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Section: New Results

Stochastic modeling and identification of gene regulatory networks in bacteria

At the single-cell level, the processes that govern single-cell dynamics in general and gene expression in particular are 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 ranges from a better comprehension of the biochemical regulatory mechanisms underlying cellular phenotypes to the development of new strategies for the (computer assisted or genetically engineered) control of cell populations and even of single cells.

Work in IBIS on gene expression and interaction dynamics at the level of individual cells is addressed in terms of identification of intrinsic noise models from population snapshot data, on the one hand, and the inference of models focusing on cellular variability within isogenic populations from fluorescence microscopy gene expression profiles, on the other hand. Along with modelling and inference comes analysis of the inferred models in various respects, notably in terms of identifiability, single-cell state estimation and control. Other problems related with single-cell modelling and extracellular variability are considered in eukaryotic cells through external collaborations.

Concerning identification of intrinsic noise dynamics in single cells, previous results on the contribution of stochasticity to parameter identifiability have been revisited in the context of reconstruction of unknown networks. For the case of population snapshot meaurements, where the dynamics of the population statistics are observed by simple time-lapse experiments, we performed an analytical study of the additional information provided by variance measurements for the reconstruction of unknown first-order kinetics. Based on simulated example, we showed that a tremendous improvement in network reconstruction is achieved relative to the utilization of population-average statistics alone, as addressed by deterministic modelling. These exciting yet preliminary results were published in the form of a paper in the proceedings of the IFAC World Congress [22] and will be further developed.

Reconstruction of promoter activity statistics from reporter gene population snapshot data has been further investigated, leading to a full-blown spectral analysis and reconstruction method for reporter gene systems. Building upon reults in previous conference papers, in the context of the ANR project MEMIP (Section 8.2), we have characterized reporter systems as noisy linear systems operating on a stochastic input (promoter activity), and developed an inversion method for nonparametric estimation of promoter dynamics, namely the autocovariance function, from the considered readouts. These theoretical and simulation results have been submitted for journal publication and are also available as an arXiv pre-print. The method will be further developed and applied to real data and case studies.

Modelling of heterogeneity in isogenic cell populations is also an active research direction. Still in the context of MEMIP, in collaboration with the INBIO team, we are considering generalizations of our achievements on Mixed-Effects modelling and inference on yeast, in order to account for different sources of noise and lineage effects. As an offspring of this work, a study of inter-individual variability of E. coli gene expression and growth rate in growth arrest-and-restart experiments has been carried out with BIOCORE. Results obtained so far are part of the PhD thesis of Stefano Casagrande.