Gene regulatory network inference from single-cell data using optimal transport

Abstract

Modelling gene expression is a central problem in systems biology. Single-cell technologies have revolutionised 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. Here we introduce GRIT — 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 that minimises the transport cost between consecutive time points.