Current experimental technologies for the time-lapse monitoring and control of cellular processes in microorganisms have enabled automatic control of the dynamics of microbial cells around desired behaviors, with exciting research and even biotechnological perspectives. Several works have recently appeared [1,2] where, in particular, growth and gene expression of microbial populations are to follow prespecified profiles. This is achieved by computer-operated platforms variously combining flow-cytometry, videomicroscopy, chemical induction, optogenetics, in a feedback control scheme. In general, though, only the mean behavior of a homogeneous population of cells is regulated, whereas heterogeneity associated with phenotypic diversity is largely neglected.
In principle, the ability to experimentally monitor microorganisms at the individual-cell level allows one to address more complex problems where the different phenotypes of different cells, or even the genotypic diversity of interacting species, is explicitly taken into account to optimally control the behavior of a heterogeneous microbial community. However, this requires a quantitative understanding of the growth, interaction dynamics, and response to perturbations of physiologically different subpopulations in a shared environment [3,4,5].
In the context of the Inria Project-Lab “COSY- Real-time COntrol of SYnthetic microbial communities”, this PostDoc proposal addresses the problem of modelling the dynamics of heterogeneous bacterial populations, as well as generalizations to microbial ecosystems composed of several species.
In a first phase, the project will focus on the dynamical modelling of Escherichia coli populations comprising two different but interacting phenotypes. Starting from population dynamics such as generalized Lotka-Volterra models, we will consider extensions including simple descriptions of individual cell metabolism and study the population dynamics emerging from the interaction of the different pheotypes in response to environmental perturbations. Models will be developed based on existing data (dynamic metabolomic experiments, gene reporter profiles) and will profit from the collaboration with experimental biologists at LIPhy (Université Grenoble-Alpes, including members of IBIS).
In a second phase, models developed for heterogeneous E.coli populations will be generalized to models of microbial communities comprising several interacting subpopulations of possibly different species. Heterogeneous population models will be extended to arbitrary number of subpopulations and alternative interaction patterns, and will be applied to the study of specific microbial ecosystems, in particular, the gut microbiota responsible for fiber degradation.
This interdisciplinary project is at the frontiers of current research in systems biology. Funding is available from the Inria Project-Lab COSY for a duration of 18 months. The project will be based at Inria Grenoble – Rhône-Alpes within project-team IBIS (https://team.inria.fr/ibis/), and will be developed in collaboration with the project-team BIOCORE (Inria Sophia – Antipolis) and MaIAGE (INRA Jouy-en-Josas). The project should start during fall 2017.
Interested candidates should have a preparation in dynamical systems/control theory, and familiarity with biological modelling. Knowledge of probability theory, estimation theory, microbial ecology constitute a plus.
The successful candidate will be working in an international environment and is expected to be open to collaboration with applied mathematicians, biologists, and modelers. Prior to application, candidates are invited to contact Eugenio Cinquemani, firstname.lastname@example.org .
Official application must be submitted via the Inria recruitment site at
(please locate and follow the PostDoc proposal with the same title in the list of offers) where more information about eligibility, salary etc. are provided.
 J .Uhlendorf, A. Miermont, T. Delaveau, G. Charvin, F. Fages, S. Bottani, G. Batt, P. Hersen. Long-term model predictive control of gene expression at the population and single-cell levels. PNAS, 109(35):14271-6, 2012
 A. Milias-Argeitis, M. Rullan, S.K. Aoki, P. Buchmann, M. Khammash. Automated optogenetic feedback control for precise and robust regulation of gene expression and cell growth. Nat Commun 7:12546, 2016.
 J. Izard, C. Gomez Balderas, D. Ropers, S. Lacour, X. Song, Y. Yang, A.B. Lindner, J. Geiselmann, H. de Jong. A synthetic growth switch based on controlled expression of RNA polymerase. Molecular Systems Biology, 11(11):840, 2015
 A. Llamosi, A. Gonzalez, C. Versari, E. Cinquemani, G. Ferrari-Trecate, P. Hersen, G. Batt. What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast. PLoS Computational Biology, 12(2):e1004706, 2016
 Widder et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. The ISME Journal, 10:2557-2568, 2016