Dynamic gene regulatory network inference from single-cell data using optimal transport

Modelling gene expression is a central problem in systems biology. Single-cell technologies have revolutionized the field by enabling sequencing at the resolution of individual cells. This results in a much richer data compared to what is obtained by bulk technologies, offering new possibilities and challenges for gene regulatory network inference.

In this work, we introduce GRIT (gene regulation inference by transport)-a method to fit a differential equation model and to infer gene regulatory networks from single-cell data using the theory of optimal transport. The idea consists in tracking the evolution of the cell distribution over time and finding the system whose temporal marginals minimize the transport cost with the observations. GRIT is finally used to identify genes and pathways affected by two Parkinson's disease associated mutations.

Matlab implementation of the method and code for data generation are at gitlab.com/uniluxembourg/lcsb/systems-control/grit together with a user guide. A snapshot of the code used for the results of this article is at doi: 10.5281/zenodo.15582432.