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Estimation of stochastic gene expression dynamics in individual cells using fluorescence microscopy data

Offer for PostDoc position (duration up to 24 months)

Estimation of stochastic gene expression dynamics in individual cells using fluorescence microscopy data


Modeling the dynamics of gene expression is central in the study of the response of bacteria and other living organisms to various stimuli, and has a natural impact in applications such as control of biochemical industrial processes and drug development. Traditional modeling approaches describe gene expression kinetics at a population level by ODEs. However, there is now convincing evidence that stochasticity is inherent in gene expression and, together with inter-individual variability, plays a key role in living organisms.

Modern techniques for the observation of gene expression at the individual-cell level, such as time-lapse fluorescence microscopy, not only provide evidence for the variability of the process within and across individuals, but also enable one to infer probabilistic gene expression dynamics from in vivo experiments. Despite the attention devoted to the topic in the recent years, many fundamental and practical questions remain open and request further investigation.

This PostDoc proposal concerns the identification and real-time estimation of probabilistic gene expression dynamics in individual cells, based on fluorescence microscopy experiments providing measurements of gene expression at the single-cell level. With reference to simple biological networks, goals of the project are the development of application of methods for the identification of regulatory network models with stochastic dynamics (intrinsic noise) and/or other sources of uncertainty (extrinsic noise), and their use for the synthesis of "virtualsensors", i.e. real-time estimators of the state of unobserved intracellular quantitites.

The specific tasks that will be addressed will be among the following :

-  The development of stochastic dynamical gene expression models for small networks in E.coli and/or yeast, and their calibration from experimental data ;
-  The development of single-cell real-time state estimators ;
-  The application of the estimation algorithms to real data, and the evaluation of their performance.

The research activity 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 addition to the direct access to experimental data and biological support, the project will also 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.

Skills and Profile

Interested candidates should have a preparation in system/control theory (familiarity with dynamical systems and stochastic processes/probability theory). They should have working knowledge of scientific programming tools (Matlab, C++), as well as a reasonable understanding and/or a strong interest in biochemical regulatory networks. The successful candidate will be working in a multidisciplinary and international environment. Propensity to interaction and cooperation but also spirit of initiative are expected qualities of the candidates.

Some relevant bibliography

-  Paulsson, J. (2005) Models of stochastic gene expression. Phys. Life Rev., 2, 157-175.
-  Blake, W.J. et al. (2003) Noise in eukaryotic gene expression. Nature, 422, 633-637.
-  Rao, C.V. et al. (2002) Control, exploitation and tolerance of intracellular noise. Nature, 420, 231-237.
-  Rosenfeld, N. et al. (2005) Gene regulation at the single-cell level. Science, 307, 1962-1965.
-  Longo, D. and Hasty, J. (2006) Dynamics of single-cell gene expression. Mol. Syst. Biol., 2:64.
-  Cinquemani, E. et al. (2008) Stochastic dynamics of genetic networks : modelling and parameter identification. Bioinformatics, 24(23), 2748-2754.
-  Munsky, B. et al. (2009) Listening to the noise : random fluctuations reveal gene network parameters. Mol. Syst. Biol., 5:318.
-  Megerle, J.A. et al. (2008) Timing and dynamics of single cell gene expression in the arabinose utilization system. Biophysical Journal, 95:2103-2115.
-  Uhlendorf, J. et al. (2012) Long-term model predictive control of gene expression at the population and single-cell levels. PNAS, 109(35):14271-14276
-  Zechner, C. et al. (2012) Moment-based inference predicts bimodality in transient gene expression. PNAS, 109(21):8340-5
-  Milias-Argeitis, A. et al. (2011) In silico feedback for in vivo regulation of a gene expression circuit. Nature Biotechnology, 29:1114-1116


This position is offered at the INRIA Grenoble - Rhône-Alpes Research Unit of INRIA, located near Grenoble and Lyon. The Unit includes more than 600 people, within 34 research teams and 10 support services.

Application and additional information :

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

Interested candidates may contact :

Eugenio Cinquemani

Last modified February 26, 2014