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

Area of Science:

• Genomics
• Systems Biology
• Computational Biology

Background:

• Gene regulatory networks (GRNs) govern essential biological processes like development and disease.
• Characterizing GRNs across diverse cell types and states is a significant challenge.
• Single-cell omics technologies offer high-resolution biological system measurements.

Purpose of the Study:

• To review current methods for inferring GRNs from single-cell omic datasets.
• To focus on the inference of dynamic regulatory processes and responses to perturbations.
• To identify key challenges and future directions in the field.

Main Methods:

• Leveraging single-cell omics data (e.g., scRNA-seq).
• Applying computational inference algorithms.
• Analyzing dynamic and perturbation-based datasets.

Main Results:

• Single-cell omics enables unprecedented resolution for GRN inference.
• Computational methods provide insights into cell type-specific mechanisms and causality.
• Focus on dynamics and perturbations reveals complex regulatory behaviors.

Conclusions:

• Advances in single-cell omics and computational methods are transforming GRN inference.
• Further research is needed to address challenges in inferring dynamic and perturbed GRNs.
• Accurate GRN inference is crucial for understanding complex biological systems.