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Ph.D. offer (Closed)

Modelling and estimation of gene expression variability in yeast cells from fluorescence microscopy data


Modeling the dynamics of gene expression is central in the study of the response of living organisms to various stimuli, and has a natural impact in applications as diverse as, for instance, control of biochemical industrial processes and drug development. Traditional modeling approaches describe gene expression kinetics at an average population level by ODEs. However, it is by now evident that gene expression does not occur identically across cells even if these share the same genome and are placed in the same environmental conditions. Rather, gene expression dynamics varies across cells (extrinsic noise) and even within the same cell (intrinsic noise), leading to a variety of behaviors for which average models are simply inadequate. Modern techniques for the observation of gene expression at the individual-cell level, such as time-lapse fluorescence microscopy, not only provide evidence for this variability but also enable one to infer gene expression dynamics in individual cells from in vivo experiments. Despite the attention devoted to the topic in the recent years, many questions remain open and motivate further investigation.

This Ph.D. proposal concerns the estimation of gene expression dynamics in individual cells from fluorescence microscopy experiments. Reconstructing (partially) unknown dynamics is interesting per se, as a means to understand and analyse the mechanisms that the cells implement to control their own functioning. In perspective, it provides a key block for addressing frontier challenges such as the computer-based control of single-cell behavior and the synthesis of novel biochemical circuits. In collaboration with team CONTRAINTES at INRIA Rocquencourt, we will especially focus on the expression in yeast of genes responsive to changes of environmental osmolarity via the so-called HOG pathway. Based on single-cell gene expression control experiments in microfluidic devices and several time-lapse physiological and fluorescent reporter gene-expression readouts, the following aspects are in the scope of the project :

-  Development of individual-cell gene expression models accounting for intrinsic and/or extrinsic noise ;
-  Development and application of methods for the identification of the above models ;
-  Investigation of structural network identification from single-cell data ;
-  Real-time estimation of unobserved gene expression dynamics ;
-  Participation to design and execution of biological (wetlab) single-cell experiments.

The research activity will start by the familiarization of the student with the existing literature on modelling, inference and control of stochastic biochemical network dynamics. It will be developed within the IBIS group, which includes applied mathematicians, computer scientists, biologists, and modelers, at the INRIA Grenoble - Rhône-Alpes center located in Montbonnot, in tight collaboration with group LIFEWARE at INRIA Rocquencourt. In addition, the project will profit from ongoing collaborations of IBIS with a number of national and international research institutions and the participation of the group in various research projects.


Interested candidates should have a solid mathematical background with an understanding of dynamical systems, probability and stochastic processes, a strong interest in biology and biochemical regulatory networks, and practical knowledge of scientific programming tools (Matlab and/or C++, or the like). The successful candidate will be working in a multidisciplinary and international environment. Propensity to interaction and cooperation are expected qualities of the candidates.

Relevant bibliography

M. B. Elowitz et al., "Stochastic gene expression in a single cell". Science, 2002.

H. El Samad et al., "Stochastic modelling of gene regulatory networks". Intl J Rob Nonl Control, 2005.

B. Munsky et al., "Listening to the noise : random fluctuations reveal gene network parameters". Molecular Systems Biology, 2009.

C. Zechner et al., "Moment-based inference predicts bimodality in transient gene expression". PNAS, 2012.

J. Uhlendorf et al., "Long-term model predictive control of gene expression at the population and single-cell levels". PNAS, 2012.

A. Milias-Argeitis et al., "In silico feedback for in vivo regulation of a gene expression circuit". Nature Biotechnology, 2011.

E. Cinquemani et al., "Stochastic dynamics of genetic networks : modelling and parameter identification. Bioinformatics, 2008.

A. Carta and E. Cinquemani, "State estimation for gene networks with intrinsic and extrinsic noise : A case study on E.coli arabinose uptake dynamics". Proceedings of ECC, 2013.

A. M. Gonzalez, "Identification of biological models from single-cell data : a comparison between mixed-effects and moment-based inference". Proceedings of ECC, 2013.

G. Neuert et al., "Systematic Identification of Signal-Activated Stochastic Gene Regulation". Science, 2013.

C. Zechner et al., "Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings". Nature Methods, 2014.

Application and additional information

Application deadlines, details of the application procedure and additional information on Ph.D. positions at INRIA (eligibility, salary...) are reported on the INRIA official website.


Candidates who intend to apply are encouraged to contact Eugenio Cinquemani at eugenio.cinquemani@inria.fr

Last updated March 26, 2014