in accordance with doctoral school rules.

Compétences

The interested candidate should have a solid preparation in mathematical analysis (dynamical systems) and control theory, as well as familiarity with or strong interest in biology / biotechnological applications.

Some knowledge of machine learning is a plus. He / she will be working in a collaborative and international environment, and is thus expected to be open to scientific interaction, and to be proficient in English.

Avantages

  • Subsidized meals
  • 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 (after 6 months of employment) 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
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