Estimating drivers of cell state transitions using gene regulatory network models

Area of Science:

• Genomics
• Systems Biology
• Bioinformatics

Background:

• Cellular states are defined by gene expression patterns, which change during development and disease.
• Transitions between cellular states can be modeled by alterations in gene regulatory networks.

Purpose of the Study:

• To introduce MONSTER (MOdeling Network State Transitions from Expression and Regulatory data), a novel regression-based method.
• To infer transcription factor drivers of cell state transitions at the gene regulatory network level.

Main Methods:

• MONSTER utilizes gene expression and regulatory data to model network state transitions.
• The method employs a regression-based approach to identify transcription factor drivers.

Main Results:

• MONSTER was applied to four chronic obstructive pulmonary disease (COPD) studies.
• The method identified transcription factors altering network structure during COPD progression.

Conclusions:

• MONSTER detects robust regulatory signals across studies and tissues for the same disease.
• These signals are not identifiable through conventional differential expression analysis.
• An R package for MONSTER is publicly available.