Section: New Results

Stochastic modeling and identification of gene regulatory networks in bacteria

At the single-cell level, the processes that govern gene expression are often better described by stochastic models. Modern techniques for the real-time monitoring of gene expression in single cells enable one to apply stochastic modelling to study the origins and consequences of random noise in response to various environmental stresses, and the emergence of phenotypic variability. The potential impact of single-cell stochastic analysis and modelling is tremendous, ranging from a better comprehension of the biochemical regulatory mechanisms underlying life, to the development of new strategies for the control of cell populations and even of single cells. General modeling paradigms, such as the Chemical Master Equation, exist for the description of stochastic dynamics at the single-cell level. However, due to the complexity of the interactions, current studies have often preferred to focus on specific cases of interest by ad-hoc modeling and analysis. In addition, theoretical and practical challenges inherent in the inference of stochastic models from biological experimental data have limited the development of general identification approaches.

In view of the potential and the relevance of the subject, one research line of IBIS is dedicated to the probabilistic modeling of the dynamics of gene regulatory networks at the level of individual cells. Our activity is centered around two main challenges. On the one hand, we address the problem of developing methods for fitting unknown network parameters of stochastic models to experimental data. As a reference case study we consider the network regulating the inset of the arabinose uptake process in E. coli upon depletion of glucose in the growth medium. For this system, Eugenio Cinquemani and Michel Page are developing and implementing methods for the inference of unknown parameters from fluorescence microscopy data. On the other hand, we investigate several alternative modelling approaches in an attempt to determine their relevance to different systems and application scenarios. This activity is being developed in collaboration with Gregory Batt (CONTRAINTES, Inria Paris-Rocquencourt), Giancarlo Ferrari-Trecate (University of Pavia, Italy), and Alfonso Carta (COMORE, Inria Sophia-Antipolis - Méditerranée). First results connected to control applications on real and simulated data have been submitted for presentation at the European Control Conference to be held in 2013. Finally, further ongoing work concerns the study of noise propagation in gene regulatory networks, in collaboration with Irina Mihalcescu (Université Joseph Fourier), and the analysis of data from Fluorescence Recovery After Photobleaching (FRAP) experiments, in collaboration with Marianna Rapsomaniki and Zoi Lygerou (University of Patras, Greece) and John Lygeros (ETH Zürich, Switzerland).