Learning stochastic mixed-effects models of gene expression dynamics from individual microbial cell data

Start date: As soon as possible

Duration: 4-6 months

Modern experimental technologies allow one to monitor the response of microbial organisms to environmental stimuli at the level of individual cells. Observation of single-cell dynamics reveals the variability of this response across different cells, as well as the stochasticity of the dynamics in terms of fluctuations over time within the same cell. Quantitative understanding of this variability is key to investigate the origins of randomness in gene expression, as well as its functional role for the survival and proliferation of microbial populations in changing environments. Development of methods for the reconstruction of quantitative models of single-cell response is thus an active research field.

This proposal concerns the implementation of a new method for the identification of Mixed-Effects models of gene expression dynamics from single-cell data. Contrary to existing approaches, where standard ME modelling is used to describe variability across cells in terms of parameter variability of a deterministic response, we will capture random fluctuation of any given cell response in terms of stochastic dynamics. Starting from existing inference algorithms for standard ME models, the internship will address the integration of a novel step accounting for these stochastic dynamics in a computationally effective manner.

In more detail, the project will consist of the following steps:

– Familiarization with an existing algorithm for the inference of ME models
– Extension of the code with functions accounting for the stochastic dynamics
– Testing of the resulting algorithm on simulated data (and real data, if time allows)
– Reporting

The intership will be carried out under the joint supervision of Eugenio Cinquemani (IBIS, INRIA Grenoble – Rhône-Alpes) and Jakob Ruess (INBIO, INRIA Saclay – Île-de-France and Institut Pasteur Paris), in collaboration with Marc Lavielle (XPOP, INRIA Saclay – Île-de-France and CMAP, Ecole Polytechnique) in the context of the ANR project MEMIP. Depending on the candidate, it will be based in Grenoble or Paris.

Interested candidates are expected to be familiar with Matlab programming, have a reasonable understanding of dynamical systems/probability theory/stochastic processes, and be interested in biology. Basic English speaking is required. Successful candidates will be working in collaboration in a stimulating international context. Standard internship remuneration will be provided. Please contact eugenio.cinquemani@inria.fr for more information and application.

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