A scalable gene network model of regulatory dynamics in single cells

TL;DR

This paper introduces FLeCS, a scalable differential equation-based model that captures gene regulatory dynamics in single cells, improving understanding of transcriptional responses to perturbations.

Contribution

FLeCS incorporates gene network structure into differential equations, enabling scalable and accurate modeling of single-cell transcriptional dynamics.

Findings

FLeCS accurately infers cell dynamics from pseudo-time series data.

FLeCS reveals transcriptional mechanisms in gene knockout experiments.

FLeCS simulates single-cell trajectories after drug perturbations.

Abstract

Single-cell data provide high-dimensional measurements of the transcriptional states of cells, but extracting insights into the regulatory functions of genes, particularly identifying transcriptional mechanisms affected by biological perturbations, remains a challenge. Many perturbations induce compensatory cellular responses, making it difficult to distinguish direct from indirect effects on gene regulation. Modeling how gene regulatory functions shape the temporal dynamics of these responses is key to improving our understanding of biological perturbations. Dynamical models based on differential equations offer a principled way to capture transcriptional dynamics, but their application to single-cell data has been hindered by computational constraints, stochasticity, sparsity, and noise. Existing methods either rely on low-dimensional representations or make strong simplifying assumptions, limiting their ability to model transcriptional dynamics at scale. We introduce a Functional and Learnable model of Cell dynamicS, FLeCS, that incorporates gene network structure into coupled differential equations to model gene regulatory functions. Given (pseudo)time-series single-cell data, FLeCS accurately infers cell dynamics at scale, provides improved functional insights into transcriptional mechanisms perturbed by gene knockouts, both in myeloid differentiation and K562 Perturb-seq experiments, and simulates single-cell trajectories of A549 cells following small-molecule perturbations.