Animal transcription factors drive complex spatial and temporal patterns of gene expression during development by binding to a wide array of genomic regions. While the . in vivo DNA binding landscape and . in vitro DNA binding affinities of many such proteins have been characterized, our understanding of the forces that determine where, when, and the extent to which these transcription factors bind DNA in cells remains primitive.In this chapter, we describe computational thermodynamic models that predict the genome-wide DNA binding landscape of transcription factors . in vivo and evaluate the contribution of biophysical determinants, such as protein-protein interactions and chromatin accessibility, on DNA occupancy. We show that predictions based only on DNA sequence and . in vitro DNA affinity data achieve a mild correlation (. r=. 0.4) with experimental measurements of . in vivo DNA binding. However, by incorporating direct measurements of DNA accessibility in chromatin, it is possible to obtain much higher accuracy (. r=. 0.6-0.9) for various transcription factors across known target genes. Thus, a combination of experimental DNA accessibility data and computational modeling of transcription factor DNA binding may be sufficient to predict the binding landscape of any animal transcription factor with reasonable accuracy.