Inference of enhancer-specific transcription factor interactions from gene expression data using a biophysical model

Transcription factors (TFs) play a central role in gene expression and regulation. In recent years, numerous experimental techniques have generated large-scale datasets, alongside computational methods aimed at inferring the role of TF–TF interactions in gene regulation. However, these approaches typically yield global interaction patterns across datasets, which may not accurately reflect local regulatory interactions at specific enhancers. Here, we model transcription using an Ising-type biophysical framework and introduce approximations based on its mean-field representation to infer TF–TF interactions at the level of individual enhancers from expression data, such as STARR-seq or fluorescent protein measurements. We validate our approach using simulated data and evaluate the effect of the strengths of TF–TF and TF–DNA interactions on inference accuracy. We then apply the model to experimental fluorescence data of gap genes for the eve stripe-2 (eve2) enhancer in the fruit fly embryo. The model successfully infers the established roles of the gap genes and predicts the possibility of cooperative and antagonistic interactions among them, which can be experimentally investigated.