- Meeting ID: 867 6409 6440
- passcode: 149120
13h00 - 13h50 – Alexandre TAN-LHERNOULD (INRIA/Servier)
GRNgene: a Parametric Generator of Synthetic Gene Networks
ABSTRACT Gene Regulatory Networks (GRNs) constitute a useful abstraction of complex molecular mechanisms responsible for cell behaviors, cell differentiation, and various diseases. Advances in high-throughput transcriptomics, such as single cell RNA sequencing data, have enabled unprecedented access to large-scale gene expression data. However, the manual reconstruction of GRNs from such data and literature is a hard task, and the automatic reconstruction by GRN inference algorithms remains a major challenge to analyze and validate. Previous work revealed that the performances of GRN inference algorithms vary significantly according to network topology and motif preponderance. During this seminar, I will present a tool to generate synthetic GRNs which can be used to simulate gene expression data in order to evaluate GRN inference algorithms and drive future developments. GRNgene aims to reproduce key network properties such as scale free topology and specific 3-gene motifs preponderance. I will illustrate the calibration of GRNgene and compare the generated networks to currently known GRNs to show the capability of GRNgene to synthesize realistic GRNs.
Dernière modification le 03/10/2025