Olivier Gandrillon

Gene Regulatory Network Inference reveals tcf4 as a key a player in neuroblastoma gene expression circuitry

Mechanistic stochastic gene regulatory network inference on neuroblastoma tumoroid scRNA-seq predicts TCF4 as a hub whose loss slows chromaffin-to-sympathoblast progression, supported by JQ1 treatment ex vivo.

Inferring and simulating a gene regulatory network for the sympathoadrenal differentiation from single-cell transcriptomics in human.

The paper infers and simulates a gene regulatory network for sympathoadrenal differentiation using single-cell RNA-seq data from human cells, identifying a 97-gene network that drives the transition from Schwann cell precursors to chromaffin cells and sympathoblasts.

Cell Trajectory Inference based on Optimal Transport and a mechanistic stochastic gene expression model

This paper introduces a method for cell trajectory inference using optimal transport with a mechanistic stochastic gene expression model, showing the Bursty model better captures gene dynamics than the Diffusive model.

Multiscale modeling of the spatial structure of stem cells in neuroblastoma patient-derived tumoroids reveals a critical role for a short-range diffusive process

This study uses a multiscale agent-based model to show that short-range diffusive signaling among stem cells is critical for reproducing the spatial structure of neuroblastoma tumoroids.

A temporary challenge by tumor cells can lead to a permanent partial-impairment of memory CD8 T cell function

This study compares memory CD8 T cells generated from tumor rejection and acute viral infection, finding that tumor-induced cells show persistent functional defects similar to exhaustion.

MYC shapes ER-mitochondria calcium transfer by directly targeting ITPR1: implications for MYC-induced safeguard mechanisms and cancer

This study shows MYC regulates ITPR1 to control ER-mitochondria calcium transfer, inducing cell death in normal cells but exploited by cancer cells for survival.

Modeling relaxation experiments with a mechanistic model of gene expression

The paper models relaxation experiments using a mechanistic gene expression model with a proliferation term, showing good agreement with experimental data on CD34 antigen distribution.

TopoDoE: A Design of Experiment strategy for selection and refinement in ensembles of executable Gene Regulatory Networks

The paper introduces a Design of Experiment strategy to refine Gene Regulatory Networks by selecting informative perturbations and validating predictions against experimental data.

Multiscale chromatin dynamics and high entropy in plant iPSC ancestors

The paper investigates chromatin dynamics in plant protoplasts during dedifferentiation, finding rapid multiscale changes influenced by nutrients and a decline in chromatin entropy over time.

Stochastic modeling of a gene regulatory network driving B cell development in germinal centers

The paper uses a stochastic model to fit single-cell transcriptomic data and shows it can recapitulate mRNA distributions during B cell differentiation in germinal centers.

In silico modelling of CD8 T cell immune response links genetic regulation to population dynamics

This study models CD8 T cell immune response using a multiscale agent-based approach to link genetic regulation to population dynamics.

scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data

scAN1.0 is a reproducible pipeline for processing 10X single cell RNAseq data, demonstrating its utility in analyzing human and murine datasets.

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

The paper uses CALISTA to reconstruct transcriptional uncertainty landscapes from single-cell data, revealing universal patterns in cell differentiation trajectories.

One model fits all: combining inference and simulation of gene regulatory networks

This paper combines two methods to use a single gene regulatory network model for both inference and simulation of transcriptional profiles from time-course single-cell data.

Hematopoietic differentiation is characterized by a transient peak of entropy at a single cell level.

The paper uses single-cell data to show that hematopoietic differentiation involves a transient peak of gene expression variability, suggesting a stochastic process with a stage of cellular indetermination.

Reduction of a stochastic model of gene expression: Lagrangian dynamics gives acces to basins of attraction as cell types and metastability

The paper reduces a stochastic gene expression model to a discrete coarse-grained model, using Lagrangian dynamics to identify basins of attraction as cell types and calculate metastability.

Practical Identifiability in the Frame of Nonlinear Mixed Effects Models: the Example of the in vitro Erythropoiesis

The paper assesses practical identifiability in nonlinear mixed effects models using a multistart approach to simplify a model of chicken erythropoiesis and improve parameter estimation.

Drugs modulating stochastic gene expression affect the erythroid differentiation process

The paper identifies drugs that modulate stochastic gene expression and shows their impact on erythroid differentiation.

WASABI: a dynamic iterative framework for gene regulatory network inference

WASABI infers causal gene regulatory networks from time-stamped single-cell data, revealing a distributed network structure without hub genes in avian erythroid differentiation.

Calibration, Selection and Identifiability Analysis of a Mathematical Model of the in vitro Erythropoiesis in Normal and Perturbed Contexts

The paper develops a mathematical model of in vitro erythropoiesis, identifies its parameters, and evaluates the impact of Rapamycin on cellular differentiation.

Deep proteomic analysis of chicken erythropoiesis

This paper analyzes the chicken erythroblast proteome during terminal differentiation, revealing both conserved and divergent protein expression patterns compared to human erythroblasts.

SINCERITIES: Inferring gene regulatory networks fromtime-stamped single cell transcriptional expression profiles

SINCERITIES infers gene regulatory networks from time-stamped single-cell expression data, showing better accuracy than existing methods in simulated and experimental datasets.