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Ulysse Herbach, INRIA Nancy
| When |
Apr 15, 2022
from 11:00 to 12:00 |
|---|---|
| Contact Name | Salle des Thèses, Equipe Gandrillon |
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Contact : Olivier Gandrillon
Inferring regulatory networks from gene expression data is a longstanding question in systems biology. So far, most studies have been based on the bulk data paradigm: now that technologies enable to observe mRNA levels in individual cells, a revolution in terms of precision, the network reconstruction problem paradoxically remains more challenging than ever. Indeed, the traditional way of seeing a gene regulatory network as a deterministic system with some small external noise is being challenged by the probabilistic, bursty nature of gene expression revealed at single-cell level. Here we describe a strategy in which network inference is seen as a calibration procedure for a mechanistic model driven by transcriptional bursting: this model inherently captures the typical variability of single-cell data without requiring ad hoc external noise. This approach has three interesting features: it is computationally tractable with realistic numbers of cells and genes, it provides inferred networks with biological interpretability, and the underlying mechanistic model allows testable predictions to be made. This work is in collaboration with Elias Ventre, Thibault Espinasse, Gérard Benoit and Olivier Gandrillon.