Elias Ventre

GSET: Gene Signals from Embedding Trajectories select compact contexts for mechanistic GRN inference

GSET retrieves dynamic gene signals to improve mechanistic GRN inference by identifying bridge genes involved in cell-state transitions.

CardamomOT: a mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling

CardamomOT improves gene regulatory network inference and trajectory reconstruction by integrating mechanistic models with optimal transport, using exact time labels and reducing hyperparameters.

Global StationaryOT: Trajectory inference for aging time courses of single-cell snapshots

This paper introduces a method to infer cell trajectories from aging time courses, improving accuracy in noisy data by leveraging temporal information.

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.

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.

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.

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics

The paper introduces CARDAMOM, a method for inferring Gene Regulatory Networks from timestamped scRNA-seq data by leveraging metastability and transcriptional bursting.