Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks

TL;DR

This paper reviews recent advances in computational methods for inferring gene regulatory networks from single-cell omics data, emphasizing dynamics, cell type specificity, and perturbations, and discusses remaining challenges.

Contribution

It provides a comprehensive overview of current approaches for GRN inference from single-cell data, highlighting recent progress and open challenges in the field.

Findings

Single-cell omics enables detailed GRN inference across cell types.

Current methods reveal dynamic and causal regulatory relationships.

Open challenges include data integration and modeling complexity.

Abstract

Gene regulatory networks (GRNs) define the regulatory relationships among molecules such as transcription factors, chromatin remodelers, and target genes. GRNs play a critical role in diverse biological processes, including development, disease manifestation, and evolution. However, fully characterizing these networks across multiple cell types and states remains a significant challenge. Recent advances in single-cell omics have dramatically enhanced our ability to measure biological systems at unprecedented resolution. These technologies have opened new avenues for computational methods to infer GRNs, offering deeper insights into cell type-specific mechanisms, causality, and dynamic regulatory processes. This review summarizes the current state of GRN inference from single cell omic datasets, with a particular focus on dynamics and perturbations, and outlines key open challenges that must be addressed to advance the field.