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Probabilistic modelling of gene expression dynamics in individual cells using fluorescence microscopy data


Offer for PostDoc position (16 months)

Probabilistic modelling of gene expression dynamics in individual cells using fluorescence microscopy data

Description

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 models 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 inference, analysis and use of probabilistic models of gene expression dynamics in individual cells from fluorescence microscopy data. In connection with real-world gene network case studies, goals of the project are the development of methods and tools for the analysis and identification of regulatory network models with stochastic dynamics (intrinsic noise) and/or other sources of uncertainty (extrinsic noise), as well as the use of these models and methods for the investigation of the biological systems and for control-related applications.

Depending on the candidate’s interests and the development of the project, one or more of the following aspects will be addressed :

-  The development, identifiability analysis, and identification of single-cell models of gene regulatory networks, in Escherichia coli bacteria or in other organisms (e.g. yeast), based on experimental data ;
-  The analysis of the models in terms of noise in the dynamics and other sources of variability, and the comparison of different modelling alternatives ;
-  The processing of single-cell fluorescence microscopy data ;
-  The contribution to single-cell experiments carried out in the experimental lab of Prof. Hans Geiselmann (part of IBIS) at the Université Joseph Fourier in Grenoble.

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

Environment

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 March 2, 2013