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 bacterial populations and even of single cells, with applications in for example biotechnology and medicine.

In the literature, much effort has been devoted to the analysis of stochastic gene expression models derived from biochemical kinetics and specific knowledge of the systems at hand. Less effort has been dedicated to developing general methods for inferring unknown parameter values of these stochastic models from single-cell experimental data. While some strategies have been proposed in the recent literature, no method of general applicability exists. IBIS recently started a new line of research dedicated to the study of stochastic modelling and identification of gene regulatory networks in single cells. This work, coordinated by Eugenio Cinquemani, focuses on simple network modules in bacterial cells. Our reference system is the regulation of the inset of arabinose uptake in E. coli upon depletion of glucose.

In the past year we developed a working method for the estimation of unknown network parameters of a simple stochastic model of the arabinose uptake process. The method was tested on simulated data and applied with success to time-lapse fluorescence microscopy data acquired by Guillaume Baptist. This application involved the development by Michel Page of a microscopy data processing program based on a customization of the freely accessible Matlab tool CellTracer. Preliminary results were presented in the poster session of the Conference on Stochastic Systems Biology held in Monte Verità (Switzerland). The work is currently being extended in preparation for a journal publication. A generalization of the method and the investigation of alternative stochastic modelling and identification methodologies are being pursued in parallel. Other ongoing work concerns the study of noise propagation in gene regulatory networks in collaboration with Irina Mihalcescu (Université Joseph Fourier).