European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Joint trajectory and gene regulatory network inference using a mechanistic optimal transport-based framework

MS114-02

14 Jul 2026, 11:00

20m

15.06 - HS (University of Graz)

Minisymposium Talk Numerical, Computational, and Data-Driven Methods Multiscale modelling and simulation of stochastic gene regulation

Speaker

Elias Ventre (INRIA Centre d'Université Côte d'Azur)

Description

A key challenge in inferring gene regulatory networks (GRNs) governing cellular processes, such as differentiation and reprogramming, from experimental data lies in the impossibility of directly observing protein trajectories at the single-cell level, which prevents establishing causal relationships between regulator activity and target responses.
In this talk, we present CardamomOT, a new algorithm that uses temporal snapshots of scRNA-seq data to calibrate a mechanistic model of gene expression \cite{mauge2026}. The method reconstructs both the GRN and the unobserved protein trajectories using an innovative mechanistic optimal transport framework. We present some results on both in silico and experimental datasets, demonstrating the ability to accurately recovers velocity fields driving cellular trajectories and unobserved protein levels, alongside reliable GRN structures. We finally show that the calibrated mechanistic model can be used as a generative model to predict cellular responses to unseen perturbations.

Bibliography

@article{mauge2026,
title={{CardamomOT}: a novel mechanistic optimal transport-based framework for joint gene regulatory network and trajectory inference},
author={Mauge, Yann and Ventre, Elias},
journal={bioRxiv},
pages={2026--13},
year={2026},
publisher={Cold Spring Harbor Laboratory}
}