Computational approaches to understand transcription regulation in development

Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genomewide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.

Automated summary

Gene regulatory networks (GRNs) are useful abstractions for understanding transcriptional dynamics in developmental systems. Computational prediction of GRNs has been applied to gene expression measurements, but these inferred networks are inaccurate and based on correlative interactions. This review highlights three approaches that significantly impact GRN inference: multi-omics, single cell sequencing, and neural networks. These developments have the potential to improve the quality of inferred GRNs.