How are gene regulatory networks inferred from expression data and what are the challenges?

Gene regulatory network (GRN) inference reconstructs transcription factor-target relationships from expression data. Methods: 1) Correlation-based - WGCNA identifies co-expression modules; limited to undirected associations. 2) Information theory - mutual information (ARACNe, CLR) captures non-linear relationships; data processing inequality reduces indirect edges. 3) Regression-based - GENIE3 uses random forests to predict each gene from all TFs; TIGRESS uses stability selection. 4) Bayesian networks - model causal relationships but computationally expensive. 5) Perturbation data - knockouts/knockdowns provide causal information. Challenges: distinguishing direct from indirect interactions; causality vs correlation; combinatorial regulation; context-specificity; validation is expensive. Best practices: integrate multiple inference methods (wisdom of crowds); incorporate ChIP-Seq, ATAC-Seq, and motif data; validate key predictions experimentally.