Biophysically Motivated Regulatory Network Inference: Progress and Prospects.

Area of Science:

• Computational biology
• Genomics
• Systems biology

Background:

• Genomic technologies and computational advancements are driving progress in network inference.
• Accurate large-scale models of cellular regulation are increasingly feasible.

Purpose of the Study:

• To outline computational strategies for accurate large-scale models of chromatin state and transcriptional regulation.
• To identify key research questions for advancing network inference.

Main Methods:

• Focuses on computational strategies and experimental designs.
• Highlights four critical research areas for network inference progress.

Main Results:

• Identifies four key research questions: network structure constraints (sparsity), informative priors and data integration, latent regulatory factor activity estimation, and new methods for modeling regulatory factor interactions.
• Suggests that progress in these areas, combined with genomic technologies, will yield dynamic genome-wide regulatory network models.

Conclusions:

• Advances in computational strategies and genomic technologies are crucial for developing accurate, large-scale models of cellular regulation.
• Future research should focus on the identified key questions to achieve biophysically motivated, dynamic genome-wide regulatory network models.