Advances in computational and experimental approaches for deciphering transcriptional regulatory networks

Abstract

AbstractUnderstanding the influence of cis‐regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell‐type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression‐based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR‐Cas9‐based screening, which have significantly contributed to understanding TF binding preferences and cis‐regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis‐regulatory logic is analyzed. These computational advances have far‐reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.