Team, Visitors, External Collaborators
Overall Objectives
Research Program
Highlights of the Year
New Software and Platforms
New Results
Partnerships and Cooperations
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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 rather than the deterministic models underlying the linear inversion methods discussed in Section 6.1. 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 parametric intrinsic noise models, on the one hand, and the nonparametric inference of gene expression statistics, on the other hand, from population snapshot data. Along with modelling and inference, identifiability analysis is dedicated special attention. The investigation of the problem of reconstructing promoter activity statistics from reporter gene population snapshot data has led to a full-blown spectral analysis and reconstruction method for reporter gene systems. In the context of the ANR project MEMIP (Section 7.2), we have characterized reporter systems as noisy linear systems operating on a stochastic input (promoter activity), and developed an inversion method for estimation of promoter activation statistics from reporter population snapshots. The method has been demonstrated on simulated data. Theoretical as well as simulation resuts have been published in Automatica this year [15], and will be the object of application to real data.

One of the key limitations of the method is the assumption of stationary promoter activation statistics. In the context of controlled gene expression processes, this may hamper applicability of the method. In response to this, an extension of the method for so-called modulated processes (stationary processes reshaped by a time-varying control input), has been developed and demonstrated on simulations of controlled gene expression. Results were submitted for possible presentation and publication in the proceedings of the IFAC world congress 2020.