Table 1 Overview of commonly used techniques for gene regulatory network mapping and their advantages and limitations

Computational inference (reverse engineering)

Infers putative regulatory relationships from gene expression data

Fast; cheap

The interactions predicted could be indirect (they do not have to reflect physical interactions); regulators that themselves do not change in expression will be missed; detection limits of mRNA measurements will affect GRN predictions (regulators or genes expressed at low levels will be missed)

Chromatin immunoprecipitation (ChIP)

Experimentally identifies physical interactions between TFs and DNA; TF-centered (protein-to-DNA)

In vivo; can detect TF dimers and complexes

Condition-dependent interactions can be missed; needs high-quality, highly specific ChIP-grade antibodies; when a universal tag is used for immunoprecipitation, TF is usually overexpressed; peak calling required

Yeast one-hybrid (Y1H)

Experimentally identifies physical interactions between DNA and TFs; gene-centered (DNA-to-protein)

Heterologous; condition- and context-independent

TFs that require post-translational modifications before binding DNA will be missed; not yet suitable for heterodimers